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9,890 | https://devpost.com/software/land-mark | Starting screen
Search for a city
Add a city
City details
City details
Add a picture and a comment
Add as many cities as you want!
Inspiration
Currently we're all stuck inside waiting for this quarantine to be over. Everyone is bored and wants to get out of their house. I believe this is a good time to look back on all the exciting moments in our lives to forget about the fact that we can't leave the house. So my app idea was to allow people to create their own map that displays all the cities they've visited in their life. Every city in the world has something special about it and everyone has a memory of all the cities they've traveled to. Creating a collection of visited cities would be a really nice way for someone to keep track of where they've been and attach a memory to it. Users can continuously add cities to their list and create a customization screen, where they can add the date they visited, a picture, and comments. The end result is a world map with a bunch of markers everywhere. Everyone's map will be different and heavily personalized. Users will come back to the app whenever they want to re-visit a memory.
What it does
Users are initially greeted with a world map, where they can easily move around and zoom in on. If they tap the "plus" button in the top right, they are able to search for a city. Once they type in a city, the map zooms in on that city and they are given the option to add it to their list. If they say yes, they are brought to a new screen for their specific city. Here they can view the exact location, add the date they visited it, add a picture, and add comments. This screen is meant for the user to customize to their liking, as it gets saved and they can always come back to it and view their memory.
The user can add as many cities as they want to their list. The app supports every single city in the world and displays all of the added cities as markers on the map. Once they populate their map, they can come back to it any time and keep adding more. They can also view it on any apple device as it backs up to iCloud.
How I built it
I built the app entirely using Xcode/Swift. The UI is built with UIKit, and the MapKit framework is used to display the map. To get the city data, I parsed a large JSON file from the internet. I also saved all user data to iCloud so that nothing gets deleted when they close the app or use a different Apple Device.
Challenges I ran into
The biggest challenge I ran into was saving all the user data so that it wouldn't get deleted when they close the app. I needed to save all the cities the user adds to their list, and then all the data associated with them. The user is able to edit this data whenever they want so that makes it even more difficult to save data efficiently. I ended up having to refactor a lot of code to get it working but in the end it was worth it. Not only does the data save to the user's device, it also saves to their iCloud account so that they can easily view their map on another apple device.
I also had problems with the search functionality. Apple's built in search returns anything it can find, from countries, to pizza shops. I only wanted it to return cities so I needed to come up with a good solution. I decided to find a city dataset and then only allow the user to search for items within that dataset. I did it in such a way that the search query still goes through Apple's API and then goes through the dataset filter I made. This meant that people could have typos in their query and it would still return the correct result. I was very satisfied with this result.
Accomplishments that I'm proud of
I am proud of the fact that the app works really well and has no bugs (that I know of). I got stuck many times trying to fix bugs or implement features and I'm glad I pushed through and managed to get everything working to my liking.
What I learned
I learned quite a bit while developing this app. I learned how to use the MapKit framework, how to parse JSON with Swift, how to save user data to iCloud, how to build a complex TableView, and how to add search functionality in apps. Overall it was an amazing experience and I feel like a much better iOS Developer now.
What's next for Land Mark
A feature that I wanted to add but didn't have time for, would be sharing capabilities. I want users to be able to share their maps full of their experiences/memories with their friends/family so that they can view it on their device. This would be a great addition to the app and would definitely be a challenge to implement. I would also like to add an On-boarding screen to inform the user what the app is all about and how to use it. I will be submitting this to the App Store very soon.
Built With
json
mapkit
swift
Try it out
github.com | Land Mark | An app for people to track all the cities they've visited and re-visit those memories any time | ['Arjun Dureja'] | [] | ['json', 'mapkit', 'swift'] | 28 |
9,890 | https://devpost.com/software/supply-blockchain | Inspiration
Thousands of people die every year because of essential medical supply shortages and fraud.
The COVID-19 crisis has put unprecedented strain on the global supply chain of every product, but most acutely on critical medical supplies. With more than 3 million cases of COVID-19 worldwide, hospitals around the world are having difficulty getting safe and quality supplies.
Frontline healthcare workers are running out of essential resources, with no medicine to help. This leads to desperate decisions and irrevocable consequences. There have been widespread reports of fraudulent production and fraudulent claims of PPE and other critical supplies across the supply chain. Trust in the global medical supply chain is broken, putting millions of people's lives at risk.
The current situation is a much broader story about weak supply risk management and the lack of governance and integration, and the inability to manage the complexity of the problem. The current supply chain system is long, complicated & inflexible. It is heavily controlled by third-party distributors, resulting in a lack of communications, trust, and transparency between supply and demand.
According to the WHO, it is estimated that up to $200 billion worth of counterfeit pharmaceutical products are sold globally every year; according to the United Nations Office on Drugs and Crime (UNODC), corruption and fraud may amount to 10 – 25% of procurement budgets. In real life, this means vital services are never delivered, much-needed equipment never reaches hospitals, critical infrastructure is never built and people’s lives and well-being are at needlessly risk.
What it does
From point-to-point integration to ecosystem-level:
1 Decentralizing the Ledger for Transparency & Trust:
A transparent marketplace for hospitals to source and track life-saving medical supplies safely.
The real-time peer-to-peer communication provides a more flexible system connecting the supply & demand directly.
Tracking provenance, traceability, and historical procurement for products as they move downstream to the medical facilities with blockchain finality.
Digitize and secure travel document workflows on the blockchain with Smart Contract, where the terms are payable upon receipt, proof of delivery from a logistics carrier, will immediately trigger automatic digital invoicing and payments through the banking system.
2 From cloud-based management systems to smart sensors, the real-time supply chain provides a constant stream of real-time data to increase system efficiency.
It enables greater visibility, agility, and in-the-moment decision-making via AI, machine learning applications, and data analysis. The inventory system is drive-by hospital usage data to determinate inventory count, if the product is running too low, you can have auto repurchase on, if overstocked, the supplies are available for repackaging and distribution to nearby medical facilities.
How I built it
React & Baseline with zero-knowledge proof
Business Model
SaaS Model
Authentication and quality is offered to all users
Add-on services such as Smart Contract with IoT integration, automation, Analysis report, payment solution
Accomplishments that I'm proud of
We already have two venders that are willing to use our platform.
What I learned
According to the WHO, it is estimated that up to $200 billion worth of counterfeit pharmaceutical products are sold globally every year and 50% of these drugs are purchased online.
According to OECD estimates, up to US$2 trillion of procurement costs could be lost to corruption.
What's next for GMedChain
Within the health care field specifically, the goal of supply-chain management is to guarantee the availability of the products needed to treat patients.
The ability to track and manage resources at the ecosystem level can provide greater accuracy and better forecasts on medications and medical supplies, reducing waste from expired and damaged goods, preventing stock-outs, supply-side shortages, delivery time variability, supply-chain disruptions due to natural disasters, political unrest, and many other causes. Its Traceability enhances product safety & inventory allocation
.
Blockchain is an enabling technology, which is most effective when coupled with other next-generation technologies such as the IoT, robotic cognitive automation, or smart devices. The whole ecosystem can be useful in understanding which products led to successful patient outcomes, projecting future programmatic and budgetary needs, protecting against corruption, and ultimately allowing for an increase in the number of patients served.
It is especially needed in developing countries where no efficient supply chain system is in place and the combination of infrastructure issues could create supply chain dysfunction.
Built With
ai
blockchain
data
iot
machine-learning
Try it out
www.gmedchain.com | GMedChain | Healthcare supply chain solutions integrating AI & data, smart-logistics, and blockchain technology to trade and manage life-saving medical supplies safely. | ['Miriam Dong', 'Nicolas Six', 'Alejandro Ballesta', 'Yan He', 'Husein Attarwala, CPHIMS, PMP', 'Mona Hamdy'] | [] | ['ai', 'blockchain', 'data', 'iot', 'machine-learning'] | 29 |
9,890 | https://devpost.com/software/lookout-p8nsv3 | Landing Page
Register - 1
Register - 2
Login
Progress Calendar
Questions - 1
Questions - 2
Lifestyle Quality Index(LQI)
Overall LQI
Profile Page
Contact Us
Inspiration
This idea was conceived when in our very own city, we were able to see a drastic disparity between the different areas that we lived in, while some areas excelled in some parameters, they lacked in others, and vice versa. We realized that the government doesn't have access to proper data from the grassroots level, which may lead to overspending in certain sectors and certain areas, causing a disparity between the quality of services and essentials provided in two areas which are part of the same city.
We decided to build an application with the purpose of helping the government identify the quality of essentials and services being received in certain areas, and redirect the assignment and allocation of their resources to lessen the disparity and this is how, LookOut was born!
What we do
LookOut crowdsources data from the users about their residential area. Users rate their area based on 4 parameters daily, to form a Lifestyle Quality Index for their respective area. This raw data is then provided to the government, to learn more about the conditions of the different areas, and to reallocate resources accordingly, improving equitable distribution of services and essentials. The data collected is regularly updated for a time period of 14 days, to ensure a more accurate representation of the current scenario and to avoid redundancy. Users can also track their progress in the progress calendar, which also reminds them of their quarantine period.
How we built it
We initially used Figma to design the user interface.Then we started working on the backend, which we implemented through Node.js, we also made a controller in PHP that communicates with the backend and handles the job of requesting data from the model and displaying it in the browser. We integrated a location API, that would detect the pin codes, and sort the data accordingly, the data is stored in Mongo DB Atlas.
Challenges
Due to us having separate preferences about the Model and Controller of the MVC architecture, one implementation challenge was to make Node+MongoDB talk to PHP. We solved the problem by making an API using Express and then making PHP send requests to that API to retrieve data. Learning to send requests from PHP turned out to not be as simple as we initially assumed.
Accomplishments
Our application won the 2nd runners up in The Global Hack(Governance Track), and was in the top 300 in Hack The Crisis India. We have made significant progress on the project within just a few days. The idea was refined and revised multiple times, the idea itself is quite unique, and we haven’t been able to find an equivalent alternative.
What's Next
We are further looking to collaborate with state governments to run a pilot program by deploying the applications in their respective cities. After the pilot program, we’d try to scale vertically by collaborating with other state governments in India. Once we attain enough sustainability and cover a significant number of states and are able to perform to the set expectations, we’d look to onboard international embassies which face a similar disconnect between the public and the government and cater to their specific geographical needs through multiple localised features.
Built With
adobe-illustrator
express.js
heroku
html/css
mongodb
node.js
photoshop
php
react
Try it out
github.com | LookOut | The purpose is to improve the connect between the public and the government, hence ensuring proper governance being followed in a fast track manner during emergency situations. | ['Aryan Mediratta', 'Anshul Mahajan', 'surya bansal', 'aadit saluja'] | [] | ['adobe-illustrator', 'express.js', 'heroku', 'html/css', 'mongodb', 'node.js', 'photoshop', 'php', 'react'] | 30 |
9,890 | https://devpost.com/software/iot-sensors-to-detect-infection | Logo
Vitalcatch concept
High level Flowchart
Communication diagram
Vitalcatch PCB
App mock up
Web Interface
Inspiration
Currently a lot of people who show symptoms of corona, who are not tested, rather asked to go home, self-isolate and rest, only visit hospitals once their conditions get worse. By which, it means emergency rooms. This has led to wild mortality rates especially at nursing homes or at home deaths. What if we could create a system that detects and notifies such people, who feel they have symptoms but are not tested based on known biomarkers.
What it does
Biomakers such as temperature, heart rate, blood oxygenation are easy to monitor and good markers of COVID infection. The idea is to use sensors such as PPG and temperature sensors to detect anomalies in physiological markers communicated directly to an app through IoT/Cloud based systems. Based on regular measures, the system can notify your primary GP and on whose recommendation further testings can be taken.
How I built it
The system is composed of a hardware module, a mobile/web application front end and a cloud analytics backend. The hardware module, of the size of an euro coin, records temperature, heart rate, respiratory rate and blood oxygenation in real-time and at a regular basis. Physiological data are transmited through IoT technology to a Smartphone app, which automatically communicates it to the cloud server. Subsequently, an analysis based on machine learning algorithms is performed through the Cloud app to detect alerting COVID infection. If a COVID infection is suspected, the smart app creates an alert and send a report to the GP.
Our system is user-friendly, totally open-source and can be produce at low cost.
Challenges I ran into
We started from scratch. We performed an extensive literature review.
We developed:
a PCB design
a first functional device prototype
a Cloud app
a mock up smart app
a business plan
Accomplishments that we are proud of
The idea came from a simple exchange of emails between two strangers looking for a team on Friday evening. After 48h, we are proud of presenting Vitalcatch, an innovative project based on the collaboration of a great multidisciplinary team. We succeeded to develop a first functional prototype able to record physiological data, a Cloud app able to collect and analyze data and a bussiness plan based on litterature review.
What's next for IoT sensors to detect infection
We still need to develop the machine learning analysis and validate our prototype.
Over the next three months, we plan to launch a pilot study involving 100 nursing homes across Europe on a quarterly subscription based model. In the meantime, we want to expand partnerships with research institutes and exposure tracking solutions such as Ohioh or Safepaths.
Contact us:
[email protected]
&
[email protected]
Thanks for your interest!
Built With
iot
java
machine-learning
software
stryke
visual-interface | Vitalcatch - IoT sensors to detect infection | IoT sensors such as PPG and temperature sensors to detect when the condition of those who are infected with corona gets worse based on ML | ['www.stryke.io', 'Edoardo Palmieri', 'Kelly Assouly', 'Xav H'] | [] | ['iot', 'java', 'machine-learning', 'software', 'stryke', 'visual-interface'] | 31 |
9,890 | https://devpost.com/software/rods-7h6vi9 | Inspiration
A large part of the economic and social growth in the last 20 years is due to social mobility under the form of schools, internships, jobs or simply vacations. The current pandemic will affect this mobility on the long term. Without it, we run the risk of returning to a reduced area of experiences, interactions, ideas and therefore a contraction of dreams, aspirations and implicitly of interior capacities. In order to maintain and develop the multicultural, transnational and global spirit we created ROds as an evolution support pillar.
What it does
This is why we are building an application through which pupils/students/graduates from a part of the world to interact actively with real experiences, people, schools, universities, centers, firms, programs, events and communities from other parts of the world…BUT in a reversed way.
If until now we explored the global resources by searching new experiences, interactions, information to extend our horizon and make it more dynamic and flexible, through the ROds application we help pupils and students travel through global resources starting from practical and real elements such as miniJobs, miniInternships, miniTasks.
In a first phase, we will target the most dynamic fields: technology, science, research, entrepreneurship and social media.
How I built it
Our activity has been based on this active transnational concept. We have put youths in contact with all the important places in the world. In the context of travel restrictions, we see an opportunity to take this idea further through online long term interactions as compared to short term international stages.
When building the project we based it on our past global experience, but also on the vast international network that we already have.
www.topminds.ro
,
www.caravanacunoasterii.ro
,
https://www.facebook.com/RO-ds-108924410501250/?modal=admin_todo_tour
Challenges I ran into
Our inspiration came not only from the COVID reality, but also from the need of pupils and students to maximize their experiences, resources, international network.
Accomplishments that I'm proud of
We have over 6500 pupils and students that we already connected to international resources and 300 who attended international stages at CERN, NASA, Silicon Valley, Los Angeles aeronautic area, Tokyo, Australia, Singapore.
What I learned
Through the ROds app the number of the beneficiaries can grow exponentially.
This will allow for a niche project to be taken in the classroom or anywhere they are when they need the information or the real experience.
If every educational institution would utilize this in the classroom
What's next for ROds
ROds is today only in concept stage and will be followed by implementation in the next 6-12 months.
Costs: Research and Customer development, Product definition (UX/UI design), Product development (IOS, Android, Backend, web Admin), Maintenance, Product Landing Page, Marketing. Fees: Fee for schools, universities, companies; Free for pupils, students
Built With
adobe
adobe-illustrator
analytucs
chatapp
e-market
fronted
layoutdesign
photoshop
uidesign
uxdesign
Try it out
xd.adobe.com | ROds | VisionLearning mobile application which connects pupils/students with information, opportunities and cultures from other parts of the world, in a reversed way | ['Dragos Bulbes', 'Diana Nitescu', 'Flavia Achim', 'Stefan Ivan', 'Georgiana Achivei', 'Rafael Tomososchi', 'Larisa Sârghie', 'Diana Nitescu'] | [] | ['adobe', 'adobe-illustrator', 'analytucs', 'chatapp', 'e-market', 'fronted', 'layoutdesign', 'photoshop', 'uidesign', 'uxdesign'] | 32 |
9,890 | https://devpost.com/software/aidistance | An image of our website- customizable definitions of "safe" for personal preferences!
Leaving the room!
Entering the room!
Visit the url pasted at the bottom of our project description for an explanation of our server-side code
Project Description
AIDistance is a website that allows users to see the amount of people in highly crowded areas. By analyzing security camera footage, AIDistance is able to get the precise number of people in a given location. All this information allows users to see when it is safe to visit certain locations, and allows people to naturally stagger their outings.
Misson
The 2020 Covid-19 global pandemic has revealed a lot about our safety measures as a nation. In these trying times, it is imperative that we uphold the guidelines set up by our health care professionals. However, life must go on, and to preserve the safety of our loved ones and those at risk, AI Distance proposes a solution. By monitoring active, public spaces, we ensure that no one place becomes too crowded to effectively maintain social distancing. Getting over this pandemic is a community effort, and we want to add a practical solution that can also be applied in different situations in the future.
Analyzing Security Camera Footage
AIDistance combines deep convolutional networks with a clean and efficient django UI. For the client side, designed for use on a Jetson Nano mini-computer with a Raspberry Pi-camera V2, we used the inception_v2_coco model from the tensorflow zoo library to detect humans in a real-time video feed from the Picam. Using novel algorithms applied on the model's output, we were able to extrapolate much more data than simply the basic bounding box provided by the model. Curiously, at close proximity, the model underwent performance issues, sometimes classifying a single human as two, with an arm or a leg being identified as a separate entity. We were able to efficiently solve this using several tools from NumPy to mathematically infer an erroneous overlap. We can also infer heading by keeping track of the historical movement of centerpoints of the various bounding boxes produced by the inception model. Along with various other tweaks, we were able to cut through a lot of the noise that we were receiving earlier in the project's history, such as random incorrect classifications. The data netted by rcnn.py is then fed straight to our server where it is handled and processed for users to easily access and manipulate.
The Website
The website for AI Distance is comprised of three main parts. The first part is the home page which details the goal and importance of the project, along with important information about the current world situation. The second part of the website is the Shops Nearby Page. This page allows users to see the population densities of nearby locations, and tells users if that location is safe to go to at this time. The page automatically gets data from security camera footage, or in this example, video taken from a Jetson Nano computer, and determines the amount of people in the store(This process is detailed in the first paragraph). It then displays this information in an easy to read, efficient way to the users. Finally, we also have the addLocation page which allows users to add their nearby shopping locations. For demonstration purposes, we only have video footage linked up to one location: Krogers. Obviosly, we hope to partner with businesses to include our software at their locations.
In addition, this website does have block-stack sign in. Users must sign in in order to have access to this information. However, on the public IP and for demonstration purposes, we disabled this functionality, so that everyone can see this information for the demo. Our video demo shows this sign in.
Video Explanations and Demos
https://www.youtube.com/watch?v=TkG2QDZwPHU
-Server side explanation
https://www.youtube.com/watch?v=00PF7QCuBIk
-Client side explanation
Built With
blockstack
css
firebase
html
javascript
numpy
python
scikit-learn
shell
tensorflow
Try it out
github.com | AIDistance | Project for DistanceHacks hackathon using deep convolutional networks and website integration to aid in social distancing. | ['Liam Pilarski', 'Mehul Ghosal', 'Aryan Dugar'] | ['3rd Place', 'MLH - Best use of Blockstack'] | ['blockstack', 'css', 'firebase', 'html', 'javascript', 'numpy', 'python', 'scikit-learn', 'shell', 'tensorflow'] | 33 |
9,890 | https://devpost.com/software/coronaconnect-iaxj32 | . | . | . | ['Irfan Nafi'] | [] | [] | 34 |
9,890 | https://devpost.com/software/ai-based-screening-of-covid-19-using-chest-ct-scans | Intermediate Layer's features mapping. (a) Layer 1 (b) Layer 4, (c) Layer 8, (d) Layer 14. Shows complexity of features extracted via CNN.
Image pre-processing visualization. Summary of the image pre-processing module. The final image is free of artifacts.
Architecture of the truncated VGG16 used. Truncation point was determined experimentally. Last block was fine tuned to improve performance/
Confusion Matrix of Various Classifiers Tried. Features extracted using Truncated VGG16 architecture. Tested on test set.
Overview of the Project
Motivation
The rising cases of COVID-19 and not enough testing equipment motivated us to find a scalable solution that was accurate and at the same time cost and time efficient.
Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyse the vast amount of chest CT Scan images, which can be critical for efficient and reliable COVID-19 screening. In this project, motivated by the fact that CT Scans are much more accurate for diagnosis compared to X-Rays , a transfer learning-based feature extraction model, which we call VGG Feature Classifier, is proposed to screen COVID-19 positive CT scans from other healthy and non-COVID diseases.
How did we get the Resources
To evaluate the model performance, we have used 58 chest CT Scan images of 50 patients confirmed with COVID-19 and 66 chest CT Scan images of non-COVID patients. The images were collected and compiled from medRxiv and bioRxiv posted between Jan 19th and Mar 25th. To train our model we used 232 COVID-19 images and 260 non-COVID images, collected from same source as the evaluation data. We also included CT Scan images of Pneumonia and Tuberculosis patients in our non-COVID training set to improve the precision of the model.
What we have done
Our experimentation shows that our model can detect COVID-19 samples with an accuracy of 0.944. Our model has a precision of 0.968 and a F1 score of 0.946 when evaluated on the 124 test images. Thus, offering a promise of our proposed model for reliable COVID-19 screening of chest CT Scan images.
Project Structure
Summary of Model
In this work, considering the fact that CT Scan imaging systems are more accurate and precise than X Ray systems, Deep learning model is built to screen COVID-19 using CT Scans. A transfer-learning based model is proposed, which we call Truncated VGG feature based classifier. For training, other than healthy CT scan samples we have also collected Pneumonia and Tuberculosis patients’ CT Scans to increase the model’s selectivity/recall.
Performance Summary
The proposed model is validated to classify COVID-19 positive CT Scans from Pneumonia, Tuberculosis and healthy CT Scans. Using the limited number of COVID- 19 positive CT Scans, we have achieved a very high precision and recall. On the whole, in this paper, we demonstrate that the Truncated VGG features based classification model outperforms the state-of-the-art results for COVID-19 positive cases.
The project provides an Ideal solution for COVID-screening at large scales with a testing time of less than 0.5 seconds and an accuracy of over 95%.
How we built it
Inspired from the fact that COVID-19 shows patches of ground glass opacity (GGO) and consolidation
in CT Scans, to detect COVID-19 cases, a multi-resolution analysis of the CT Scan images is
deemed useful. The required trait is possessed by the VGG16 module. Additionally, considering the fact that the number of data samples of COVID-19 positive CT Scans is very scarce at present, a modified version of the VGG model is proposed, which we call Truncated VGG.
Pre-Processing -
FIgure - 1 : Summary of the preprocessing module
Since our Data is composed of images from varied sources, it is very sporadic. If trained directly on these images, the features extracted will not be homogeneous across data causing the model to have a low accuracy. To tackle this, a strong image pre-processing module has been designed.This module ensures our model can generalize well to this data.
We first have an adaptive region of interest extractor which crops, rotates, zooms, centers and straightens the image. Many images in the data-set have labelling and markings around the corners which could affect the performance of the model. To overcome this and to get a more accurate ROI selection, elliptical masks are applied to the CT Scan images. ROI selector removes all the non lung features from the images.
CT scans have artefacts like beam hardening, noise, scatter, etc. These artefacts if not handled, reduce the accuracy of the model. To overcome this, an artefact removal module is applied which uses filtering and morphological transformations to remove such artefacts. For the purpose of filtering, a bilateral filter, with a kernel of (5 x 5), is used because of its edge preserving property. Morphological transformations namely erosion and closing are applied to reduce background holes and intensify the productive features in the image.
Feature Extraction
Features are extracted from images using a transfer learning based truncated VGG Model. This section describes the implementation of the truncated VGG alongwith the reason for a truncated architecture.
Reasons for Choosing VGG Model -
VGG, ResNet 50 and AlexNet are the most popular CNN architectures for medical image classification. The three CNN archtectures are compared in Table-1 for our dataset. For Comparing the models, a Softmax layer is appended to the architectures. As can be seen from the data, VGG outperforms ResNet and AlexNet on the dataset.
Table-1 Comparing Various Architecture Accuracies
Architecture
Accuracy
ResNet50
79.3%
Alex Net
74.6%
VGG16
85.5%
The combined output of the VGG module provides rich feature maps of varying perspectives. The small-size convolution filters allows VGG to have a large number of weight layers leading to an increased performance. Such a property of the VGG module explains its unique performance in medical imaging, and in our case, on the COVID-19 CT Scans.
Truncated Architecture -
Figure-4 : Truncated VGG Architecture Summary.
The VGG model was designed for imagenet database which has over 14 million images. Owing to the large size of the imagenet database, the VGG16 architecture is built to extract complex features with high dimensionality from images. Since our COVID-19 dataset is much smaller with only 492 images, a high complexity of feature set will cause the model to overfit the data. To prevent this, A truncated VGG16 architecture is proposed which contols the complexity of the features. The first three convolution blocks of the VGG16 architecture have been used for our truncated architecture and the output has been flattened. The reduced complexity of the architecture allows our model to generalize well to the small dataset.
The truncation layer was determined by evaluating performance on the test set with different points of truncation. Table-2 gives a summary of the results of this analysis. Figure-2 depicts the architecture of the truncated CNN.
Table-2 Comparing Truncating Points
Truncation Point
Accuracy
Layer-10
81.8%
Layer-14
92.2%
Untruncated
86.5%
Transfer Learning
Training a Neural Network from scratch requires huge amounts of data. Since the COVID-19 dataset available is significantly smaller, we rely on transfer learning to extract accurate and concise feature set from our training data. With transfer learning a solid machine learning model can be built with comparatively little training data because the model is already pre-trained. For our purpose we use a pre-trained model i.e. VGG16 with imagenet weights.
Since the Image net set is non-overlapping to the problem, the last 4 layers i.e. the third convolution block are fine tuned on the training data. This ensures the representation extracted from the data is relevant to the classification. Through transfer learning, an accurate set of reduced feature representation of our data has been extracted.The extracted features displayed as a color map are represented in Figure-3.
Figure-3 : Feature map of various intermediate layers.
Summary of Feature Extraction
A Truncated VGG16 architecture is proposed for extracting features. The architecture is based on a represeantation learning model using imagenet weights. The last block of the truncated architecture is fine tuned with diffrential learning rates for more accurate extraction. The model thus gives a reduced representation of raw data with accurate features to be used for the classification.
Dimensionality Reduction
The feature extractor module, reduces the dimension of the data to 25,000 features per image for an image size of 112 x 112 pixels. However, with only 492 training examples, the model will still overfit the features. To prevent this feature selection and dimensionality reduction of data is applied.
PCA, Autoencoder and KBest have been used to select about 300 features, and then compared their accuracies after classification with an SVC. While using PCA 95% variance was retained which yeilded 358 features. The Autoencoder and Kbest were also configured to select 358 features. The results of the analysis is tabulated in Table-3. For our data, PCA gives the highest accuracy. One of the keys behind the performance of PCA is that in addition to the low-dimensional sample representation, it provides a synchronized low-dimensional representation of the variables. This provides a way to visually find variables that are characteristic of a group of samples.
Table-3 : Comparision of Feature Selector Accuracies
Technique
Accuracy
PCA
94.3%
KMeans
84.1%
Auto-Encoder
86.4%
Classification
The extracted features from the training set are used to train the classification module to screen COVID-19 CT Scans. For classification a different model that trains on the features selected by PCA is used. In machine learning no one algorithm is suitable for all problems. Hence, for achieving the highest performance, 6 different classification model were evaluated, using test set features. Based on these results the best suited classifier for our problem is chosen. What follows are the brief details of our implementation of various classification models followed by a summary of results.
Figure-4 : Confusion Matrices of the Various models Proposed.
Deep CNN
Since, VGG is itself a CNN architecture, for our Deep CNN model, a fully connected layer of size 1024 is added to the truncated VGG architecture followed by a softmax layer for classification. This gives us the most direct classification model where the feature extraction and classification are in the same CNN architecture. The deep CNN utilizes the fine tuned weights and uses it to directly predict the output. The performance of Deep CNN model is summarised in Table-4. The cocnfusion matrix is shown in figure-4.
SVM
For classification using SVM, radial basis function kernel (RBF) was used because of the high dimensionality of the data. The hyper-parameters C and gamma were experimentally determined. The summary of SVM evaluation for different values of C is given in Table-4. The confusion matrix for C = 2.5 is given in figure-3.
Bagging Ensemble
For classification using Bagging SVM, the data set was randomly divided into10 parts and trained the individual classifiers independently. They are aggregated to make a joint decision by the deterministic averaging process. The same SVC model was used as the base estimator. The confusion matrix for Bagging SVM is given in figure-4.
Extreme Learning Machine (ELM)
For classification using ELM, various activation functions like gaussian, multiquadric and polyharmonic RBFs were implemeted. The number of hidden layers in the model are 1000 with best-suited gamma(Width multiplier for RBF distance).The confusion matrix for ELM is given in figure-4.
Online Sequential Extreme Learning Machine (OS-ELM)
For classification using OS-ELM,
SLFN
was implemented with sigmoid activation function with 2500 hidden layers. The dataset was divided into chunks, and the model was initialized and sequentially trained. The confusion matrix for OS-ELM is given in figure-4.
Experimentation
Data Augmentation
The size of the available COVID-19 Chest CT Scan Dataset is quite small. If the model is trained on such a small number of COVID-19 positive cases, it would have a very low recall. To overcome this, Data AUgmentation has been applied. We augment each training image by random affine transformation, random crop, flip and random changes in hue, brightness and saturation of the image. The random affine transformation consists of translation and rotation (with degrees of 5, 15, 25).
Adversarial Defense
Deep learning models are easily fooled with noise perturbations. Such perturbations might also happen in the real world. To defend our model against such noise attacks, a defense module has been designed.
Three image denoisers have been applied namely total variation, guassian filter and wavelet denoising. The prediction of all 4 images is passed to an ensemble which finally classifies the image. On evaluation on the test set after adding random noise, the model gives an accuracy of 82.34%.
Data-Set
The COVID-19 data is very scarce and difficult to obtain. Despite this we collected 290 CT Scans of people tested positive for COVID-19. The dataset was collected from pre-prints uploaded on medRxiv and Biomed from 25th January 2020 to 30th March 2020. The Summary of Data set is given below.
COVID-19 Positive Scans - 290
COVID-19 Negative Scans - 326
We divided the set for training, validation and testing in a 7:1:2 ratio.
We had 431 train images, 123 test images and 62 validation images.
Results
For evaluation, the dataset is randomly split in a 7:1:2 ratio as training, validation and testing sets respectively. The overview of the split is shown above. The final performance is the average of 5 random set divisions.
Our proposed model's evaluation is summarized in Table-6. Our model has a very high precision of 96.82% and an F1 score of 94.45%. Our model outperforms any other model on COVID-19 screening using CT Scan Images in terms of accuracy and F1 score.
As a model signed for COVID-19 screening, the proposed method aims to reduce the false negative rate as much as
possible, since false positive cases can potentially be identified in the subsequent tests, but false negative cases will not have that chance. Our proposed model has a false negative rate of 6.58%, which is significantly lower than other COVID-19 CT Scan screening models.
Table-4 Summary of Results -
Classifier
Accuracy
SVM
94.35%
DeepCNN
92.65%
Bagging Classifier
95.38%
ELM
91.93%
OSELM
93.54%
Deployment
We have also made made a GUI for our model. The GUI is basic at the moment where the user uploads the image of a Chest CT Scan and the website uses our model to predict if the case is of COVID-19 or not.
We are currently trying to publicly deploy the website and add featurres like severity score and progress tracker.
We are also working on building an app for the model.
The basic version of the model is deployed at :
COVID-19 CT Scan Screener
Accomplishments that we're proud of
We built a model that outperforms any other model for CT Scan based COVID-19 Screening. This was a huge accomplishment for us. We have also launched a website for users to upload images and test CT Scans.
We are really happy and proud of our work but will be more proud when it is actually deployed in hospitals and helps fighting the pandemic.
We also built the model in a time-span of 1 week which was really difficult given the scarcity of data and complexity of the problem.
What we learned
We learnt many new techniques of Image classification and Image processing.
Overall this was a great learning opportunity for us. We look forward to implementing this solution on a scale that helps people fight the COVID-19 outbreak.
How will this help in the fight against COVID-19
As the cases of COVID-19 keep increasing globally, the pressure on the healthcare system keeps mounting. Studies have shown that people are unable to find trained professionals to check their reports. This is a flaw in screening and will lead to further spread.
If our solution gets deployed, it will help in screening patients at an accuracy that is comparable to a trained doctor in 1/1000th of the time taken by an actual professional. Bulk testing through our model will lead to better screening and isolation of patients.
A doctor on average needs 15-25 minutes to screen a CT Scan from print. This is just the doctor's time, the overall screening process takes hours. With our solution, we could screen a patient within seconds and also generate a report of how severe the infection is. Not only that the model has a feature to flag low confidence instances for a doctor to look over.
This can be really crucial especially in areas where the doctors are heavily outnumbered and trained professional can't cater to everyone's needs.
Ultimately this will help flatten the curve.
We look forward to implement this model at a wider scale.
Video Demo Link :
Video Demo of Website
Built With
angular.js
css
flask
html
image-augmentation
javascript
joblib
keras
numpy
opencv
pickle
python
scipy
skimage
sklearn
tensorflow
vgg16
Try it out
github.com
covid19-ctscan.herokuapp.com | Transfer Learning based COVID-19 Screening of Chest CT Scans | Transfer Learning is one of the strongest feature extraction techniques for images. Combine it with an ensemble of classifiers and you have a complex classifier capable of screening even COVID cases ! | ['Mukul Singh', 'Shrey Bansal'] | [] | ['angular.js', 'css', 'flask', 'html', 'image-augmentation', 'javascript', 'joblib', 'keras', 'numpy', 'opencv', 'pickle', 'python', 'scipy', 'skimage', 'sklearn', 'tensorflow', 'vgg16'] | 35 |
9,890 | https://devpost.com/software/covi_fight | Inspiration
The virus has affected humanity in various ways, be it our economy, our freedom of movement, and the loss of loved ones. Then how do we live on, comfortably, and safely with this virus around?
Even after the lockdown is over, there is a massive possibility that traces of the virus will remain, and it can spread again.
We wanted to bring people back their mobility and keep them safe at the same time. We wanted people to know about their status while they leave their houses.
What it does
CoviFight alerts me about the risks of catching the virus if I have come in
contact with an infected person
within the past three weeks. It also informs the healthcare system accurately about the spread of infection.
CoviFight also generates a
map with hotspots for what places have virus traces
, so that people can prevent travelling at these places and authorities can sterilize or lockdown these places efficiently rather than having a complete lockdown of a country.
How we built it
We develop a three-tier app:
• A user's app
• A provider's app
• An official's portal.
While utilizing
Bluetooth and GPS
of your phone, CoviFight makes sure that the confidentiality of every individual is secured and can not be compromised. Data is encrypted using a secret key, and no one can view it without your permission. It only traces the past data of positively tested patients. This way, CoviFight also meets the
GDPR compliance.
By using
Geo-fencing and Machine Learning
, we predict your chances of catching the infection so that you can take preventive measures.
A provider's app for aggregation points like shops, restaurants, and public transport synchronizes with the nearby user app. This interface is the key to the detection of infection points, be it
a stationary workplace or a moving vehicle
. If McDonald's installed CoviFight and had an infected customer in the past 15 days, all the customers after the positive tested patient would get alerted, and hence the restaurant can be sterilized.
Only the medical system may update a person's status over the official's portal, and the
authenticity
of the app is maintained hence preventing
false positives
or self-reporting, which might lead to falsification of records.
So, with the help of our app, people can move around while being alerted about their status, stay away from the virus, and be free from the worry of their privacy maintenance at the same time.
Take a look at our demo by clicking
here
Challenges we ran into
To maintain the authenticity of the predictions and analysis, we were initially in a fix as to how to update a person's status as positive or negative. Then we decided to come up with a three-tier system, and we developed a Doc App or official's portal, which is only accessed by the medical system so that the authenticity is maintained.
No one else can manipulate the data.
Accomplishments that we're proud of
• We have made sure that the
privacy
of every individual is maintained and can not be compromised. The encryption algorithms meet the standards of the leading social networking apps existing in the market.
• CoviFight not only alerts people about their own risks but provides heatmaps of the traces of the virus too. CoviFight also shows what
specific restaurant or public transport( like a bus or a train) may be infected
precisely.
• We do not need to compare data between people, thus making
computation very cheap and exponentially faster and efficient.
Our Journey So Far
•
Winners( Runner up) in the #EUvsVirus, a Pan-European Hackathon
Organised by the European Innovation Council to counter COVID-19 pandemic with more than 9k participants and 2000 teams. We stood second in the Real time Communication and Prevention challenge.
•
Top 6 finalists of The Global Hack, by Garage48, April 2020
The Global Hack is a hackathon designed to share and rapidly develop ideas for urgently needed solutions in the face of the COVID-19 crisis, and to build resilience post-pandemic, with over 12k participants from 100+ countries. The team developed a mobile application solution for the containment and tracking of this virus. We were in the top 6 teams in the Crisis Response Track.
•
We were also in the Top 23 Student Innovators in COVID-19 SAMADHAN MHRD( Ministry of Human Resource Development, India) Mega Online Challenge
What we learned
It has been an enjoyable experience to work with people who have not even met each other before and still successfully develop this amazing app. We learned a lot through the hackathon, from interacting with the mentors and getting their guidance, to develop the app.
What's next for CoviFight
We plan
to get this deployed
at its earliest so that people may get their
safe mobility
back. We plan to deploy this on the Play Store and make a version for iOS as soon as we can.
We are also in touch with the Indian Government and we might be able to save lives in India also.
The necessities to continue the project:
• Approval from government authorities to implement and track data.
• Participation from Hospitals/government bodies to update the status of a patient so that system can generate realtime alerts and mark hotspots.
• Cloud resources to scale up the project. Currently limited by the free tier of cloud infrastructure.
The value of our solution after the crisis:
• This application can be used for any contagious disease management.
• It can be used in disaster management to understand the right victims and relief reaches all rightful beneficiaries( such as in the case of floods and storms).
• It can be used by Providers such as McDonald's and Public transport systems to implement targeted location-based marketing complying with data collection practices.
What we have done till now.
• Implemented masked identities for users to comply with GDPR and privacy requirements.
• Identification of hotspots in realtime based on the patient status update.
• Fixed bugs in the flow and to make it work E2E.
• Produced a product demo.
New Technology introduces
• Blockchain to manage user identification data(making it immutable), adding the security of ECC digital signature
• Moving from NoSQL mogoDB to a hybrid of BlockChain and GraphDB for better analysis and prediction while keeping the user's id secrete on the Graph
• Use of Kubernetes for creating multiple threads for gaining concurrency
•Adding caching mechanism to mobile devices to handle any kind of network failure.
Built With
firebase
google-directions
har
java
machine-learning
maps
python
Try it out
github.com
sidhantha.medium.com | CoviFight | A source - contact tracing app, CoviFight is a 3-tier solution that uses geo-fencing and machine learning to trace and track the COVID-19 spread | ['Shikhar Mathur', 'Anshuman Saboo', 'MANIT BASER', 'vishwas puri', 'Sidhantha Poddar', 'Yash Bhagat', 'Neil araujo', 'Navdeep Chawla'] | ['2nd Place Overall Winners', 'The Wolfram Award'] | ['firebase', 'google-directions', 'har', 'java', 'machine-learning', 'maps', 'python'] | 36 |
9,890 | https://devpost.com/software/icook | iCook
Inspiration
We have all been in that situation where we look at the food we have in our home and have no clue what meal we can make. So we came up with iCook, an app that help you solve this problem. During the Covid-19 pandemic, it is nessacery for us to self-isolate to flatten the curve. This mean essential trips to get grocery should also be minimized. As a result, many of us have had to get more creative with what we have at home.
What it does
iCook lets you add and remove from a persional ingredients list. A user can then use that ingredient list to look for recipes, or simply look for a recipe by name. Recipes can also be saved for easy access in the future.
How we built it
We use Figma to mock-up an app design, Dart and Flutter to implement the app, and Firebase to implement authentication.
Challenges we ran into
We both have little to no previous experience with Dart/Flutter and Firebase. We've also ran into a number of error at the beginning of the project.
Accomplishments that we're proud of
Successfully implement basic features of the app.
What we learned
How do use Firebase Authentication
How to use some widgets in Flutter such as BottomNavigationBar
What's next for iCook
Users can upload recipes
Categorize ingredients
Keep track of amount for each ingredient
Remove/change ingredients from list
Expiration date or when they bought it and the app will reminds user when their ingredients go back
Built With
dart
firebase
flutter
intellij-idea
kotlin
swift
visual-studio
Try it out
github.com | iCook | We have all been in that situation where we look at the food we have in our home and have no clue what meal we can make. So we came up with iCook, an app that help you solve this problem. | ['Trang Trần', 'Dayeong-git'] | [] | ['dart', 'firebase', 'flutter', 'intellij-idea', 'kotlin', 'swift', 'visual-studio'] | 37 |
9,890 | https://devpost.com/software/divoc-e0fywm | Flow chart depicting the working of the whole system.
Homepage of the application
Teacher Login
Student Login
Teacher Dashboard
Student Dashboard
Canvas as a blackboard
Asking question in middle of a lecture
Tab Change alert to gain students attention to the lecture
Inspiration
There is an old saying,
The Show Must Go On
, which kept me thinking and finding out a way to connect teachers and students virtually and allow teachers to take lectures from home and to develop a completely open source and free platform different from the other major paid platforms.
What it does
This website is completely an open source and free tool to use
This website whose link is provided below, allows a teacher to share his / her live screen and audio to all the students connected to meeting by the Meeting ID and Password shared by the teacher.
Also this website has a feature of Canvas, which can be used as a blackboard by the teachers.
Including that, this website also contains a doubtbox where students can type in their doubts or answer to teachers questions while the lecture is going on.
Again this website also has a feature of tab counting, in which, tab change count of every student is shown to the teacher. This will ensure that every student is paying attention to the lecture.
Also, teacher can ask questions in between the lecture, similar to how teacher asks questions in a classroom.
How I built it
1) The main component in building this is the open source tool called WebRTC i.e. Web Real Time Communication. This technology allows screen, webcam and audio sharing between browsers.
2) Secondly Vuetify a very new and modern framework was used for the front end design.
3) Last but not the least NodeJS was used at the backend to write the API's which connect and interact with the MongoDB database.
Challenges I ran into
The hardest part of building this website was to find a
open source
tool to achieve screen and audio sharing. This is because Covid crisis has affected most of the countries economy due to lockdown. Hence, it is of utmost important that schools and colleges do not need to pay for conducting lectures.
Accomplishments that I'm proud of
I am basically proud of developing the complete project from scratch and the thing that anyone who has the will to connect to students and teach them can use it freely.
What I learned
I learned a new technology called WebRTC which I believe that is going to help me more than I expect in future.
What's next for Divoc
Integrating an exam module and allowing teachers to take exams from home.
Built With
mongodb
node.js
vue
webrtc
Try it out
divoc.herokuapp.com | Divoc | DIVOC - An Antidote For - COVID | ['Sanket Kankarej'] | [] | ['mongodb', 'node.js', 'vue', 'webrtc'] | 38 |
9,890 | https://devpost.com/software/co-help-a-web-portal-for-covid19 | Introduction
During this pandemic, almost every country is in the lockdown phase and every person in that country is in their home so it is very difficult for them to get the daily essentials and other things.
Co-Help is a web portal where every essential service is offered so that social distancing can be maintained properly.
What it does
Co-Help
is a web portal with tons of features. It provides a platform for the common people to get their essentials, to know about
Corona Virus
, and stay aware. We have made this site so that the common people do no come out of their homes to get their things, and it will help in decreasing the spread of the Corona Virus. Here, we have also introduced the
hospital section
so that people can know about the containment zone near their residence and visit COVID Hospitals if they feel that they are having the symptoms.
Sections of Co-Help
Co-Help has several sections for every section of people. These are discussed below:
Hospital Section: 1. It shows the nearest COVID Hospital so that if people who are suffering from COVID19 can get isolated at COVID Hospitals. It uses the
Google Map's API
for showing the hospitals. Also, we have given the hospital contact numbers and common state government contact numbers for help.
2. It has a map, thanks to
Google Maps
, where it shows the
containment zones
in a particular city. It'll help and aware people to stay away of that zone which will reduce the spreading of the Corona Virus.
3. Corona Test Centers are also marked so that people can go there for the testing.
Daily Essential Section
for buying essential items and those will be supplied at the doorsteps by Govt authorities so that there will be no movement of public mass in the market.
All the products as been categorized so that there will be smooth delivery of products.
Information portal
for updates from WHO.
COVID Help section
for Donation fo fund and volunteering option.
In the donation section, people can donate money to the relief fund so that the Govt can use that money in providing food to the poor.
In Volunteering Section, jobless people can apply for jobs like sanitization work so that they can earn money by doing that work.
i-Education
for providing free education to the students. There are sections for class notes on various subjects, videos so that students can understand each concept properly and free online courses also so that students can learn during this quarantine.
Corona Go App
: This app is used to track the COVID19 stats and give realtime updates from the Ministry of Health & Family Welfare (India) and WHO's Twitter feed and their website. Also, it shows the stats of COVID19 of the World as well as of India.
Online Doctor
is a section where you need to upload your queries and doctors linked to it will call you so that they can know from what you are suffering from. This will help people in getting medical care directly from the home.
How we built it
HTML
CSS
Google Map's API
Java (for Android App)
Team name: PIYSocial
Members: Saswat Samal & Sanket Sanjeeb Pattnaik
Links
Website Link:
https://cohelp.netlify.app/
Github Link:
https://github.com/PIYSocial-India/Co-Help
CoronaGo Link:
https://bit.ly/piyappstore
Built With
css3
google-maps
html5
java
javascript
Try it out
github.com
cohelp.netlify.app | Co-Help | A Web Portal for COVID19 | A web portal to help the general public during this pandemic! | ['Saswat Samal', 'Sanket Sanjeeb Pattanaik'] | [] | ['css3', 'google-maps', 'html5', 'java', 'javascript'] | 39 |
9,890 | https://devpost.com/software/stacy-bot | Interface in FB messenger
This representation of NLP
Features which will be added more as time goes
PLEASE NOTE THIS IS A TEST BOT, AS PUBLISHING AND VALIDATION TAKES TIME, SO IF U WANT TO USE THIS THEN U NEED TO BE THE TESTER. BUT U CAN USE THE PHONE CALL FACILITY.
CALL AT: +1 463-221-4880
(This is a toll-free number based in US, if you are out of US then only minimal international charges will be applicable, I am from India and it takes 0.0065$/min)
If you want to use this app in your Facebook Messenger like shown in the video then please comment your Facebook ID in this project's comment section, I will add you as a tester to this app
IT IS JUST AN WORKING DEMONSTRATION OF MY IDEA TO TACKLE THE PROBLEM, IT CAN BE MADE AS PER THE DEMAND OF ANY ORGANISATION. AND THE BEST THING IT IS NOT A CONCEPTUAL IDEA IT IS TOTALLY A REALISTIC IDEA THAT CAN BE DEPLOYED AT ANY MOMENT ACCORDING TO THE DEMAND OF THE ORGANIZATION
Our Goal
General Perspective
Due to the situation of COVID-19 the work force of the world is decreasing(since everyone is maintaining self quarantine and social distancing ), which is creating a big havoc in the world, through this project of mine, I mainly target to tackle this problem and help the health organizations with a virtual workforce that runs 24*7 without any break, and handles all kind of mater, starting from guiding the people to fill up the forms to managing the data of the patients automatically and all-together.
Business Perspective(if required)
Bot service (it is not a company yet, I am just referring to the thing that we want to build or start this company, we are student developers right now) which adds a virtual work force to every client organisation to bloom in the market. In business perspective Our potential business targets are small business,NGO and health organisations and we help them to be free from human service cost and help them to grab more users by providing 24*7 interaction with there users, thus generating more revenue for them.
Inspiration
I really got inspired for making this advance A.I bot by seeing the current COVID-19 situations, because of these COVID-19 situations people are restricted from gathering hence work force and user interaction of various health organisation are diversely effected. Through this project I aimed to connect the health organizations with the patient anywhere in the world,using any platform(not limited by android, ios or Web). And also manage the data of the patients automatically thus reducing human effort and maintaining social distancing.
MADE THIS PROJECT TO BRING A CHANGE
.
How is our product different than others
1)
There are many types of A.I bots,where most of them are Decision tree based models that work with particular buttons only,our products will be totally based on NLP based models,which are more advanced and are in higher demands than others.
2)
Other service A.I bot service providers are confined to only 1 or 2 platforms, whereas we at the same time are providing advantage to the client to choose from a large scale of platforms like FB messenger, google assistant,slack,line,website bots and even in calls
3)
For the health organisations that are willing to buy our technology (We are also willing to donate this tech for free), from business perspective we will also be cheaper than our other competitors, when others are taking near about $3300/year for the service, we are doing it in $100-$1500 one-time fee range with more versatility.
It will totally be free for any user using it, no charges will be applicable for users
What it does
Our bot provides the power to every health organisation at such situations of COVID-19 by managing the screening,testing and quarantine data and also connecting the persons that are willing to do the test with the help of diversified digital platforms. In cases where internet is not working (where other bots won't function) still our bot works inside the phone number thus providing fruitful results in such situations.It basically covers all important aspects of an advanced A.I bot. It also connects the health organisations with volunteers that are willing to donate their time as helping hands in this hour of need.
How I built it
I built it using Google cloud A.I solutions, Google cloud Dialogflow framework(which includes automatic firebase integration) where I trained the bot with NLP with huge datasets from WHO and government and then integrated it with the Facebook messenger through Facebook Developer account. It is also supporting Phone call facility
Challenges I ran into
I had to go through many challenges, starting from being a solo developer, I really had to face a lot of problems as making such a complex app which all the advanced features as mentioned, all these things together cost me a lot of sleepless nights but i hope my hard-work pays off
Accomplishments that I'm proud of
I am really proud of the app that I made because it itself is a big milestone for a solo developer like me.
What I learned
I learned a lot of things through out this journey of developing this app, starting from advance use of Google cloud A.I solutions, Dialogflow and integrating it to Facebook messenger, making filters inside the chat-bot to enhance user experience etc.Connecting it with a phone number to receive phone calls etc.
What's next for Health Bot
If my work gets selected, then for sure I am going to work really hard to make Health Bot even bigger and to add more amazing functionalities to make my users happy.
Built With
dialogflow
facebook
google-cloud
javascript
json
Try it out
github.com | Advanced A.I Health Bot | An A.I bot with: Telephone calling,NLP,24*7 health coverage,total automatic data management,wipes rumors,Easy navigation,HD pictures,Customer service help etc | ['Udipta Koushik Das'] | ['Accessibility: Second Prize', 'Healthcare: Second Prize'] | ['dialogflow', 'facebook', 'google-cloud', 'javascript', 'json'] | 40 |
9,890 | https://devpost.com/software/team-discover-qg7kn3 | The project is the winner of the EUvsVirus Health & Life Domain!
The problem our project solves
There are thousands of (potentially) infected people being monitored in hospitals in non-intensive rooms. These are cases that are not severe enough to be in ICU care, but if their conditions worsens, they need to be relocated there. Nurses work around the clock to help and monitor them many times a day, but current practices have huge shortcomings.
There is a shortage of protective gear and they are highly overused, which puts nurses at high risk after having so many close physical contact with patients.
Just as with the equipment, there is also lack of human resource: nurses are critical to stay healthy so that staff numbers don't drop.
Monitoring the vital signs of a patient takes about 5 minutes for a nurse, without considering the changing of gear, which amounts to a small number of people being inspected under an hour.
The measured data rarely entered and stored online, which limits any further analysis to be made.
What we bring to the table
We give nurses superpowers, by doing a 100 check-ups in the time that it used to take 1. All while being far from the patient, staying out of risk.
Our solution enables a highly scalable patient monitoring system that minimizes physical contact between nurses and patients, which also leads to smaller shortage of protective gear. Instead of occasional visits, our device measures vital parameters real-time and uploads each patient’s data into a central server. With the help of our dashboard, doctors and nurses can oversee hundred times more patients, while our automatic alert functionalities make it possible to diagnose deteriorating cases instantly and to reach quicker reaction times.
In the span of 48 hours, we have created a fully-functional pair of 3D printed glasses, allowing patients to initiate frequent measuring of their vital signs, all by themselves. These include body temperature, oxygen saturation and respiratory rate, the key values nurses regularly check on coronavirus patients.
What we have done during the weekend
We have improved our 3D printed prototype, that we have created on another weekend. We had to re-assemble the sensors and performed benchmark tests to measure the accuracy of our sensors. We have consulted with multiple medical professionals on top of the ones we have already talked to earlier and were able to come up with better infrastructure for our solution. We also focused more this time on the supporting services such as the dashboard, which we have designed from scratch, along with our pitch video.
Our solution’s impact to the crisis
Our medical device enriched with our data analysis system is designed without the need for any specific infrastructural requirement, which allows universal usage in any country. Furthermore, hospitals, regions or even countries can collaborate and share their data to find global patterns, which opens doors for new innovations to fight the virus together. Our modular sensor design and 3D printed case allows fast mass-production and short implementation time. From the medical view, we are keeping the medical staff in a safe distance to protect them from highly infective patients. With our real-time, large-scale monitoring, nurses and doctors can filter out and deal with most pressing cases while our system keeps an eye on every other patient.
We have talked with over 15 professionals, including multiple doctors, nurses, investors and manufacturers, and they were eager to hear how fast we could get this to hospitals. After further recognition and an award from EIT Health, multiple doctors reached out to us, offering their expertise and support, which gave us another huge confidence boost in the project.
The necessities in order to continue the project
For us to scale up this project, we need partners that can help us in mass manufacturing, as we lack the experience in this area. For the manufacturing, we would need a large quantity of sensors ,injection-molding and assembling facilities. For fast delivery of the device, we also need the cooperation of hospitals, doctors and nurses to help us in testing. Their feedback is invaluable for the success and impact of our product.
The value of our solution after the crisis
Although the parameters measured by our medical device are the most informative values for COVID-19 infected people, body temperature, oxygen saturation and respiratory rate are key indicators for illnesses under normal circumstances as well. Therefore, our wearable makes everyday routine check-ups faster even in normal situations.
Another key change would be digitalization. Many hospitals still don’t have a centralized medical system and database, while our solution could start a new wave of data analysis and speed-up innovative activities in the health industry.
The available data and its analysis can also boost cross-European collaboration by sharing trends and new findings between countries, leading to more efficient and smarter future detection measures.
Team
We have multiple years of experience in hackathons and real life projects. Our team combines a multi-disciplinary knowledge of full-stack development, machine learning, design and business development. We are double-degree EIT Digital students at top universities, including KTH Royal Institute of Technology, Aalto University, Technical University of Eindhoven and Technical University of Berlin.
Márton Elődi - EIT Digital MSc Student in Human-Computer Interaction Design
- Several years of experience in software and product development
Kristóf Nagy - Electrical engineer
and professional motion graphics designer
Péter Lakatos - EIT Digital MSc Student in Data Science
- Experience in ML and business development
Miklós Knébel - EIT Digital MSc Student in Autonomous Systems
- Experience in robotics, deep learning and automation
Péter Dános - EIT Digital MSc Student in Visual Computing
- Expertise in 3D printing and design
Levente Mitnyik - EIT Digital MSc Student in Embedded Systems
- Vast knowledge of electrical engineering, micro-controllers and embedded systems.
Built With
3dprinting
arduino
autodesk-fusion-360
infrared
microphone
pulsoximeter
Try it out
github.com | Team Discover - EUvsVirus Health & Life Domain Winner | We give nurses SUPERPOWERS! | ['Kristóf Nagy', 'Péter Dános', 'Miklós Knébel', 'Peter Lakatos', 'Levente Mitnyik', 'Márton Elődi'] | ['Grand Winner (Health & Life Domain)', 'Challenge Winner'] | ['3dprinting', 'arduino', 'autodesk-fusion-360', 'infrared', 'microphone', 'pulsoximeter'] | 41 |
9,890 | https://devpost.com/software/corona-stats-website | Website interface
Inspiration
I wanted to build a simple website that can be accessed by people easily to get information about the Corona Virus pandemic.
What it does
It displays the statistics of corona Virus pandemic for the selected country and date.
How I built it
I built it using
vanilla javascript, leaflet.js
and the_ rapid api_.
Challenges I ran into
The major challenge I faced was to understand the concept of APIs and how to use it to access information in JSON format.
Accomplishments that I'm proud of
I learnt how to use APIs and some additional Javascript libraries. I also have successfully revised my front-end skills.
What I learned
i learnt how to create a website that displays some relevant information in visually beautiful ways and can be accessed by all.
What's next for Corona Stats website
Next, I aim to integrate the
IBM Watson chatbot
so that people can ask the bot about some relevant information about coronavirus to the chabot like symptoms, availability of medicines etc.
Built With
javascript
leaflet.js
rapidapi
Try it out
www.covidmap.tech | Corona Stats website | Its a website that gives information about the statistics of Corona virus pandemic according to specified country and chosen date. | ['Avhijit Nair'] | [] | ['javascript', 'leaflet.js', 'rapidapi'] | 42 |
9,890 | https://devpost.com/software/zilch | Common Products(ios)
Expiration information(ios)
Scan instructions(ios)
Expiration information(android)
Scanned product(android)
Inspiration
According to the National Resources Defense Council, nearly 40% of the food produced in America is wasted. One of the most efficient ways to reduce food wastage is by educating consumers about expiry dates. Studies show that nearly 80% of Americans misinterpret the expiration dates resulting in products being thrown out prematurely. Consumers might find it difficult to interpret the differences between sell- by, best before, and other dates resulting in food wastage. Additionally, companies may intentionally present these dates in a confusing manner to increase the sales of their products. For example, Mac and Cheese can be used up to 1 year even after the expiry date on the box! Through this app, we hope that consumers will become more aware of the true expiration of their products.
What it does
Using Zilch, the user has access to the true expiration date of any product. Zilch has 3 tabs, Scan, Home, and history. In the scan tab, the user can scan their product and get more information on the expiration date. In the home tab, the user can look through the most commonly used items and learn more about their expiration dates. In the history tab, the user can look at the items they have previously scanned and keep track of their expiration dates. In this tab, the user can also delete their history and undo any changes.
How I built it
The app is built with the Flutter SDK, which simplifies the process of deploying to ios and android devices. In fact, this app can be run on near 80% of all smartphones. The barcode lookup API was used find product information.
Challenges I ran into
Some of the challenged I encountered include the following: structuring the app ui, using flutter packages, managing scanner, and making ui elements reactive.
Accomplishments that I'm proud of
Building a reactive app that runs on both iPhones and Android phones.
What I learned
I gained a better understanding of creating flutter apps.
What's next for Zilch
Future plans include adding support for more barcode formats and storing user information in the cloud.d
Built With
barcode-lookup
barcode-scan
bottom-navy-bar
dart
flutter
Try it out
drive.google.com | Zilch | Zero waste | ['Ruthvik Ananthula'] | [] | ['barcode-lookup', 'barcode-scan', 'bottom-navy-bar', 'dart', 'flutter'] | 43 |
9,890 | https://devpost.com/software/maskeleton | maskeleton
maskeleton1
hand drawings
hand drawings2
new models2
new models
BUY ONCE USE LIFETIME Washable - Reusable
picture
Inspiration
Are surgical masks safe or big risk;
Billions of people use simple surgical masks to avoid the epidemic every day. Misuse of masks and in use difficulty can increase contagiousness of the coronaVirus.if there is a small gap between the mask and face. the masks are not necessarily protective for the wearer in terms of preventing inhalation of the residual droplets in the air, which enter from the sides unfiltered. touching our mask with contaminated Hands makes it risky.
In addition Some people prefer to use cloth masks because they think it is more elegant.
That is, if we can combine elegance and safety for masks, we will ensure that everyone will wear surgical masks and increase safety.
What it does
Firstly, you put the mask on the MASKELETON.
. then you can easily wear the MASKELETON on your face. it is now easy to fit and remove the mask .. the MASKELETON is Stylish and Fashionable. It allows children and young people to get used to using masks. elderly people can easily use it too.
The part between the nose and chin protects the mask from external impacts.
How I built it
we started with hand drawings. We used the Rhinoceros application.At the end of the day we made it 3d-printable .
Challenges I ran into
All team members live in different cities. In fact, it doesn't matter. Brainstorming is more difficult. it takes a long time to explain the requests to the designers( there are two designers in our team).
Accomplishments that I'm proud of
I am proud to finish the project in a very short time. I hope it will be useful for all people.
What I learned
Working for the health of all the people in the world was a great feeling. Only one of us can change the destny of World.
What's next for MASKELETON
our aim is to cooperate with big companies for mass production after prototype production and tests.
Built With
rhinoceros | MASKELETON | Easy-to-use tool for surgical mask.Masks fits very well by using MASKELETON. There will be no gap between face and mask. Visor prevent airborne aerosol ,so coronaVirus cant reach to mask directly. | ['Mehmet Akif Dalaslan', 'Evrim Koruyan', 'feyza dalaslan', 'Aleyna Duman'] | [] | ['rhinoceros'] | 44 |
9,890 | https://devpost.com/software/draw-it-d3u8x9 | Inspiration
I was inspired to create Draw it because of the difficulties most of my teachers had faced in teaching their students virtually. One of my classmates asked a question: "could you explain how to draw and label energy diagrams?" My teacher shared his screen and opened a virtual drawing pad. Then, my teacher proceeded to draw contorted lines using the trackpad on his computer. This process not only made it hard for my teacher to explain but made it difficult for students to understand the material and the challenging concepts. This inspired me to create Draw it.
What it does
Draw it is a tool incorporating computer vision that enabled teachers to draw on their screen by moving their writing implement (pen/pencil) in the air. This allows teachers to swiftly and effortlessly create diagrams or demonstrate challenging concepts to their students during the relatively short class time.
How I built it
I used python and the OpenCV computer vision library to build Draw it. After coding the simple interface, I used the OpenCV library to detect objects blue in color (for the project to work, the implement must be blue in color). Next, the program is able to capture and draw the motion of the blue-colored object.
Challenges I ran into
I struggled a lot with finding the best data structures to work with the OpenCV library. After a lot of trial and error, I decided to use a deque to best handle the date.
What's next for Draw it
I hope to enhance Draw its features by using a machine learning model in order to perform image recognition and detect writing implements such as pens and pencils. Furthermore, I hope to expand the codebase in order to provide more tools for teachers in order to provide an even better distance learning experience for their students during COVID-19!
Built With
opencv
python
webcam | Draw it | A distance learning tool for teachers to better teach their students during COVID-19 | ['Veer Gadodia'] | ['Honorable Mention', 'Best Community Building Hack'] | ['opencv', 'python', 'webcam'] | 45 |
9,890 | https://devpost.com/software/bidustry | Bidustry - Industrial B2B marketplace!
Bidustry is B2B marketplace for industrial sellers and buyers!
Producer goods, manfufacturer goods, capital goods or Lets say industrial goods!
Product prices are changeable for industrial goods because of logistic costs, manufacturing quantities and stock status.
So e-com is not a solution for those manufacturers and suppliers and Bidustry is not an e-com marketplace!
See how Bidustry works:
https://www.bidustry.com/en
Built With
blockchain
php
Try it out
www.bidustry.com | Bidustry | Industrial B2B marketplace! | ['Yiğit Can BANDIRAN', 'HAKAN TAŞLI'] | [] | ['blockchain', 'php'] | 46 |
9,890 | https://devpost.com/software/virtual-health-checkup-modelling-of-coronavirus-technoband | Technoband
Software Modelling of Future conditions of CoronaVirus
Inspiration
Daily surge in cases, health conditions of citizens pushed me to work hard
What it does
It predicts the curve of future conditions of any country w.r.t. data set available
How I built it
I built it through software, that have been mentioned.
Challenges I ran into
Lots of challenges, but overcomes and got the results as expected
Accomplishments that I'm proud of
That I did something, which satisfies and help at least one citizen, then the chain will follow up.
What I learned
I learned new softwares, skills
What's next for Virtual Health Checkup|Modelling of CoronaVirus|Technoband
If got success, wanna make it open source.
Built With
arduino
c++
embedded
matlab
python
webex | Virtual Health Checkup|Modelling of CoronaVirus|Technoband | Future prediction with Virtual checkup online and Smart electronic band | ['Shreyansh Pagaria', 'Maor Mashiaxch'] | [] | ['arduino', 'c++', 'embedded', 'matlab', 'python', 'webex'] | 47 |
9,890 | https://devpost.com/software/unmask-fqdblz | List of potential items to buy at the grocery store.
Tags at the top represent grocery stores near our location. Data filters and changes according to location. We also use COVID county data.
Social factor for masks is high, so the high price indicates that multiple of this item should not be bought.
Cart view of all products you want to buy, with impact and total costs.
TensorFlow Lite used to detect objects. Clicking on each object goes to an item breakdown page.
We created our application for iOS using Swift and XCode
Inspiration
Forests are torn down to make our paper. Producing a single battery demands nearly 9000 liters of water. Buying medical masks can leave doctors without proper protective equipment. As the world moves forward into the perils of COVID-19 pandemic and moreso, global warming, our individual spending decisions account for the health of our whole community and the globe. The supermarket is the battleground. This concept inspired our team to engineer an app that helps users avoid the social and environmental costs that hide behind every purchase and, in turn, protect the commonwealth of humanity.
What it does
We are
Commonwealth
. Commonwealth alerts the average consumer of the environmental and social footprint of their purchases to promote responsible eco-friendly spending
and
resist against the spread of COVID-19. Commonwealth employs years of real world data to analyze and accurately calculate the costs of a single purchase on your community and the Earth. Users can easily add items to their shopping cart through our Item Selection tab to be informed of their cost. If they're in a rush, they can pull out their phone and scan items in real time using our TensorFlow object detection software and the individual items are assigned a cost.
How we built it
We developed an iOS mobile application using Swift to create our logic and interfaces. We utilized GCP's ML Kit (Tensorflow Lite) to create the real-time scanning of items using the phone's camera. We also used the Google Map's API for location tracking.
While the final output is a simple price tag, Commonwealth is built on a wide variety of data. Here are some of the different measures and datasets that were used to create our unique scoring function.
WATER FOOTPRINT
While 75% of the world is made up of it, there is only so much of it that humans can use. Water is the key to producing a great deal of industrial products. The
Water Footprint Network
maintains a comprehensive database of how many liters of water it takes to produce just about any animal product, crop, and industrial item. This data is cognizant of the differences in production footprint in different parts of the globe. However, as we needed a single, more universal measurement, we used
Pandas
to aggregate this tabular data into a weighted global average (L) for every individual item. This final number of liters is added to the cost according the US government's utility rate for tap water.
DROUGHTS AND FOOD DESERTS
Not all cities are made equal. While some cities like Portland, OR and Atlanta, GA. are surrounded by fertile land for agriculture, desert cities like Phoenix and Las Vegas are not as lucky. As such, markets in these cities often get their fruits and other goods from distant agricultural area. The transportation of these goods from across the nation leaves a rough financial impact. We account for this by adding to the uCost based on the number of droughts that the users county experiences and other current data from the
United States Drought Monitor
. This data helps us ascertain whether the user is in a food desert or a more self-sufficient city.
MEDICAL DEMAND
Who really needs the rubber gloves? Medical professionals across America have come into trouble acquiring PPE (personal protective equipment) such as goggles, gowns, gloves, and —
most importantly
— face masks. One of the ways we can help our doctors and nurses is by not exhausting the supply of PPE. As such, all medical items come with an according social tax based on how much medical personnel need them.
PUBLIC DEMAND
While some items are still plentiful even during quarantine,
others
due to their perceived longevity in a crisis. As such, non-perishables like rice and pasta are some of the first to leave shelves in a crisis. This is problematic as these items are some of the limited options that SNAP EBT(food stamp) users can purchase, leading to great stress on
state funding
. As such, to alert users of the costs that buying these items in bulk has on the community, we add an additional social tax. We used the aforementioned Amazon data to determine demand for these items categorically (Health, Household, Electronics, etc) and determined their supply as an inverse of this demand.
LOCATION
In the age of COVID-19, it is important that we remember the two 3s. An infected person can go up to
three
weeks without noticing their symptoms and still being viral. Secondly, the virus can live on surfaces for up to
three
days. We used the number of Coronavirus cases by county scaled to the population to measure the risk of spreading the disease that one causes by going to a store and coming in contact with uncleaned surfaces and perhaps other people. This risk coefficient is multiplied with the precalculated cCost to generate the final price. This coefficient was generated through MinMax Scaling using
sklearn
Our Design!
Challenges we ran into
While we worked with a vast array of datasets and endless entries in each one, we managed to narrow that data down to two final .csv files. This was of course possible due to the tabular data processing possible on pandas. One of the problems we faced was the joining of two tables regarding Coronavirus case data by county and county population respectively. This was hard to join because the two tables had many inconsistencies about the names of counties and the spellings (including or discluding hyphens, etc). Additionally, there were numerous errors such as miscellaneous characters like .!~* appeaing before the names of counties at random. This cleaning process took a great deal of time and slowed down our processes a lot.
Using sk-learn and pandas helped us immensely with preprocessing.
Accomplishments that we're proud of
A great deal of data analysis and calculated fields are created by academia to measure different phenomenon every year. However, without proper visuals and interactivity to present that data, a lot can get lost in between the lines. That is why we made a special effort to make the Commonwealth app with bright, material, and easily navigable design. We're especially proud of the use of TensorFlow to automatically detect objects in the camera mode. However, overcoming the difficulty of properly implementing our design and wrangling the underlying data and algorithms together all in one night is definitely the accomplishment we're proudest of.
Using this data, we created a mobile interface on iOS.
What we learned
Commonwealth is a simple platform built on a myriad of different technologies.
We were able to collaborate and manifest this creative vision through the real-time interface design tool, Figma. This allowed us the export our ideal design to Swift for Storyboarding. The immense data wrangling process was mostly carried out in Python and used pandas and numpy to integrate different datasets and clean their content. sklearn libraries were used to scale datasets with wide spreads and decrease the variance of certain figure. To expedite the addition of items to the user's shopping cart, we used a TensorFlow Lite mobile-optimized classifier. This was our first intro to transfer learning. Furthermore, we all learned a great deal about the various impacts that the simplest purchases can have on our world.
What's next for Commonwealth
Commonwealth's systems are built specific to American coronavirus data and a broad global average for water footprints. However, the underlying concept transcends the USD and American borders. Climate change and coronavirus are both deeply global issues and the effects will disproportionately affect those in the developing world. A further venture could move to localize Commonwealth for different markets.
Built With
ar
figma
google-bigquery
google-maps
google-places
ios
kaggle
ml
numpy
pandas
swift
tensorflow
Try it out
github.com | Commonwealth | Reveal hidden social and environmental costs behind your spending to help save the world and your community. | ['Anurag Pamuru', 'Daniel Truong', 'Amy An', 'Jesse Liang'] | ['3rd Place'] | ['ar', 'figma', 'google-bigquery', 'google-maps', 'google-places', 'ios', 'kaggle', 'ml', 'numpy', 'pandas', 'swift', 'tensorflow'] | 48 |
9,890 | https://devpost.com/software/fitspo | Home Page. Click on notification.
Notification page. Your friend has challenge you, click to accept challenge.
Challenge Page. Start Challenge now.
Push-up challenge. I will be the first to reach 30 push-ups.
Push-up challenge. Sadly, i need more challenges.
Gamification Feature
Live Challenge
Inspiration
Due to Covid19, to prevent community transmission, outdoor exercises are limited and these truths are sinking in
-Your social life is lacking
-You are gaining fats
-Working out alone sucks
There are two main types of fitness apps
1) Personal fitness tracker (e.g. Google Fit app)
Encourage running, walking or bike rides through tracking of steps/heart rates/pulse. Such activities are not possible during Covid19. Time to re-invent; encourage people to keep fit with static exercises at home.
2) Instructional videos for static exercises (e.g.Home Workout app): Contains videos/instruction on how to do static exercises. However, it loses the social element of working out with friends.
There would always be temptation to have a short run in the park. But if everyone do so, the community transmission would not cease. How then shall we have our fun share of working out with connecting with friends?
Behold Fitspo mobile app.
What it does
Fitspo is the first mobile app that is able to count the number of static exercises done e.g. plank, sit-up, push-up, lunges, star-jump. Simply strap on the mobile app to your arm before working out.
Create a fitness regime (Challenge) and seek out your friends for the challenge.
No idea what type of fitness regime to do? Fret not, you can seek out the community/fellow fitspo online and take up their regime.
Earn achievements/badges for social recognition.
How I built it
Using accelerometer and gyroscope in phone/wearable to track static exercises.
For proof of concept, i research on the main type of static exercise that can be captured on the android phone.
Sit-up - Rotation Sensor i.e. Capture rotation (~90 degree e.g. portrait to landscape) motion from resting to sitting
Push-ups - Gravity Sensor i.e.Capture y-axis motion and magnitude of gravity from down/upward movement
Lunges - Significant Motion Sensor i.e. Capture “walking” movement from forward lunging
More variant of static exercises possible with some calibration.
Challenges I ran into
Trying to find the equivalent library function iOS. More research need to be done to roll out such features in wearable.
Accomplishments that I'm proud of
I have discovered new purposes for the accelerometer and gyroscope in phone.
What's next for Fitspo
Mark I: Use phone in-built accelerometer and gyrometer to track basic static exercise
Mark II: Use wearable to to static exercises and increase the type of static exercise that could be track
Mark III: Use machine learning to detect static exercise from phone camera
Mark IV: Able to detect pilates and yoga poses and issue advice to rectify postures
Mark V Add VR/AR gamification feature
Built With
kotlin
swift
wearable
Try it out
marvelapp.com | Fitspo | Covid19 limit outdoor exercises making usual fitness apps which track steps heart rate/pulse less relevant. If only there is an app that can track static exercise e.g. plank/push-up and make it fun. | ['Tan Glenn'] | [] | ['kotlin', 'swift', 'wearable'] | 49 |
9,890 | https://devpost.com/software/covid-19-test-centers-map-data | This page in our website contains a map with which users can enter their location and find the nearest Covid testing sites.
This page contains all the test centers per state through geodata and a quantity table.
This page contains a form with which users can submit their own testing locations.
The Team
We are a group of students that wanted to do our part in combatting the COVID-19 pandemic.
Inspiration
We saw that there were many drive-thru test centers for COVID 19, but there wasn’t a database that listed them all. So we wanted to become part of the solution to the COVID 19 crisis by making a website that includes all the testing sites.
What it does
The website we’ve created will compile the list of all the testing centers in the US so that the user can identify the nearest location. It also includes an interactive map, a data dashboard, a form to add more testing locations, and a contact page.
How we built it
First, we used Spreadsheets to collect the data. Then we used Wordpress.com to build the site. We used Storepoint to create the interactive map and also used Google Data Studio for the data dashboard.
Challenges we ran into
We had trouble collecting all the data because there was no single resource with all the testing locations. We had to go through various web pages and news articles to find the test center locations.
Accomplishments that we’re proud of
We’re proud of our cooperation in combining the map with the website. We are also proud of the many hours we put into data collection.
What we learned
We learned that cooperation is essential to success in a group project; if one person lacks, everyone suffers, and the whole project gets delayed.
What’s next for COVID 19 Test Centers Map & Data
The next step is to continue research and find all the test center locations in the US. However, to do this, it is necessary that we gain the public's help through crowd-sourcing; we can also work with other partners to collect more data.
Built With
css
google-data-studio
google-spreadsheets
html
iframe
storepoint
wordpress
Try it out
covidtestingnear.me | Covidtestingnear.me | A website to see the map of all the COVID-19 testing sites in the US, and find the nearest location to the user. | ['Saad Nawaz', 'Ahmed Nawaz', 'Amjad Nawaz', 'Tamjeed Nawaz', 'Zahid Nawaz', 'Tauheed Nawaz'] | ['Highlighted Project', 'Honorable Mention'] | ['css', 'google-data-studio', 'google-spreadsheets', 'html', 'iframe', 'storepoint', 'wordpress'] | 50 |
9,890 | https://devpost.com/software/covidseek | Inspiration
Since the beginning of this pandemic, many people globally are in a state of confusion and panic. Many healthcare systems need a way to allocate resources properly based on the density of the pandemic. Furthermore, many people do not know when this virus will keep spreading. We built COVIDSeek to answer these problems through providing an accurate visualization and predictions/forecasts of the pandemic.
What it does
COVIDSeek is a web application that connects people and healtchare systems through accurate information, and predictive analytics. Users enter their location to see a density heatmap of the virus on an international scale, which is also useful for medical practitioners and the healthcare system. They also will see the specific number of cases and deaths in their respective area on a given day. Finally, they are provided with a forecast of what cases might rise/lower to in the next 1-2 months.
How we built it
On the front end, we used html, css, and javascript through the bootstrap web framework. On the backend, we first use the google-maps api in python (through gmaps) to visualize the heatmap, and we passed this into an html file. Furthermore, we used Flask to serve the json data of the cases and deaths (across the world) to our front end, and SQLAlchemy as a way of storing data schema in our database. We use the FBProphet library to statistically forecast time-series data and future cases through Bayesian analysis, logistical growth, and predictive analytics, by factoring in trend shifts as well.
Challenges we ran into
We ran into challenges regarding the visualization of the heatmap, as well as the creation of our forecasting algorithm, as we didn't have much experience with these areas. Furthermore, serving some parts of the data to the front-end from Flask had some errors at first. It also took time to assemble data into a consolidated file for analysis, which was a bit hard in terms of finding the right content and sources
Accomplishments that we're proud of
We are proud of how much progress we've made considering how new we were to libraries such as FBProphet and Flask, and the unique, special, and effective way we learned how to implement it. We learned how to create opportunities to benefit different areas across the world through data analytics, which is something that we're very proud of doing.
What we learned
In terms of skills, Aryan learnt how to develop his front-end skills with Bootstrap and using different ways of styling. Shreyas also developed his front-end skills while working with Aryan to structure the front-end, as well as finding new skills in learning Flask and the Gmaps API. We learnt that there are numerous ways that an individual can help the world around them through computer science.
What's next for COVIDSeek
In the future, we want to add a user-interactive search bar that places a marker on their location and zooms into the map, as well as a way for users to report symptoms/cases on the map. We also want to add more features, such as nearby testing sites, hospitals, as well as nearby stores with a certain amount of resources that they might need. Overall, we want to make this web app more scalable worldwide.
Built With
bootstrap
css3
fbprophet
flask
google-maps
html5
javascript
matplotlib
numpy
pandas
python
sqlalchemy
Try it out
github.com | COVIDSeek | Serving healthcare systems and people through accurate data tracking, visualizations, and forecasting of the coronavirus | ['Shreyas Chennamaraja', 'Aryan Agarwal'] | [] | ['bootstrap', 'css3', 'fbprophet', 'flask', 'google-maps', 'html5', 'javascript', 'matplotlib', 'numpy', 'pandas', 'python', 'sqlalchemy'] | 51 |
9,890 | https://devpost.com/software/covaid-53hv21 | CovAid Register Page
CovAid Login Page
CovAid Requests Page
CovAid Requests Viewer
CovAid Request Submission
CovAid Home Page
Inspiration
The world we live in has changed dramatically amidst the COVID-19 outbreak. Although some of us are safe at home with the proper equipment, a large portion of the population does not have access to essentials. In analyzing the issue, we realized the immunocompromised currently had no access to essentials as they could not simply leave their houses to go to a grocery store. We decided to provide a solution to this problem by creating a website in which we could allow users to make virtual requests for items, such as toilet paper or hand sanitizer, and then enable volunteers to accept these requests to donate supplies to them. As there is no preexisting platform that allows for direct pairings between users and volunteer deliverers, we believe this is the perfect solution to help those most impacted by COVID-19.
What it does
CovAid is a web application that connects volunteers to those in need during the COVID-19 outbreak using AI-driven intelligence. The website connects at-risk users with volunteers willing to donate necessities. Users can make requests for items to the website and volunteers can respond to those requests. These pairings are created efficiently with a machine learning algorithm that takes into account various factors such as the distance between the user and the volunteer.
How we built it
Through the development of CovAid, we were able to learn how to integrate Flask, JavaScript, and jQuery as our back-end with HTML and Bootstrap together to develop a website from scratch. We used SQL to operate the database of users and the Google API to calculate the miles and estimated time between users. These topics were new to us and we were able to truly learn how to integrate every part together to create a fully-functioning website. In order to perform the matching between users and volunteers, we developed a Machine Learning Neural Network model to sort the requests on a volunteer’s page, as we wanted requests most relevant to the volunteer to show up when a volunteer is searching for a request to accept. We used Keras, NumPy, Pandas, and a Sequential Machine Learning Neural Network model with Dense layers to develop our model before implementing it into our website.
Challenges we ran into
We faced numerous challenges when it came to properly communicating with Flask view and the various HTML templates. Since CovAid is a dynamic site form data had to be sent back and forth between the files and stored in a database. Using a database was something new to all of us and understanding how to integrate it for our needs was a major roadblock for a while. Another major challenge was implementing our machine learning sorting algorithm with our Flask and HTML to sort the requests for each volunteer, since we had to learn how to get live user data to enter into the model.
Accomplishments that we're proud of
We are proud of how we could efficiently push out a website while allowing everyone on our team to contribute equally. After beginning with our entire team working together to create the basic layout of our website, we split up into two teams. Shrey and Atin worked on the front-end and back-end of the website while Anirudh and Aarav worked on the machine learning aspect of the project. We also learned various CS skills while also helping our community at the same time. In addition, we are also pleased that we have created another scenario that AI can help ease our lives. We are excited to see how our project will be able to create opportunities for other people to make a positive impact on their surroundings.
What we learned
In developing CovAid, aside from exploring new software such as Bootstrap and Flask, we fully understood the broader impacts of our project — that any simple act of kindness can be influential, especially to those that are impacted the most from issues like these.
What's next for CovAid
In order to create a real difference in our community we hope for CovAid to be more widespread and have a larger impact on the world. We also want to implement a system in which users are able to be further interconnected. Our vision is that through our product everyone will have access to essentials and will stay safe as our world continues to change from COVID-19.
Built With
bootstrap
css3
flask
google
html
javascript
jquery
keras
machine-learning
numpy
pandas
python
sqlalchemy
Try it out
github.com | CovAid | CovAid is a web application that facilitates deliveries to those in need during these pressing times. The website connects at-risk users with volunteers willing to donate necessities. | ['Atin Pothiraj', 'Aarav Khanna', 'Shrey Gupta', 'Anirudh Bansal'] | ['2nd Place'] | ['bootstrap', 'css3', 'flask', 'google', 'html', 'javascript', 'jquery', 'keras', 'machine-learning', 'numpy', 'pandas', 'python', 'sqlalchemy'] | 52 |
9,890 | https://devpost.com/software/safe-zone-40qd1a |
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Safe Zone will be a free mobile app available via Play Store
This app creates a scanning zones around the user to detect nearby mobiles. It may then calculate their distance from the user.
The app also be used to detect vehicles, The user will hence be able to walk around safely even with his attention focused on his phone.
The appropriate distance from each other according to WHO World Health Organisation) is 6 feet or about 2 meters
My inspiration
In today's fast-developing society, people often have their attention focused on their mobile phones in various situations (e.g; walking on the street, going to the mall or other public places.). Consequently, people may not even realize when they are getting to close to one other. not respecting the social distancing may then increase drastically the probability of getting or spreading the viruses from one to another.
What it does
This app may use Bluetooth or Mobile-Hotspot to create a scanning zone around the user to detect nearby mobiles. By measuring the strength of the signal from each device, the app can then calculate their approximate distance from the user. The app will create a safe zone of about 6 feet around the phone. It will then informs the user by vibrating the phone or by sending a notification if someone comes to close.
How I built it
Currently, it is just an interesting idea and I haven't built it yet due to my lack of knowledge in programming.
This is why I'm looking forward to your help. DEVPOST being the greatest platform for innovation and development of software; your help will be the most welcome in the development of this app.
Further possible application of this app
1.An App for blind People
The app can be modified to detect obstacles (E.g; street lamps and buildings) as well as facilities ( E.g pedestrians crossing or road sign) such that blind people could move around safely and easily.
Small and cheap sensors such as Qr code can be added to those obstacles and facilities such that the app could identify them and inform the user of their presence.
2.Monitoring the evolution of the traffic congestion of pedestrians and vehicles
Vehicle and pedestrian Traffic Monitoring is also another important application as understanding the flow and congestion of traffic is essential for efficient road systems in cities. Smooth flows reduce journey times, reduce emissions, and save energy. Similarly, the efficient flow of pedestrians in an airport, stadium, or shopping center saves time and can make the difference between a good and a bad visit. Monitoring traffic - whether road vehicles or people - is useful for operators of roads, attractions, and transport hubs.
Monitor in real-time the number of vehicles passing for a certain point in highways and roads
Detect the average time of vehicle stance for traffic congestion prevention
Monitor average speed of vehicles in highways and roads
Provide travel times on alternate routes when congestion is detected
The monitoring system can also be used to calculate the average speed of the vehicles which transit over a roadway by taking the time mark at two different points.
Thes data can then be used for better management of traffic. By doing so vehicles can be distributed more efficiently across the different roads diverting some of them from the overused road to the underused one for a smoother flow.
The app can also be used to calculate the best path for a pedestrian such as to meet the fewer people possible on their way to their destination. By doing so, they minimize the risk of contamination | (((Safe zone))) | Safe Zone will be a free mobile app which will help in keeping a safe distance from each other. By using Bluetooth or mobile Hotspot. it will detect nearby mobiles and vibrate if someone come to close | ['Wong Piu Yee Christopher How Feng', 'Jalal L'] | [] | [] | 53 |
9,890 | https://devpost.com/software/solocoin | Home Page
Social-distancing timer and Challenges
Detailed Rewards
Rewards and Coupons
Detailed Leaderboards
Leaderboards, Milestones, Badges
The Inspiration
COVID-19 has severely affected the livelihoods of the people around the globe. With people getting infected by COVID each passing day, Consumers have learned to stay home, preserve the money they have, and consume less. However, this affects the economy. Similarly, local businesses have experienced unprecedented losses. For those that have survived this period the possibilities for re-opening, recovery, and growth are limited and possibly bleak. SMBs don't have a sustainable solution through which they can grow their businesses again and recover their losses. Traditional ad-tech channels are broken and without significant investment, any business can't get RoI. But, with inbuilt game mechanics, we can motivate people to purchase and help SMBs advertise their product without any upfront investment. So, the perfect way to help both sides of the community SMBs and making it fun for people suffering to help the SMBs is gamification.
I’m a co-founder of a Blockchain gaming company. I (Arbob) have a lot of experience in implementing game mechanics inside consumer apps. So, I thought, why not apply the same gamification techniques I implement in blockchain into a consumer app to encourage people to do a task based on their location, spend more and help SMBs in economic recovery. So I started to scout for members in hackathon channels and got an amazing team to build this idea.
What it does
SoloCoin is an app that rewards users in virtual coins based on their location. Currently, to engage in social-distancing, based on their location to their home. It's basically an app that rewards users according to their proximity/location. (using the tech of GPS, Geofencing, and Accelerometer - to track whether the phone is put on idle or not - awards will be rewarded only when the user is using the phone). If their smartphone is within a certain radius (~20m) to a reward hotspot (Geofencing reward location), then the app will reward them with virtual coins that they can later redeem for "Partner Coupons". These partners can be SMBs, Local businesses, and any online B2C businesses from E-commerce, Entertainment, Lifestyle, Health, etc. Our app rewards, nudges, and drives beneficial consumer behavior. This will help post-COVID to accelerate economic recovery in local communities. They can also compete with nearby players for achievements and badges which they can later share with their friends.
The global ads market is $600B+ with digital advertising accounting for $220B+. But there is a niche in hyperlocal digital-ads where specifically no-one has targeted their solution. Just in India, $650B is the size of traditional retail shops. Simple and Engaging Advertising needs of such a big segment of the market will create an ad-tech boom in the hyperlocal sector in India. We can be at the forefront of this market in the next 5-7 years.
How we built it?
When Arbob had the idea, he started building it on Python's Beeware toolkit which ports python code to a native mobile app. Then, he started to scout for members across hackathons. Since the idea was innovative and exciting, many amazing people joined his team. then the team decided to make it on native android and iOS directly for better usability and support. We have a group of open-source collaborators from around the world including startup founders & CXOs, people from IITs, BITS, Stanford, Microsoft, Uber, Github, Neuro-Researchers and more among our community, with expertise in Frontend (android-studio, Java, Kotlin), Backend (rails+postgresql | RDS) mobile development, Marketing, Product and Design (Adobe xd), etc. AWS is the cloud service provider with authentication from Firebase and Maps from google. The product is built 100% remotely.
Challenges we ran into
The first challenge was finding good team members with relevant expertise. But, we got through with it thanks to our hard work and to the volunteers joining because of the innovative nature of the product.
Management of tasks. To tackle this issue, we created a Trello and GitHub task board for better collaboration.
*
One thing I've noticed while building our product is the founder's vision for the product is very important. When I first pitched the idea of SoloCoin, I was alone. But the idea was innovative and has never been done before. The vision I put to gamify social-distancing resonated with people and that helped us in getting amazing people with great skills from all over the world to voluntarily work on my idea without any financial incentive. People were and still are excited every day they wake up to build and scale the product. And, that's how from an idea it became an MVP and then a working product. *
Accomplishments that we're proud of
The clarity in our concept.
Innovative approach towards social-cause using gamification.
1000+ sign-ups for the app launch.
5000+ user visits since the website launch.
The prototype was done in 3 days.
Beta Launched in Play Store.
Positive feedback and support from across the community, both tech and non-tech. Influencers asking to promote our app for FREE. Many top-level government officials agreed to promote our product in their network.
Partnerships for reward coupons.
Partnerships with multiple influencers.
An amazing team of 20+ open-source collaborators. 100% remote.
Tackling a greater cause to eradicate COVID-19 from the face of the earth through gamified social-distancing.
Won India’s largest COVID Hackathon - CODE19.
Won World's largest COVID Hackathon - EUvsVirus.
Incubated in European Innovation Council's business accelerator and Central European University's Innovation Lab.
What we learned
Clear documentation from both product and development perspective for faster user onboarding.
The idea of making a difference motivates a person more than money.
Distribute department leads early on to avoid clashes among members. We were lucky that we did it early on.
Single design philosophy to avoid misalignment of colors, content that is being pushed on the internet for promotion and visibility.
Don't let negative people come anywhere near your extremely positive and passionate team. Take care of your team.
Regular motivation and clear communication are very important when managing a remote team.
Just like you can’t expect 9 women to give birth to a baby in 1 month, you shouldn’t expect 10 developers to build a product of a global scale in a day.
Ideas are the most valuable asset to any startup. That's why we have a dedicated #ideas channel our discord for people to post ideas that can help improve our product. Till now we have received 20+ ideas since the project's inception.
Planned Revenue Model
Brands pay us for promoting their deals via Rewards. Sponsors will pay us to get their rewards listed on our app. Like if a new e-commerce startup wants better visibility of their product, they can pay us some fee and get their brand coupon listed on our app, which users can redeem for coins and purchase stuff from their website.
Sponsored badges and goals. For e.g.When someone redeems the coins for Amazon Coupons they’ll get an “Amazon Badge of Honor” which they can share in their social media.
Sponsored Daily/Weekly/Monthly Rewards via Challenges
Location-based targeted ads for grocery, medicine, and other necessities. Later we can open it for a broader audience and sponsors.
Subscription model for faster coin generation.
Impact of SoloCoin
SoloCoin will have a massive impact on the reduction of COVID-19 transmission and on society as a whole. Some of them are:
Impact on the society - It is a “simple way” for citizens to do their part, practices social distancing, and get real economic rewards and esteem awards to show they stood up to the challenge! They can be proud of themselves.
Impact on Public Health Officials: Public Health Agencies can get access to anonymized data insights from our app to help form better messaging to inform/educate the public. ‘
Impact on Partners-Sponsors: Coins can be exchanged for exciting "Partner coupons". These partners can be SMBs, Local businesses, and any online B2C businesses. Our app rewards, nudges, and drives beneficial consumer behavior. This will help post-COVID to accelerate economic recovery in local communities and help get their businesses on track.
Impact on People-driven Health and Wellness: having an all-access platform for behavioral change with people from all around the world will help support post-COVID measures, promoting healthier physical, mental, and social behaviors.
What's next for SoloCoin?
Making this a global app with partner support.
Rewarding for good habits like washing hands, timely self-isolation, Yoga, etc.
Determining efficient/less crowded routes for commuting and avoiding people.
Give users the possibility to chat with nearby quarantined people.
Map list of nearby available essential stores for groceries, medicines, etc.
Adding an anonymous user authentication system.
Give users the possibility to add a mask to their profile picture to tell people they are practicing social-distancing.
What after COVID ends?
We have identified many ideas on what we can do with our tech post-COVID. Some of them are:
Our app can be used for concerts/stores, basically to gather people. The more they stay the more they earn. That way sponsors will get better revenue as well.
Chat with friends, hang out, and earn rewards as you have a good time! Real-time location tracking automatically detects when you and friends are out and about, so you can passively earn points all day.
Use group messaging and location sharing to stay in touch with your friends at any time. Emergency contact features let you get in touch with your circle whenever you need a helping hand because friends watch out for each other!
At the current stage, the app is used as a Social-distancing app with "Home geo-fence". After COVID, we can increase the geo-fence to multiple locations like "rewarding concerts, malls, local stores, etc." Our app can be used for concerts/stores, basically to gather people. The more they stay the more they earn. That way sponsors will get better revenue as well. Our app rewards, nudges, and drives beneficial consumer behavior. This will help post-COVID to accelerate economic recovery in local communities. Now if you think about it we're venturing into not a consumer app space but rather "Ad-tech" space. Our app can now be at the forefront of "Hyperlocal based targeted ads", basically directly competing with Google's and Facebook's Pay Per Click.
Shopping is fun in real life. You can go with friends, treasure hunt for bargains, discover new products, better understand a brand’s vibe, ask store reps questions, and also touch and feel items so you have more confidence you’re going to love it. The shopping revolution we bring will COMBINE ecommerce and entertainment, where both are equal in importance. Toss in gamification and boom. We may see waves of new brands and more pricing transparency for everything... products and services, alike.
We can gamify people’s entire lives, everything they do, with the tech and 20+ sensors present in smartphones.
Our vision is for a healthier, happier, COVID-19-free world and we can't wait to launch this app in the global market and help make the world a better place.
You can also look at our
Product Roadmap
for the present and the future to get an idea of where we are headed.
App Demo Video can be found here
Built With
amazon-web-services
android-studio
firebase
geo-fencing
google-cloud
google-maps
java
postgresql
ruby-on-rails
Try it out
xd.adobe.com
www.solocoin.app
github.com
drive.google.com
docs.google.com
play.google.com | SoloCoin | Get rewarded to shop locally with your friends. Helping SMBs and local businesses towards economic recovery and recoup their losses due to COVID. | ['Arbob Mehmood', 'Adesh Bhansali', 'Aditya Sonel', 'Aayush Patni', 'Vijay Daita', 'Narayani Modi'] | ['Challenge Winner'] | ['amazon-web-services', 'android-studio', 'firebase', 'geo-fencing', 'google-cloud', 'google-maps', 'java', 'postgresql', 'ruby-on-rails'] | 54 |
9,890 | https://devpost.com/software/community-map-gzfnqv | Inspiration
Creating a community of various projects and services - ecosystem where projects grow and develop easier.
What it does
Location-based software platform acting as a
hub for various services
helping people in certain areas to
get better visibility, communicate and collaborate
more efficiently.
It’s basically an
open map
-like system allowing people and local (or global) businesses to participate in it using some of the services it provides. Part of them are built-in and developed by us, others -
by partner projects and developers
thus making the platform extendable and adaptive to various environments and conditions.
One of the biggest problems with crisis projects and initiatives is actually
segmentation
and lack of critical mass of user to get it going, too big Time to Market.
That's why we shifted our focus to helping other projects develop by providing the base to build upon.
It's not just the crisis project - such ecosystem is needed for more effective society that would be ready and more responsive for any current or future situation.
The information in the platform is anchored to a certain physical
locations
or
areas
(location + radius). It can also be seen as
layers tagged with category/topic
.
The users are able to
upvote the information
they like or find important. This and filtering the relevant content helps to
reduce the informational noise
to a much more bearable level.
The short list of relevant projects include:
Local community volunteers
Online ordering and delivery
Social games and storytelling
Business survival
Community journalism
Local crowdfunding
We now have 2 ways of integration with 3rd parties - React-based SDK (powerful and flexible) and embedding with iframe (easy) along with public REST API.
We managed to create several important partnerships in the last month with projects using Open Community Map as service provider.
One of them is
Non-Zone
project - the global map for experiential and solo-travelers - our platform served efficiently for storing and retrieving project data along with fully customizable UI - dark theme of the map, their own design of all main components and controls.
How we built it
Google Maps API, Firebase, React, Typescript
Challenges we ran into
Finding the right niche to focus on.
Building reusable technical stack providing enough value.
Accomplishments that we're proud of
Growing community around the project. Finding strengths to keep going.
Creating React-based SDK allowing powerful integrations.
Built partnerships with real-world projects, fulfilling their needs for custom UI and behavior with our SDK thus validating the idea.
What we learned
There are many like-minded people out there, trying to improve the world around them. No need to compete with them, better make friends and grow together.
What's next for Community Map
Building bigger community around the project
Building flexible enough technical solution that would work for most projects
Creating more value for partners and end users
Join our Community!
Feel free to join our
Slack workspace
- we're looking for partners, supporters and collaborators!
Built With
firebase
google
map-embedding
maps
public-api
react
Try it out
communitymap.online
github.com
www.opencommunitymap.org
docs.google.com | Open Community Map | Platform for building local community and location-based services. Partnerships with other projects. | ['Dmitry Yudakov', 'Ivan Orlović', 'George Petrov', 'Ivan Stavrev', 'Станко Йорданов', 'Mohamed Hany'] | [] | ['firebase', 'google', 'map-embedding', 'maps', 'public-api', 'react'] | 55 |
9,890 | https://devpost.com/software/remotedoc | Website main page
COVID-19 screening via chatbot
Remote meeting scheduling via chatbot
Doctor enrollment web page
Patient web page with chatbot enabled
Depression screening via chatbot
The problem your project solves
The huge workload of healthcare workers and the risks of being infected during the crisis;
the difficulty of effectively informing patients regarding their medical and psychological condition because of the healthcare system overload.
The solution you bring to the table (including technical details, architecture, tools used)
The AI-empowered chatbot integrated with telemedicine and e-health systems, which takes over human work. RemoteDoc effectively categorizes patients by their condition, providing screening questionnaires, and scheduling remote consultations. This reduces the risk of being infected during the pandemic time and saves cost and time for healthcare institutions in general. Apart from helping with infectious diseases, the chatbot can do pre-screening for the psychological condition, which is extremely important during times where a lot of people are being isolated and need psychological help. The chatbot can be integrated into WhatsApp and Messenger.
We used the
landbot.io
platform for the chatbot creation. Connected with
Calendly
, it allows scheduling of meetings using
Zoom
or
Google hangouts
. Doctors are being registered in
Hubspot
. The patient pre-screening information is transferred to the doctor via Calendly and Hubspot, so doctors can check this information wherever it's more comfortable for them and organize it similar to medical histories. We created a web site with an explanation of the process and a possibility to try out the functional demo for patients and enroll in the system for doctors.
What have we done lately
added the depression scoring to the bot
defined EU countries to start with (Switzerland, Estonia, Lithuania)
conducted a lot of validation interviews
requested introductions to the relevant professionals and received them (thanks to mentors)
composed a list of over 1000 professionals to be contacted further
improved our technical knowledge (thanks to mentors)
improved our knowledge on legal matters (thanks to mentors)
composed a questionnaire for gathering feedback from the demo users
https://marcmhartmann.typeform.com/to/tP3d5W
conducted demonstrations and got initial feedback
improved website
fought some bugs
The solution’s impact to the crisis
Reducing workload on human healthcare workers by providing patient triaging
Speed up of patient screening process
Accessibility of information for patients regarding their condition (bot works 24/7 via WhatsApp & FB)
The necessities in order to continue the project
GDPR compliance (privacy policy and so on)
Intros to healthcare professionals
The value of your solution(s) after the crisis
Optimization in the patient triaging process will still be valuable. The telemedicine consultations will be more common, hence tools like RemoteDoc will have more demand.
The URL to the prototype
https://remotedoc.co
Inspiration
Right now the healthcare system experiences immense overload and there is a huge need in process optimization and infection risk reduction.
We were inspired by the idea of helping doctors and patients to secure themselves using telemedicine during the coronavirus pandemics.
Challenges we ran into
It is challenging to
find the accurate risk scoring algorithm for the risk of having the COVID-19
. So we need to re-iterate this with expertise from healthcare professionals.
We've got the initial validation of our idea during the interview stage, but we need the real validation of the product.
We are actively looking for the organization to run the pilot with.
We also
need an expert to help us with the legal side of this project
— GDPR compliance, understand the legal implications of using this system for doctors and psychotherapists within and outside of the EU, how do we create the terms of service and so on.
Accomplishments that we are proud of
We made a working minimal viable product (demo) in less than 24 hrs.
What we've learned
We learned that hacking tools like this can be really fun. We are looking forward to getting our expertise and knowledge to the point where it would really help people.
What's next for RemoteDoc
We are keen on getting the validation of this product with the professionals and run a pilot ASAP.
Contact representatives of hospitals (doctors or who has the core link to the hospitals) to search for pilot users (hospitals or clinics) or those who can help with the real-life case. We would like to let more people try out our product and give us feedback.
Create privacy notice
Run pilots, improve, re-iterate
in the future, RemoteDoc will be integrated with e-health systems in hospitals.
Built With
calendly
css
hangouts
hetlify
html5
hubspot
javascript
landbot.io
zoom
Try it out
remotedoc.co
remotedoc.co | RemoteDoc | A platform for telemedicine consultations, built with AI-empowered chatbot which pre-screens patients. It saves time and effort of healthcare workers. Also prevents unnecessary physical contact. | ['Artem Poliakov', 'Konstantin Amm', 'Ü-Ming Huang', 'Slava Gornostal', 'VadimKovalenko', 'Alex Borodin'] | [] | ['calendly', 'css', 'hangouts', 'hetlify', 'html5', 'hubspot', 'javascript', 'landbot.io', 'zoom'] | 56 |
9,890 | https://devpost.com/software/covid19-outbreak-and-npi-prediction | Coronaob.ai - Pandemic Outbreak and mitigation prediction
1.Overview
Coronaob.ai is the ultimate tool for predicting epidemic trends. It has been built with the help of artificial intelligence and statistical methods. This epidemic forecasting model helps in giving a rough estimate about the future scenario and also helps in suggesting non-pharmaceutical/mitigation measures to control the outbreak with minimum efforts. This will give a head start in the preparations that are made to curb the pandemic before taking the lives of people.
Note: An NPI is the same as a mitigation measure.
2.What exactly is the problem?
During any pandemic, it's difficult to scale up the implementation of the mitigation measures, this is often because of the chaos that is caused during the pandemic. It often becomes an unseen situation wherein the authorities lack smart judgment on which step to take further which makes the situation even worse. It's not always necessary to implement the strongest mitigation measure as medium-strength mitigation can get the job done, thus giving more weightage to the economic stability and other subjects.
3.What can be done to tackle this issue?
A strategy that can give a rough picture of the future scenario describing the number of cases and the area of spread can give an insight into what could better be done to reduce the effect in an easy and cost-effective manner. Also, having a record of previously taken successful-steps can also provide much boost to this strategy.
4.Our Goals
a. To give an estimate by forecasting the number of cases and trends in the spread etc, which will give a good construction of how the scenario would be.
b. To suggest/predict the best suitable mitigation measures, according to previously taken successful steps, thus saving resources and not creating chaos.
c. To make this approach a robust one, so that any agency working on
5.Milestones
Prototype stage
: We have completed our first stage training and testing on the covid19 data and have achieved over 90% accuracy in predicting the new cases the immediate next day and over 85% accuracy in predicting the long term scenario.
On the mitigation prediction part, we have achieved an accuracy of 91.8% and we were successful in bringing down the hamming loss to as low as 8.2%.
Accuracy
: Our method is one of the most accurate ones among the others in predicting such trends.
6.Specifications
Our submission is a script containing the machine-learning models that can be boosted with an interesting UI as mentioned in the gallery picture.
7.Technical details
Major tools used
:
a. Kalman filter
: It’s an algorithm that uses a series of measurements observed over time, containing statistical noise and other inaccuracies, and produces estimates of unknown variables that tend to be more accurate than those based on a single measurement alone, by estimating a joint probability distribution over the variables for each timeframe.
b. Regression analysis
: It’s a set of statistical processes for estimating the relationships between a dependent variable (often called the 'outcome variable') and one or more independent variables (often called 'predictors', 'covariates', or 'features').
c. Scikit-learn
: Scikit-learn (formerly scikits.learn and also known as sklearn) is a free software machine learning library for the Python programming language.[3] It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.
8. Dataset Description
Some details regarding the columns of mastersheet prediction are:
Each row is an entry/instance of a particular Npi getting implemented.
Country: This column represents the country to which the entry belongs to.
Net migration: The net migration rate is the difference between the number of immigrants (people coming into an area) and the number of emigrants (people leaving an area) throughout the year.
Population density: Population density is the number of individuals per unit geographic area, for example, number per square meter, per hectare, or per square kilometer.
Sex Ratio: The sex ratio is the ratio of males to females in a population.
Population age-distribution: Age distribution, also called Age Composition, in population studies, the proportionate numbers of persons in successive age categories in a given population. (0-14yrs/60+yrs %)
Health physicians per 1000 population: Number of medical doctors (physicians), including generalist and specialist medical practitioners, per 1 000 population.
Mobile cellular subscription per 100 inhabitants: Mobile cellular telephone subscriptions are subscriptions to a public mobile telephone service that provides access to the PSTN using cellular technology.
Active on the day: The number of active cases of covid19 infections in that particular country on the day it was implemented.
Seven-day, twelve-day and thirty-day predictions are for active cases from the date it was implemented.
And the date-implemented is converted to whether it was a week-day or a weekend to make it usable for training.
The last column represents the category to which the NPI that implemented belonged to.
9. I/O
Input
: The epidemic data such as the number of infected people, demographics, travel history of the infected patients, the dates, etc up till a certain date
Output
: 1) Prediction of the number of people who will be infected in the next 30days.
2) The countries that will get affected in the next 30days.
3) The mitigation/restriction methods to enforce such as curfew, social distancing, etc will also be predicted, to control the outbreak with minimalistic efforts.
10. Dividing the measures into categories:
Category 1
: Public -health measures and social-distancing.
Category 2
: Social-economic measures and movement-restrictions.
Category 3
: Partial/complete lockdown.
To categorize the npis we followed a 5 step analysis
:
Step 1
: We chose 6 different countries that have implemented at least one of the above-mentioned npis.
Step 2
: We had chosen a particular date wherein one of the NPI was implemented.
Step 3
: From that date (chosen) we had calculated a 5day, 8day, 12day growth rate in the number of confirmed cases in that country.
Step 4
: According to 1)
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4327893/
2)
https://www.worldometers.info/coronavirus/coronavirus-incubation-period/
we took a reference that, over 50% of the people who are affected on day1 show symptoms by day5, over 30% of the people affected on day1 show symptoms by day8 and the last 20% start showing symptoms by day12. Assuming that, they get a checkup as soon as they are showing symptoms, we had calculated a cumulative growth rate.
Step 5
: This cumulative growth rate was not very accurate due to the population densities of the countries being different. So, we had normalized the obtained scores from step4 by the population densities. That gave us the following results.
More information can be found here:
link
[ (896.4961042933885, 'CHINA', 'SOCIAL DISTANCING'),
(720.7571447424511, 'FRANCE', 'PUBLIC HEALTH MEASURES'),
(578.0345389562175, 'SPAIN', 'SOCIAL AND ECONOMIC MEASURES'),
(527.7087251438776, 'IRAN', 'MOV RESTRICTION'),
(484.1021819976962, 'ITALY', 'PARTIAL LOCKDOWN'),
(207.67676767676767, 'INDIA', 'COMPLETE LOCKDOWN')]
Ex: (Cumilative growthrate(normalised), Country Name, Measure-taken)
So the above analysis shows the decreasing order of growth rates and increasing order of strength, however, this is not very accurate due to various other reasons, but this gives a rough estimate of the effectiveness/strength of the npis.
11. Working
a
. The inputs given regarding the previous days’ record of the outbreak are first filtered by the Kalman filter and then further the modified inputs are sent to the regression model which will predict the scenario with better accuracies than any other simple regression model.
b
. Then the predictions from the above models are fed into the machine-learning model which will further help in predicting the mitigations to be used, based on the previous history given in the literature, ex-social distancing.
c
.We performed 10 Folds Cross-Validation by dividing our data set into 10 different chunks, then running the model 10 times. For each run, we designate one chunk to be for testing and the other 9 are used for training. This is done so that every data point will be in both testing and training.
12. Conclusions
This method can help the authorities to develop and predict various mitigation measures that will help in controlling the outbreak effectively with minimum efforts and chaos.
13. What did we learn?
a
.This project was challenging in terms of the conceptualization and data collection part, there was no direct data available. We learned how to take relevant data from different datasets, engineer them, and use it for our purpose.
b
. The regular regression algorithms failed in giving accurate results, so we had to think something different that can increase accuracy. Thus, we came across the idea of using the Kalman filter, and using these updated inputs we could achieve better accuracy.
c
.Since we had to take regions having more than 1000cases only for the effectiveness of data, the overall dataset became small, deep-learning models failed. This made us switch to machine-learning algorithms.
d
. We also used clustering algorithms which gave a deep understanding of why these work better in some situations.
e
. Also due to some problems, it was exciting for us to use both R and python in a single notebook thus adding it to our learning.
14. The drawbacks of our approach
a
. This above-mentioned approach has many drawbacks, one of them is an incomplete dataset.
b
. There are no good-differentiating features in the dataset.
c
. In our approach, we are not able to decide the effectiveness and a go-to plan of action for deploying npis.
All the data-points are very-similar to one-another, hence it is being difficult for the algorithm to learn.
15. What improvements do we want to make further?
a
.There could be a set of strong differentiating features in the dataset, which will make the generalization easy.
b
.There can be a further categorization of npis for better implementation of them.
c
.The dataset can also be combined with economic parameters further, to understand the economic feasibility of the NPI-implementation.
d
.It can further be used to predict the decrease in growth rates, once an NPI is implemented to further note the real-time effectiveness of the npis in a particular demographic
15. References
a
.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4327893/
b
.
https://www.worldometers.info/coronavirus/coronavirus-incubation-period/
c
.
https://archive.is/JNx03
d
.
https://archive.is/UA3g14
All the other references are mentioned in the submission notebook at every step.
16. Product Roadmap
The coronaob.ai team has the functionality of the platform. We are currently in the process of bringing our front-end up to speed with our U/X designer's wireframes. Below is our Product Roadmap post hackathon submission:
a.New Security Features
b.Admin Dashboard
c.Analytical graphs
17. The team
a. Saketh Bachu - Machine-learning
b. Gauri Dixit - UI/UX development
c. Shaik Imran - Medical Expert/Design
Built With
kalmanfilter
matplotlib
numpy
pandas
scikit-learn
Try it out
github.com | Coronaob.ai - Pandemic Outbreak and mitigation prediction | Coronaob.ai is built with the help of AI and statistical methods. This model helps in forecasting the number of cases and also predicts mitigation measures to control the outbreak. | ['saketh bachu', 'Sandeep Kota Sai Pavan'] | ['Third Place'] | ['kalmanfilter', 'matplotlib', 'numpy', 'pandas', 'scikit-learn'] | 57 |
9,890 | https://devpost.com/software/a-i-powered-digital-hospital-coronavirus-laboratory-9qsact | Diagnosis Report
A.I Generated Prescription Report
Patient & Doctor Room
Mental Health Test
Digital Nurse
Mental Relaxation Exercise (Full Screen)
A.I Coronavirus Diagnostic Test
Mental Relaxation Exercise
A.I monitored Doctor Consultation
The problem our project solves
:
As the Coronavirus outbreak continues to engulf the globe, the world's scarce healthcare resources threatened to be overburdened. There is 1 doctor for every 666 patients and 2.7 hospital beds per 1,000 persons. This makes overcrowding of hospitals and burnout of healthcare workers a likely scenario. The lack of qualified health personnel in remote regions along with the high population density is a major concern as the world fights coronavirus.
People need someone to guide them, assist them, listen to their problems, and help them feel relaxed. It is the need of the hour to assist hospitals and laboratories by reducing their burden, and offloading the patients to a digital hospital.
The solution we bring to the table (including technical details, architecture, tools used)
:
Our platform provides a COVID-19 diagnostic test that will analyze the responses to the questions through integrated technology like computer vision for facial analysis, NLP to parse the user responses, location identification to calculate distance from the nearest COVID-19 cluster, and voice recognition. These technologies will help in generating the diagnosis report and determining the probability of a person having the Coronavirus. After completing the test, patients will have the option to visit the doctor’s room to get their report reviewed and get a consultation from the doctor instantly. The patients are sort according to the potential risk, and high-risk patients can book the COVID-19 physical test from our partnering labs directly from the platform. There are additional rooms like Mental Health Room, Nutritionist Room, Digital Nurse Room, and Reception Desk like an actual hospital, which altogether helps in assisting the patient through the current challenging time, and living a balanced and healthy life.
The solution’s impact to the crisis
:
In order to ensure every COVID-19 patient receives quality treatment, it is essential to offload workload to automated systems. The A.I powered digital hospital and coronavirus laboratory will help in performing millions of COVID-19 tests each minute and provide A.I monitored real-time doctor consultation to patients.
We aim to provide quality healthcare to each and every individual across the country, which is not possible through traditional hospitals which are expensive to set up.
The digital hospital and laboratory will provide the country’s best healthcare facilities to all the patients across different states through the power of A.I
The necessities in order to continue the project
:
We would need to on-board more doctors on the platform to ensure that we can provide consultation and assistance to all the patients in these tough times. Also, we would require COVID-19 positive patients to take our diagnostic test so we can fine-tune our testing algorithms.
The value of your solution(s) after the crisis
:
Initially, we are focusing on COVID-19 due to the current panic and increasing risk. But in the coming weeks, we would add more tests to our digital testing laboratory, which would be powered by the same A.I technology, which we are developing for our COVID-19 test. That way, by reusing the A.I technology, we can add many more tests like a general weekly health checkup, mental health checkup, diet checkup, etc. for the users of our platform. We have planned to keep all the diagnostic tests in the digital laboratory free of cost to bring a large number of users and help them alleviate the feeling of panic through an instant diagnosis. After finishing the diagnostic test, we will redirect the patients to the doctor’s room. The doctor consultation will be chargeable, which will be very inexpensive in comparison to the regular doctor fees due to the advantage of the bulk consultation bookings as compared to traditional hospitals, which can only serve a limited area based on their location. We would monitor the consultation using automated A.I for generating prescriptions and analyzing the doctor consultation in real-time to ensure that the experience was seamless and genuine for both the doctor and patients. Both the doctors and patients will have 24x7 access to their personal dashboard on the website from where they can contact each other, and access the reports, prescriptions, and other features.
Built With
computer-vision
javascript
machine-learning
mongodb
natural-language-processing
Try it out
beta.covidcare.cloud
github.com | CovidCare - A.I powered Digital Hospital and Laboratory | An end-to-end solution for COVID-19 which helps a person from diagnosis to recovery | Millions of A.I powered diagnosis tests in a minute | Accessible using any smartphone or computer | ['Kavish Goel', 'Taruna Garg', 'Stuti Kalra'] | [] | ['computer-vision', 'javascript', 'machine-learning', 'mongodb', 'natural-language-processing'] | 58 |
9,890 | https://devpost.com/software/downward-dog | Landing Page
Homepage
Taking a picture of a yoga pose!
Pose detection and score
Tips for all skill levels!
Inspiration
I, like many others, have began exercising from home more and more due to quarantine, and I have noticed that sometimes when I am unsure how to correctly do an exercise or stretch, I end up in pain the next day after having done an exercise incorrectly. Without a person to spot your mistakes or interactive group fitness classes, where instructors offer insights to correct your form, exercising can lead to unwanted injuries and even chronic pain. I wanted to find a solution that would help correct my form and offer suggestions for improvement.
What it does
Downward Dog is an app that allows you to take a picture of your yoga poses, and the app will automatically identify the pose you are doing and provide a score (out of 100) for your pose. After receiving a score, users can proceed to an informational page for that pose and receive tips and feedback to improve their form and prevent injury based on their score.
How I built it
I built Downward Dog using CreateML and custom trained an image classifier model to identify and score different yoga poses. I incorporated the model in Swift and created an iOS app.
Challenges I ran into
The initial accuracy of my image classifier model was quite low, but after working with augmentation, I was able increase the testing accuracy.
Accomplishments that I'm proud of
Creating a custom image classifier model and incorporating that into an app.
What's next for Downward Dog
I hope to be able to incorporate AR to allow users to view demos of the poses, right in their own homes!
Built With
createml
swift
Try it out
github.com | Downward Dog | Your personalized ML-certified yoga instructor | ['Alice Yeh'] | ['Best Project Deployed with Buddy', '2nd Place', 'Top 5 Teams'] | ['createml', 'swift'] | 59 |
9,890 | https://devpost.com/software/hospit-ai-608itu | This is our logo!
This is part of the data that we used to build this model.
Inspiration: My (Reshma's) mother is a doctor, and she told me about the challenges that hospitals are facing. I wanted to do something about the coronavirus. I (Alice) on the other, was searching for ways to help with the coronavirus crisis and luckily came across this hackathon. I knew that Reshma was big into science and AI so I asked her if we could partner up and create something. We ended up creating Hospit-AI!
What it does: Our model tells hospitals when they will reach their maximum capacity.
How we built it: We used the AutoML Tables API from the Google Cloud Platform in order to build and test our model.
Challenges we ran into: We initially were not sure which angle we wanted to pursue. We wanted to address both the economic and medical impacts of COVID-19. After much thought and discussion, we decided on a project that had elements of both. Later on, we were not sure how to go about this project. A friend recommended the Google Cloud Platform (GCP) to build, optimize, and test our model, so we decided on this. However, it was still challenging to learn how to use this as both of us were completely new to it.
Accomplishments that we're proud of: Initially, we had no idea how to go about this project. We are proud that we were able to learn how to use the GCP and successfully accomplish our project.
What we learned: We learned how to use the GCP and we learned lots about Machine Learning. Most of all, we learned how to work together as a team and had a great time doing so!
What's next for Hospit-AI: We hope to further develop Hospit-AI to reflect the changing circumstances by adding more data. We eventually hope to have it implemented.
Built With
automl
google-cloud
Try it out
console.cloud.google.com | Hospit-AI | We wanted to create a machine learning project that tells hospitals when they will reach their maximum capacity, so they can plan ahead. | ['Reshma Kosaraju', 'Alice Tao'] | [] | ['automl', 'google-cloud'] | 60 |
9,890 | https://devpost.com/software/safeindoors | Login Screen
Indoor Positioning a user
On-device-Activity-Recognizer Service
Inspiration
Global economy's downward trend. Struggling businesses.
How to safely reopen and operate businesses when contact tracing becomes the norm.
What it does
On device human activity recognition based indoor positioning to identify exposed locations within a building.
How I built it
tensorflowlite, Android studio, Google indoor maps
Credits:
https://aqibsaeed.github.io/on-device-activity-recognition
and
http://hdl.handle.net/10211.3/194726
Challenges I ran into
API mockups for contact tracing,
Accomplishments that I'm proud of
The idea, and the code with usable components for building the application completely
What I learned
tensorflowlite and how to create personalized machine learning models for data privacy.
What's next for SafeIndoors
Integrating Google/Apple contact tracing API,
Making test appointments using exposure status and health history,
Partnerships with health insurances,
Clinics nearby feature
Built With
android
google
java
python
rest
tensorflowlite
volley
Try it out
github.com | SafeIndoors | Reopening Economy In the Times of Corona | ['Anu George Enchackal'] | [] | ['android', 'google', 'java', 'python', 'rest', 'tensorflowlite', 'volley'] | 61 |
9,890 | https://devpost.com/software/grocery-a2eg3s | Grocery Login Screen
Delivery Menu
Maps view for all available orders
Inspiration
I've lately picked up baking as a hobby in order to pass the quarantine time. A couple weeks ago, I wanted to make some chocolate muffins, but wasn't able to as I had ran out of eggs. However, I still had enough food around that making a trip to the grocery store wasn't really justifiable, as social distancing was still the priority. I wished there was a way for someone already at the store to simply drop off some eggs at my door as they headed home, in exchange for a small fee for their troubles. That's when I decided to make Grocery.
What it does
Users can make orders, deliver groceries, or do both! If you're missing an item in your fridge or pantry, you can place an order for the item you need. Other users that are on grocery runs will be able to see your request, and can pick up the item and deliver it to your doorstep, in exchange for a small driver's tip. This effectively decentralizes the grocery delivery service and allows community members to help each other out with a financial incentive attached. Grocery reduces the traffic at supermarkets and promotes social distancing measures, allowing fewer total trips to stores to be made.
How I built it
I used the Android Studio IDE with Kotlin to write the main logic of the app, as well as xml for the UI. I connected the app to Firebase, which handled the login authentication and database management back-end of the app.
Challenges I ran into
Connecting the app to Firebase was quite difficult as I've never used the platform before, and fetching data alongside asynchronous processes caused a lot of bugs.
Accomplishments that I'm proud of
Building a complete app with a dynamic back-end was very satisfying, implementing the Google Maps API into the app was also a challenging but gratifying feature.
What I learned
Gained experience in working in the Android app workflow, writing logic with Kotlin, and effective UX design.
What's next for Grocery
Add GPS tracking of the delivery driver while they are en route to the destination so that the user has an idea of when their order will arrive.
Add a secure payment client (PayPal, Google Pay, etc) so that the delivery driver can be reimbursed and paid through the app.
Port to iOS devices so that Apple devices can also use Grocery.
Built With
android
android-studio
firebase
google-maps
kotlin
xml
Try it out
github.com | Grocery | Decentralized goods delivery for the benefit and convenience of everyone! | ['Benji Li'] | [] | ['android', 'android-studio', 'firebase', 'google-maps', 'kotlin', 'xml'] | 62 |
9,890 | https://devpost.com/software/faco-fight-against-corona-jfcza9 | GIF
Confusion matrix for our final model
INSPIRATION
A diagnosis of respiratory disease is one of the most common outcomes of visiting a doctor. Respiratory diseases can be caused by inflammation, bacterial infection or viral infection of the respiratory tract. Diseases caused by inflammation include chronic conditions such as asthma, cystic fibrosis, COVID-19, and chronic obstructive pulmonary disease (COPD). Acute conditions, caused by either bacterial or viral infection, can affect either the upper or lower respiratory tract. Upper respiratory tract infections include common colds while lower respiratory tract infections include diseases such as pneumonia. Other infections include influenza, acute bronchitis, and bronchiolitis. Typically, doctors use stethoscopes to listen to the lungs as the first indication of a respiratory problem. The information available from these sounds is compromised as the sound has to first pass through the chest musculature which muffles high-pitched components of respiratory sounds. In contrast, the lungs are directly connected to the atmosphere during respiratory events such as coughs, heart rate.
PROBLEM STATEMENT
In this difficult time, a lot of people panic if they have signs of any of the symptoms, and they want to visit the doctor.
It isn’t necessary for the patients to always visit the doctor, as they might have a normal fever, cold or other condition that does not require immediate medical care.
The patient who might not have COVID-19 might contract the disease during his visit to the Corona testing booth, or expose others if they are infected.
Most of the diseases related to the respiratory systems can be assessed by the use of a stethoscope, which requires the patient to be physically present with the doctor.
Healthcare access is limited—doctors can only see so many people, and people living in rural areas may have to travel to seek care, potentially exposing others and themselves.
SOLUTION
We provide a point of care diagnostic solutions for tele-health that are easily integrated into existing platforms. We are working on an app to provide instant clinical quality diagnostic tests and management tools directly to consumers and healthcare providers. Our app is based on the premise that cough and breathing sounds carry vital information on the state of the respiratory tract. It is created to diagnose and measure the severity of a wide range of chronic and acute diseases such as corona, pneumonia, asthma, bronchiolitis and chronic obstructive pulmonary disease (COPD) using this insight. These audible sounds, used by our app, contain significantly more information than the sounds picked up by a stethoscope. app approach is automated and removes the need for human interpretation of respiratory sounds, plus user disease can also be detected by measuring heart beat from camera of smartphone.
The application works in the following manner:
User downloads the application from the app store and registers himself/herself.
After creating his/her account, they have to go through a questionnaire describing their symptoms like headache, fever, cough, cold etc.
After the questionnaire, the app records the users’ coughing, speaking, breathing and heart rate in form of video from smartphone.
After recording, the integrated AI system will analyze the sound recording, heart rate comparing it with a large database of respiratory sounds. If it detects any specific pattern inherent to a particular disease in the recording, it will enable the patient to contact a nearby specialist doctor.
The doctor then receives a notification on a counterpart of this app, for doctors. The doctor can view the form, watch the audio recording, and also read the report given by the AI of the application.
The doctor, depending upon the report of the AI, will develop a diagnosis, suggest medicines, or recommend a hospital visit if the person shows symptoms of corona or other serious condition.
In cases where the AI detects a very seriously ill patient, it will also enable the physician to call an ambulance to the users’ location and continuously track the user.
HOW WE ARE GOING TO BUILD IT
We will take a machine learning approach to develop highly-accurate algorithms that diagnose disease from cough and respiratory sounds. Machine learning is an artificial intelligence technique that constructs algorithms with the ability to learn from data. In our approach, signatures that characterize the respiratory tract are extracted from cough and breathing sounds. We start by matching signatures in a large database of sound recordings with known clinical diagnoses. Our machine learning tools then find the optimum combination of these signatures to create an accurate diagnostic test or severity measure (this is called classification). Importantly, we believe these signatures are consistent across the population and not specific to an individual so there is no need for a personalized database Following are the steps the app will take:
Receive an audio signal from the user's phone microphone
Filter the signal so as to improve its quality and remove background noise
Run the signal through an artificial neural network which will decide whether it is an usable breathing or cough signal
Convert the signal into a frequency-based representation (spectrogram)
Run the signal through a conveniently trained artificial neural network that would predict the user's condition and possible illness
Store features of the audio signal when the classification indicates a symptom
IMPACT
FACO will help patients get themselves tested at home, supporting in areas where tests and access to tests are limited. This will help democratize care in hard-to-reach or resource-strapped areas, and provide peace of mind so that patients will not overwhelm already stressed healthcare systems. Doctors will be able to prioritize patients with an urgent need related to their speciality, providing care from the palm of their hand, limiting their exposure and travel time.
CHALLENGES WE RAN INTO
No financial support
Working under quarantine measures
Working in different time-zones
Scarcity of high-quality data sets to train our models with
One Feature Related Problem- Legal shortcomings we might face when adding the tracking patient feature
ACCOMPLISHMENTS
We went from initial concept to a full working prototype. We got a jumpstart on organizational strategy, revenue and business plans—laying the groundwork for building partnerships with healthcare providers and pharmacies. On the creative side, we built our foundational brand and design system, and created over 40 screens to develop a fully working prototype of our digital experience. Our prototype models nearly the entire app experience—from recording respiratory sounds to reporting to managing contact, care, and prescriptions with physicians. Technologically, we successfully developed an algorithm for disease and have begun the application development process—well on our way to making this a fully functional product within the next 20 days.
You can explore the
full prototype here
or
watch the demo
(and
check out our promo gif
)!
WHAT WE'VE DONE SO FAR
We wanted to show that the project is feasible. Scientific literature has shown that audio data can help diagnose respiratory diseases. We provide some references below. However, it is unclear how reliable such a model would be in real situations.
For that reason, we used a publicly available annotated
dataset
of cough samples:
It is a collection of audio files in wav format classified into four different categories.
We wrote code in Python that converts those samples into MEL spectrograms. For the time being we are not using the MEL scale, just the spectrograms. We did several kinds of pre-processing of the signals, including data augmentation, then convert all pre-processed signals, along with their categories into a
databunch
object that can be used for training artificial neural networks created in the fastai library. The signals within the databunch were divided into training and validation sets.
Because the dataset size was reduced, we used
transfer learning
. That is, we used previously trained networks as a starting point, rather than training from scratch. We treated the spectrograms as if it were images and used powerful models pre-trained to classify images from large datasets. In particular, we tried both two variants of
resnet
and two variants of
VGG
differing on their depth (number of hidden layers). This approach implied turning the sprectograms into image-like representations and normalizing them according to the statistics of the original dataset our models were trained on (imagenet). We first changed the head of the networks to one that would classify according to our categories and trained only that part of the net,
freezing
the rest. Later on we
unfroze
the rest of the net and further trained it. We finally compared the different models by the confusion matrices that we obtained from the validation test. We finally settled on a model based on
VGG19
. We exported the model for later use in classifying audio samples through the pre-existing interface of our mobile app.
The results are promising, especially considering the small amount of data that we have available at this moment. We have included an image of the final confusion matrix that shows how our current network can correctly classify all four categories of signal about 50% of the time, far better than the random level of 25%. We conclude that wav files obtained trough a phone mic provide information that can be useful for diagnosing respiratory condition. We are confident that we can vastly improve both the sensitivity and the specificity of our model if we can gain access to larger, more representative datasets.
We provide an image of the final confusion matrix for our model in the gallery.
This is a
repository
that contains the most important pieces of our work, including some code, the confusion matrix image and the exported final model.
SUMMARY
We are developing digital healthcare solutions to assist doctors and empower patients to diagnose and manage diseases. We are creating easy to use, affordable, clinically validated and regulatory cleared diagnostic tools that only require a smartphone. Our solutions are designed to be easily integrated into existing tele-health solutions and we are also working on apps to provide respiratory disease diagnosis and management directly to consumers and healthcare providers.
Feel free to click on our
website
for more information. We developed this website using Javascript, HTML, CSS, Figma, and integrated it with Firebase to manage hosting and our database. Thank you for reading, and don't hesitate to reach out if you have any questions!
REFERENCES
Porter P, Claxton S, Wood J, Peltonen V, Brisbane J, Purdie F, Smith C, Bear N, Abeyratne U,
Diagnosis of Chronic Obstructive Pulmonary Disease (COPD) Exacerbations Using a Smartphone-Based, Cough Centred Algorithm, ERS 2019, October 1, 2019.
Porter P, Abeyratne U, Swarnkar V, Tan J, Ng T, Brisbane JM, Speldewinde D, Choveaux J, Sharan R, Kosasih K and Della, P,
A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centered analytic system for the identification of common respiratory disorders in children,
Respiratory Research 20(81), 2019
Moschovis PP, Sampayo EM, Porter P, Abeyratne U, Doros G, Swarnkar V, Sharan R, Carl JC,
A Cough Analysis Smartphone Application for Diagnosis of Acute Respiratory Illnesses in Children, ATS 2019, May 19, 2019.
Sharan RV, Abeyratne UR, Swarnkar VR, Porter P,
Automatic croup diagnosis using cough sound recognition, IEEE Transactions on Biomedical Engineering 66(2), 2019.
Kosasih K, Abeyratne UR,
Exhaustive mathematical analysis of simple clinical measurements for childhood pneumonia diagnosis, World Journal of Pediatrics 13(5), 2017.
Kosasih K, Abeyratne UR, Swarnkar V, Triasih R,
Wavelet augmented cough analysis for rapid childhood pneumonia diagnosis, IEEE Transactions on Biomedical Engineering 62(4), 2015.
Amrulloh YA, Abeyratne UR, Swarnkar V, Triasih R, Setyati A,
Automatic cough segmentation from non-contact sound recordings in pediatric wards, Biomedical Signal Processing and Control 21, 2015.
Swarnkar V, Abeyratne UR, Chang AB, Amrulloh YA, Setyati A, Triasih R,
Automatic identification of wet and dry cough in pediatric patients with respiratory diseases, Annals Biomedical Engineering 41(5), 2013.
Abeyratne UR, Swarnkar V, Setyati A, Triasih R,
Cough sound analysis can rapidly diagnose childhood pneumonia, Annals Biomedical Engineering 41(11), 2013.
FACO APP VIDEO DEMO
LINK
FACO PRESENTATION
LINK
FACO 1st Pilot Web App
LINK
Built With
android-studio
doubango
fastai
firebase
google-cloud
google-maps
java
machine-learning
mysql
numpy
pandas
python
pytorch
sklearn
sound-monitoring-and-matching-api
spyder
webrtc
Try it out
github.com | FACO: Fight Against Corona | A contactless digital healthcare solution to assist doctors and empower patients to diagnose and manage diseases | ['Archit Suryawanshi', 'Oghenetejiri Agbodoroba', 'Ntongha Ibiang', 'Sahil Singhavi', 'Ruthy Levi', 'Navneet Gupta', 'Mohamed Hany', 'Prachi Sonje', 'GAVAKSHIT VERMA', 'Shraddha Nemane', 'snikita312', 'Gauri Thukral', 'udit agarwal', 'Francisco Tornay', 'Rubén Aguilera García'] | ['1st place', 'The Best Women-Led Team'] | ['android-studio', 'doubango', 'fastai', 'firebase', 'google-cloud', 'google-maps', 'java', 'machine-learning', 'mysql', 'numpy', 'pandas', 'python', 'pytorch', 'sklearn', 'sound-monitoring-and-matching-api', 'spyder', 'webrtc'] | 63 |
9,890 | https://devpost.com/software/heroes-maprq6 | Inspiration
By mid-February with the virus getting closer to Spain we come up with a solution that would be anonymous for the users, easily adoptable by anyone with an smartphone and cheap by using the mobile phones sensors.
By 6th of March we had a MVP working version ready and we wrote to different Spanish administrations to let them know, but got no response.
We have continued developing this solution for some more weeks adding improvements and working on moving it from a prototype to a market-ready version.
In early April we discovered the PEPP-PT and DP-3T initiatives and checked them, we can say our design is very similar to initial one proposed by DP-3T on his white paper. A decentralized design that fully respects privacy and doesn't uploads any data to any cloud.
What it does
It helps stop the spread by making every citizen aware of his risk of being infected with covid19, therefore helping them to take preventive actions, like calling a doctor for a test based on exposure to confirmed cases provided by the app, and thus helping the administrations to do more selective tests and lock-downs.
It works as follows:
It constantly scans nearby devices and stores their anonymous IDs.
Everyday downloads from a server a lists of anonymous IDs that have been confirmed as positive by an authorized medical institution. It searches for matches of any of those anonymous IDs with any of the ones you have on your local encounters database. If anyone found, it computes the encounters startTime and endTime and provides user with the amount of time of exposition to confirmed cases over the last 20 days.
Everyday the encounters from longer than 20 days are removed (the app is data-driven so those values can be modulated based on new clinic evidences).
We have developed a secondary app for the medical authorized users, that allows, only to a pre-autorized personnel, register users as "confirmed with covid-19". That app doesn't take any personal data of the patient, just scans the code and stores it on the server, so that anonymous ID appears on the list of "confirmed cases". During the process of scanning for confirmation, a new anonymous ID is given to the user, so no one can relate him with an active case.
All the numbers are configurable. We expect the public administration and researches to provide us with the correct numbers (like minimum distance for contagion, minimum time to be at risk, quarantine days, messages with indications to display to the users, etc).
How I built it
We are videogames and apps development startup, so far the company has paid some of his workers to develop these two apps (in total has expend around 5k-6k€, not looking to recover it, but is worth mention to show how much implicated we have been on that).
Challenges I ran into
We run into some problems with Bluetooth detection technology, so we have used different approaches.
Getting the app not to be killed and be resilient also took us quite a lot of time.
And testing a tracing app during a lock-down has not been easy either, luckily family and friends has helped a lot.
Administration collaboration: This app can't work/can't be used without the collaboration of the administrations. We need them in order to better understand the medical requirements, to polish the app according with whatever restrictions they need to be in place, to conduct testing groups, to know if they want just one app or they want to have an app that integrates with other public services apps or any other requirements they might have.
Initiatives might come from private sector or individuals, but without public administration collaboration there is nothing we can do.
Accomplishments that I'm proud of
To have had working version ready by 6th of March and having notified it, much earlier than the local administrations figured out the importance of that.
To have created a protocol and an architecture design that is almost the same as the one proposed by many doctors on white papers.
To have been focus on the solution and worked hard on it just because we know something like this is needed, without thinking about his profitability.
What I learned
That reaching the administration is very difficult and even in situations like this one they don't pay attention to you if you are not friend of someone or you write a paper with several "Dr." title on it, your proposal unlikely will be attended.
What's next for Heroes
See if we can get attention from public administrations to conduct tests and check if it's really worth for us to continue investing on that development. We have an app which with few close group tests could be ready to hit the market and help to deescalate the situation. In Spain for instance the economic damage performed by the quarantine is enormous and we have started the deescalate without a fundamental tool, which might lead to new lock-downs and severe economic impact.
The app can run itself or can be easily integrated on other apps. But on top of that we have started working on a super simple SDK with just two calls:
GetUserID;
OnExposureDetected;
So anyone can create their fancy UI/UX or tools, and add up the tracing logic by just implementing those two functions.
TO BE ABE TO DOWNLOAD THE APP FIRST YOU NEED TO JOIN THE PRIVATE TESTING GROUP:
https://groups.google.com/forum/#!forum/heroes-test-group
TO TEST THE APP: USE INVITATION KEY: 01234
Built With
android
bluetooth
firebase
flutter
nearby
Try it out
play.google.com | MyExposure2Covid (Heroes) | Anonymous app to collaboratively stop the spread of the virus. | ['Axel Garcia', 'Jonathan Salazar Sanchez'] | [] | ['android', 'bluetooth', 'firebase', 'flutter', 'nearby'] | 64 |
9,890 | https://devpost.com/software/covi-helper | Add Helping hand
CMS
Add Request
Home Page
Providing the help
Asking for help
Inspiration
During the COVID-19 crisis, we know there is a lot of shortage of items in our household and in our community as well. Furthermore, all grocery shops are closed. Shopping marts are out of stock. But, In our community, some neighbors may have excess food or items that we might need or neighbors have too much to spare.
So, why not share
What it does
It provides the platform for the people to share the extra items with each other. The software allows the user to either request for help and to provide help to others.
How I built it
It is built using the functionality of Mapbox. Since we are quite familiar with google maps so I embedded the maps and the user can directly see where they are and where the nearest person who will be helping us is. Similarly, I used google maps to find the places where the user wants to drop off the items.
Similarly, I created the CMS to manage all the requests in order to filter out suspicious requests.
Challenges I ran into
The hard part was integrating my location to the Mapbox and geocoding the address using the google map API Keys.
Accomplishments that I'm proud of
I was able to add the person's location to the system. and hope that this system will enable people in the community to help each other.
What I learned
Coding is fun and with limited time, you have to choose what to put into the system and where your efforts should go into. It helps to focus on only one matter during the hackathon in which you want really contribute out of all the ideas.
What's next for CoVi Helper
Adding other APIs such as hospital status to show if the nearest hospital is overcrowded or not.
Adding the status that shows the local grocery shop's status.
Show nearest diagnostic center/booth along with taxi/uber service to ride up to center
hospital to hospital relation??
a. COVID Treatment - available/not??
b. Status of ventilator and beds??
Built With
gmap
javascript
mapbox
mongodb
mongoose
Try it out
covi.raktim.com.np | CoVi Helper | Helping your community in the time of need | ['Raktim Shrestha', 'manandhar-ashmita79'] | [] | ['gmap', 'javascript', 'mapbox', 'mongodb', 'mongoose'] | 65 |
9,890 | https://devpost.com/software/operational-remote-identification-of-diseases-covid-19-tfwpqg | Biohazard COVID-19
Decision - enter data: foto, temperature, pressure and etc.
Inspiration
OPERATIONAL REMOTE IDENTIFICATION OF DISEASES
What it does
OPERATIONAL REMOTE IDENTIFICATION OF DISEASES (COVID – 19)
How we built it
IDENTIFICATION OF INFORMATION BY COMPARISON OF STRUCTURED MASSIVES
Challenges we ran into
Rapid remote identification of diseases, collection, processing and analysis of information to ensure timely provision of emergency medical care
Accomplishments that we're proud of
certificate of authorship UA № 91811
What we learned
Patent applications UA а 201908606, u 201908607 Method for analyzing and identifying information
What's next for
Online remote identification of people with fever (COVID – 19)
ANALYSIS AND FORECAST OF THE MARKET
Built With
api
c#
mathlab
python
tensorflow
Try it out
defin.zzz.com.ua | OPERATIONAL REMOTE IDENTIFICATION OF DISEASES (COVID – 19) | OPERATIONAL REMOTE IDENTIFICATION OF DISEASES | ['Kamel Pawelsky', 'Nick Dubovikov'] | [] | ['api', 'c#', 'mathlab', 'python', 'tensorflow'] | 66 |
9,890 | https://devpost.com/software/theplate-io-platehero | (DRAFT)
WHO ARE THEPLATE (Problem/Solution)
I am doing a project for the Community, my goal is to help lower the Depression and anxiety that are "nuclear" hyper-growing among our Society and also solving a problem with Support our nurses and heroes with a P2P food sharing marketplace powered by AI and Gamification to make us all contribute and remind everyone that we are still a community and we stand together! The faithful people who are let go from their jobs and small business owners, faithful and loyal for decades, are now losing faith in Government and the system that we relied on for years!
PlateHero is a Peer-to-Peer Food Sharing-community marketplace to support those looking to make a difference. We aim to provide every citizen with a tool to contribute, earn money and paint a smile on someone in need! Good deeds are contagious! Your choices will encourage your family, friends and others in your workplace or community to follow in your example.
MARKET
Our go2market strategy is one of our strengths! Our vast network will be key to promote and get a bootstrapped go2market campaigns through influencers, food bloggers and as PR, we will do a joint marketing campaign with a well established Drone Delivery Company. We take a 20% commission on each transaction and will focus on neighbours and donations for people in need!
SWOT
We dissected the Foodtech Giants, we took every Weakness and Threat and turned it into Strength and Opportunity. We also as always validate our Problem and not focus in early stage on the solution, we saw a big growing problem that are really in the end stupid, how can people still die in starvation and others worry about feeding their Children? Rise of the Sharing Economy will be KEY!
Vision 2022
IoT
AR/VR
Autonomous Contact-less Delivery
EXPERIENCE & KNOW HOWS
I have done a complete Startup Journey from startup to breakeven in four years, the startup (taxijakt.se) was the biggest challenge och "school" of life, we started as the first Scandinavian e hailing mobility app, and with no sufficient funds and immature market challenged the time and with dedication to our lifestyle as entrepreneurs, we managed to find a solid 80% YoY Growth and clash with the giants such as Uber, MyTaxi (FreeNow) and Bolt (TaxiFy) who entered our Market with deep pockets and vast network and resources. #challenger
I have support of many NGO & famous food bloggers, have been a behaviour analysis and entrepreneur for the last 8 years with a lot of failure that taught me valuable lessons to make this Vision a reality with the right team and support!
FINANCE
I have funded the early stage (bootstrapped $50K in total until 31/5 -20), we are now ready with a Private Beta that we are dissecting and creating go2market strategies. We are raising $120K USD on $600K evaluation until 30/5 2020 the Seed round.
TEAM
For the moment, we are two Brothers and a outsourced development team, we are looking before MVP launch to complete our Superhero team of co founders.
Beidos Ali
I am a experienced Entrepreneur & behavioural Scientist since 2011, I believe that the cup is always half full and have been working as Project manager, KPI analyst, Growth management and scaling tech startups with a data-driven Strategy and have the mind of a Gamer! You will always see a smile on me =)
WE NEED:
A developer to join as co founder to help us launch the MVP week20
A UI/UX Magician
A Lead investor!
Angel Investors (Early Stage or Seed)
Requirements:
That you love and believe in what we do and share our Vision
That you do what you love
Able to quickly adjust and calibrate
Experience is good, but you need to be updated with the latest innovation and trends within your field of expertise
Bring Value and help us do the big impact we aim for!
OUR VALUES
Transparency
Sustainability
Legacy over Greed
Work smarter not harder
Able to quickly adjust
OUR VOICE
PlateHero - Beta
unitedwestand #dividedwefall #sharingiscaring #zerowastegeneration #savemore #wasteless #donate #foodforgood #goodforfood #heroes #webelieve #sharingeconomy #virtualreality #contact-freedelivery #contactless
Our SuperHeroes will soon reach out for activation and the start of a new Era and the birth of the third tech wave! We are creating
Built With
digitalocean
firebase
food-marketplace-clone
google-analytics
google-cloud
google-geocoding
google-maps
hubspot
klarna
paypal
php
sendgrid
tidio
twilio
Try it out
theplate.io
www.linkedin.com | PlateHero - Beta | A P2P Food Sharing community-marketplace to support those looking to make a difference. Our Vision is provide everyone with an opportunity to Contribute & Earn money. Good deeds are contagious! #karma | ['Beidos Ali'] | [] | ['digitalocean', 'firebase', 'food-marketplace-clone', 'google-analytics', 'google-cloud', 'google-geocoding', 'google-maps', 'hubspot', 'klarna', 'paypal', 'php', 'sendgrid', 'tidio', 'twilio'] | 67 |
9,890 | https://devpost.com/software/covid-19_tracker_analysis | structure
COVID-19_tracker_analysis
Functions:
Obtain data from widely-used third-party
APIs
and update the virus data automatically from server API every hour
Add a safety advice module to tell people what to do during this pandemic infection.
Write a footer and about us page to let people who browse this website have a better understanding about our project.
Build a news page using Microsoft Bing News API to let the website show the top news about coronavirus in the US
Write an Email subscriber to let people who want to keep updated with our website can subscribe to our newsletter, using
nodemailer
.
Host this project on Heroku
Tools in use:
Frontend framework: Angular and Bootstrap4
Backend framework: Node.js
Frontend icon: Google Map API and Chart.js
Host Platform: Heroku
Structure:
Getting started
To get the Node server running locally:
Clone this repo
to install all required dependencies
$ npm install
go to backend and run
$ node index.js
To get the frontend running locally:
go to /frontend, then run
Clone the repository.
Run npm install in folder.
Make sure you have npm and angular-cli installed.
After installation is done, run ng serve and open website at
https://localhost:4200
Group members:
Haoyu Guo
[email protected]
Tianyi Xu
[email protected]
License:
MIT
YouTube link：
https://www.youtube.com/watch?v=HiZ4z87VUSY&t=64s
Built With
css
html
javascript
typescript
Try it out
github.com | COVID-19_tracker_analysis | full-stack project about COVID-19 | ['Haoyu Guo', 'njuxty'] | [] | ['css', 'html', 'javascript', 'typescript'] | 68 |
9,890 | https://devpost.com/software/coval-2za3si | Going Back To Business
COVAL is a community of businesses and customers that keeps each other safe by keeping the regulations together
The Unmet Need
Due to the Covid 19 pandemic, people across the globe are staying at home in quarantine, causing economic loss and social disruption. Naturally, this reality is not endurable in the long term. In the near future governmental restriction will gradually decrease, while the amount of infected people will increase. Therefore, keeping track of potentially infected individuals who are obligated in quarantine is of great importance. Navigating between the need for proper economical and cultural life under democratic doctrines, while maintaining discipline, regulations and sense of security among the people is one of the greatest challenges our society will face. Therefore, there’s a great need for a technological solution that will allow citizens and business owners to go back to normal routine in a responsible and controlled manner. In our perspective, this technological solution should include two components:
Regulation infrastructure ensuring the enforcement of quarantine warrants, mainly keeping infected and potentially infected out of crowded places.
Efficient epidemiologic research tool. This tool should allow a very accurate identification of people who were in the same places (supermarkets, restaurants, bars, etc) and at the same time as the confirmed covid-19 carriers, and alarm them.
Our Vision
In order to face this great challenge, a collective responsibility and discipline among the people must be formed. Our vision is to move democratic countries from a governmental based enforcement system, to a social based enforcement system. We believe that this approach will prove as a stronger tool than the orthodox governmental methodologies both in effectiveness and endurance, while preserving the citizens’ privacy.
COVAL
COVAL creates a controlled businesses’ entry mechanism. COVAL prevents covid-19 carriers / people who are obligated in quarantine from entering businesses using the platform. Furthermore, in the case that one of the users gets infected, COVAL updates immediately all the users who were in the same places at the same time with him in an anonymous manner.
Businesses choosing to be part of the COVAL community gain an efficient and simple enforcement mechanism which assures them that all their customers are feeling good and are not obligated in quarentune warrant or are infected, while maintaining minimal friction. The only way to enter a business that is part of the COVAL community is by using the COVAL app. The process of verifying the people entering the business is done by a doorman that verifies each customer through the COVAL app by picture and ID number.
Customers choosing to be part of the COVAL community gain the ability to enter places that are protected by the platform. The user will be able to see all the businesses working with the platform on a map relative to his position. The user can choose a business by clicking it on the map or by directly searching it on the search bar, much like google maps. When selecting a desired business, the user will see all the measures the desired business is using in order to keep customers safe as possible, as seen in the illustration. When arriving at the destination the user will be able to issue an entrance request, in order to enter the business, which in time the host\guard will have to approve. In addition the user will be asked to sign a declaration of good health and integrity, each time when entering a property. The registration to COVAL is done by scanning driver license/ID like in other apps.
Everytime that the user opens the app, the app verifies that the user is not obligated in quarantine by connecting to a designated API of the local Ministry of Health. In case the user is obligated in quarantine by law at that moment, the app is locked and an explanation is shown on the screen.
A log containing the times and places that the user has entered in the last 14 days is saved locally on the device. In the case that the user gets infected, his log will be transferred anonymously to all the users of the platform, so that each user will compare locally between his log and the anonymous log of the new covid-19 carrier - the comparison result will not be transferred to anyone. Then all users who have crossed paths with the new carrier will get an alarm, and a recommendation to report to the MoH.
This way COVAL protects the businesses and the customers while preserving the customers’ privacy. COVAL is a creative, practical and long term endurable solution to get back to normal life and restore the economies of the world.
Incentives
Customers:
reduce significantly the chance of getting infected in public spaces, maintaining a basic lifestyle, receiving a reliable indication in the case they should enter quarantine.
Businesses:
attracting customers by providing a “covid-19 free” environment, reduces the chance of having a covid-19 carrier in their business that could cause damage to their reputation and even a temporary shutdown in some cases.
Base assumptions
Large Scale - Assimilating the product among businesses and customers at deployment will be a great challenge. With the right marketing and cooperation we can incentivize businesses towards using the app.
Ministry of Health API - our product is based on the existence of an API provided by the local MoH, that allows users to check their current status - obligated in quarantine / not obligated in quarantine.
About us
Ori Gil-ad: Bsc in electrical engineering and physics from the Technion. Currently a graduate student in electrical engineering at TAU.
Shay Shimonov: Bsc in electrical engineering and physics. Currently completing a Msc in electrical engineering at the Technion.
Harel Mendelman: Currently undergraduate student in the electrical engineering faculty of the Technion.
Ron Gatenio: Bsc software engineering from the Technion.
Roy Schory: Bsc software engineering from the Technion.
Built With
firebase
flutter
flutter-(user-app)
react
Try it out
github.com | COVAL | Going Back To Business | ['harel123123 .'] | ['3rd Place Overall Winners'] | ['firebase', 'flutter', 'flutter-(user-app)', 'react'] | 69 |
9,890 | https://devpost.com/software/humanner | We live in multiple ecosystem simultaniosly
All in ONE pic
We already know but don't do
Eco-System Thinking for EVERYONE
Multi layered Corporate & Collective Social Responsibility GLOCAL Platform
Public Services in a Multi Layered milieu
Simplified complexity (Federation of the Commons for the public goods)
Future Scenario Planning
We can plan together our better future
The Future = WEB 3.0
Semantic AI
Base of the Collective Intelligence
Manage the Complexity of Microservices
Inspiration
Citizen Social Science in the age of the ALPHA GENERATION
Humanner is a Citizen Social Science IT Project
, aimed at creating a new social networked environment that connects academics directly with communities so that evidence-based research may be shared and acted upon instantly with consistent feedback and contributions from the community.
THE PROBLEM IS NOT THE LACK OF A COLLECTIVE DESIRE FOR A POSITIVE FUTURE BUT THE LACK OF A COLLECTIVE VEHICLE FOR POSITIVE ACTIONS.
It is time to align people and environmental needs through new interconnected collaborative organizational models. Establish the bridge between the virtual and offline world as well as connect academics and communities to focus on social impact by providing the missing valuable functions of the social technology for the common good. We want everyone to be able to share and take joint action on everyday experiences and quality of life concerns; at a local, national and global level.
Collective Holistic Social Innovation Ecosystem Management
helps people prepare for the rapidly changing world of work, inspires veterans and their families join into the champion resilient sustainable communities and eliminate the unfair competitive advantages of the tech giant's traditional business models..
To do this by holistically connect the disconnected and isolated dots with each other and communities of
GLOCAL society
to use technologies and methods to collectively solve problems by holistic approach and
Eco-System Design thinking
to improve the..
Humanity’s relationship to its environment
Humanity’s relationship to technology, and
Humanity’s relationship to itself
.
Before you read anything please watch this video first:
You will much better understand what you will read after -
DOMINO EFFECT - COVID-19 -->> Global economy -->> Social crisis -->> Political crisis -->> ???
Earth's living system -->> Technology -->>Collective Future Planning-->> Why need? <<-->> Collective Mind = WISDOM
.
Humanner's 3 main BUILDING BLOCKS:
1/ Community
- If we do not develop common language - Can't communicate
2/ Multi Layered Social BPM PLANET PLATFORM
- If we do not provide methods and tools - Can't act collectively
3/ Social Business Digital Infrastructure Ecosystem
- If we do not make business differently - Can't help the commons
Let's start
1/ Community = people = folks = Mass
Virtual / Offline Local - like a GLOCAL like a networked PRESTON MODEL
We are truly living in a very unique time in the history of our civilization, facing several simultaneous challenges and converging crises. We now have a choice to make! Either we move into a new phase in the evolution of consciousness and a new era of life on planet Earth, or we will witness the unravelling of the web of life and the immature end of our species and much of the community of life along with us.
University Social Responsibility --> Citizen Social Science
Linking science and technologies to communities of our global society. Humanner social R&D focus on interweaving new models so smoothly and seamlessly into the currently prevailing system, that it will not be perceived as opposition, but rather as a higher quality competitive model, which gradually replaces the old one.
Our method is to create a high-quality example while showcasing our understanding of what it means to be human.
CCSR - Corporate and COLLECTIVE Social Responsibility
We can thank the pandemic for opening the way to a global transformation.
Now the way is open to creating a better world, a world that lives up to the power and the potential of the human spirit.
There has never been a more exciting or more important task in the history of humankind.
2/ Multi Layered Social BPM Semantic PLANET PLATFORM
• Erosion of the Surveillance Capitalism ---> Privacy Revolution <-- Reclaim what's rightfully belongs to us
Humanner's system work with a MULTI FUNCTIONAL holistic multisolving approach so that make the investment more impactful.
Single investment of time and money
- Defined as a way of solving multiple problems with a single investment of time and money, the multisolving approach brings together stakeholders from different sectors and disciplines to tackle public issues in a cost-efficient manner
1/ "Normal" days (GLOCAL) - Collective Social Innovation Network
2/ In Crisis situation can turn into - Collective Crisis Management System
Humanner aimed at improving digital capability in a multi-layered milieu. Envisaged transformative Multi Layered Social Network of Networks by unique SOCIETY's Web 3.0 as a semantic way connected people create a humane and sustainable collaborative environment for individuals, families, communities and all kind of organisation by helping each other in virtual and offline Common Point's Workshops to reach their fullest potential through the power of collaborative problem solving community work.
•.Holistically Multi Layered Data + Semantic + GIS --> Array of Things
•.Mindmap --> Process flow --> BPMN --> UML --> Code flow visualisation
•.CITIZEN DEVELOPMENT - no code / low code - visual programming
•.Social BPM - collective policy development and review system
•.Pilot Project (Networked Preston model)
Humanner SPOSC - Single Point of Service Contact
•.Expertise to help with solutions
•.Frameworks to help deliver those solutions
•.Toolkits to put "flesh" on the frameworks
•A repository of tried and tested methods which work (and experiential case study derived intelligence on things which don't work)
•.A common set of steps, a recipe, a plan... For the delivery of all of these things.
Digital GLOCAL
Collective intelligence
can bring together many diverse ideas and practices.
Yet we sorely lack more concerted support and action to assemble new combinations of tools that can help the world think and act at a pace as well as scale commensurate with the problems we face.
We need an entirely different model of dealing with reality, a new frame of mind, a collective intelligence. This is an ability to come into communion with a group and act as a single unit of intelligence.
For example:
Information is a prerequisite for any effective emergency response.
To get to know why WE NEED A complex SYSTEM than please watch this video first:
You will much better understand what you will read after -
The increasing importance of information has begun the process of interrelated changes of the whole society in today’s world. The rapid development of information and communication technologies occurs also in the problematic of crisis management. Since we live in a dynamic world, it is important to be ready to the changes that life brings, responding and adopting new technologies.
One of them is
crisis management information system.
Information system is a tool for information support as a functional unit that provides collection, processing, preservation and accessibility of information and data. It includes information sources, media, hardware and software, and equipment, technologies and procedures, standards and employees
It is a comprehensive set of management activities and procedures, approaches, views, experiences, methods and measures aimed on analyzing and evaluating security risks, planning, organizing, implementing and controlling the activities used by crisis management authorities to manage specific situations.
Crisis management is an integral part of the management
of the state, organization or other institution that is interested in its development.
Crisis management is also referred to as a tool to ensure sustainable development of society, organization, territory and country which can help us to save life, health and property in the whole world.
LINK: Virtual Collective as Deliberative Living Laboratory
https://www.linkedin.com/pulse/humanner-collective-deliberative-living-laboratory-our-humanner-/
Modelling of possibilities would be really helpful so everyone can sharing what they experience and how they learned in a social network environment kind of open shared distributed data way where various analytic and visual tools allow us to create narratives and explanations with models in different ways that lets us do collaborative sense-making better so facilitate lots of different people with lots of different tools to approaching the problem solving system. .
....
.
....
3/ Social Business Digital Infrastructure Ecosystem
The Base of the Cooperative Social Business Ecosystem -
The Covid-19 pandemic has jeopardized many business – a recent survey showed that 70% of founders feared bankruptcy as a consequence.
While many government initiatives have been announced, it will not be enough to keep the startup scene alive.
A recently published report by Social Enterprise UK, entitled the Hidden Revolution, highlighted that there are around 100,000 social enterprises, contributing £60bn to the UK economy and employing 2m people. According to the report the top five cooperatives in the UK pay more tax than Amazon, Facebook, Apple, eBay and Starbucks combined.
It is the system thinkers, ecosystem builders around world who are now in charge of adapting to the changed circumstances and provide founders, investors and corporates a perspective on how to continue innovating.
Companies today are born global because they are born digital. Which makes this a historic time. We are at the verge of creating a global equivalent of a medieval town square where small sellers and buyers come together to transact. It is a market where anyone can sell to anyone, anywhere, anytime.
While e-commerce enables developing countries to leapfrog to the 21st century’s technology-powered world economy - we are not optimizing this opportunity for startups and develop countries.
To get to know why WE NEED A complex SYSTEM please watch this video first:
You will much better understand what you will read after -
By the Humanner Cooperative Social Business Ecosystem model give financial self-sustainability and emerge the digital infrastructure to connect informal market, unlock the power at the bottom of pyramid, and deliver economic prosperity.
"A charity money has only one life. A Social Business money can be invested over and over again."
(Muhammad Yunnus)
Social business is a cause-driven business. The impact of the business on people or environment, rather the amount of profit made in a given period measures the success of social business.
The AliBaba features one of the most successful business models in the world and one that is very different from the accepted business/consumer model that we have grown familiar with. Humanner will convert this complex E-Industry and E-commerce business into social business model by collaborative Open Source social innovation being establish the first multinacional social IT infrastructure for small businesses.
Humanner unite the two biggest volunteer sectors
the Open Source and the Non Profit sector into a Social Business Innovation Ecosystem. We aim to explore how open source software, open source hardware, digital maker practices and open design can be effectively used by local communities to fabricate their own tools, make sense of their environments and address pressing environmental problems.
LINK: Poverty? ... Wake up and Think up ...
https://www.linkedin.com/pulse/poverty-wake-up-think-humanner-world/
The service gap is clear, undeniable
- the market demand for a solution like this and can also confirm the novelty and lack of direct competition for the idea.
- How I built it
.-5+ years learning flow by research
.-By Micro step by step as can do a single person.
.-Build Social connections.
.-Waiting the right persons to expand the possibilities with them knowledge as a team for the common good.
Please join and help us with your knowledge and experience:
...
- Challenges I ran into
.-Community building as an ex businessman
.-Motivate the members when not the money the most important factor.
.-Breakdown into micro-tasks for volunteers a very complex ecosystem.
.-Tell understandable in short - how connect the dots between - Global economy + Earth's living system + Collective Mind + Technology + Collective Future Planning
.....
- Accomplishments that I'm proud of
Humanner project got only positive feedback from academics with only a small note to improve the articulation of our identity statement.
.- More than 4000+ follower supporter on social media (pages & groups)
.- 360+ Virtual volunteer candidates from all around the world -
https://www.facebook.com/groups/humanner.synergistic.symbiosis/
.- After 5+ years research information collection we are ready to start. See all about Humanner in details (under Continuous Improvement)
.- Short listed in Google Doc: -
https://docs.google.com/document/d/13ejfHOQ7ntSBMrgVOCS5plEji0pi3hru86stS1Wer0Y/edit?usp=sharing
.- Draft Research Wiki: - (size 285 GB - gone offline - COVID eliminated our budget for the dedicated server)
http://almplan.appicentrum.com/dashboard.action
SUB-Startup projects:
.- Under the SONETMARK.com brand name all marketing tools available in house for promotion campaign.
https://app.wisemapping.com/c/maps/1058863/public
.- Any kind of web hosting problem can resolved by our reseller VENTAHOST.com -
.
- What I learned
We have to learn lot of about how to teach
- What's next for Humanner
With our TEAM would like find support for partnership with universities.
.
THE PROBLEM IS NOT THE LACK OF A COLLECTIVE DESIRE FOR A POSITIVE FUTURE BUT THE LACK OF A COLLECTIVE VEHICLE FOR POSITIVE ACTIONS.
.
REFERENCE LIST
R1/ Community
I am a citizen of a new country
https://www.linkedin.com/pulse/i-am-citizen-new-country-humanner-world/
University Social Responsibility
https://www.edx.org/course/introduction-to-university-social-responsibility
CollabDesign -
https://collabdesign.org/en/metodologias/
Problem Driven Iterative Adaptation (PDIA), A DIY Approach to Solving Complex Problems -
https://bsc.cid.harvard.edu/PDIAtoolkit
Cognitive Dissonance - Changing Minds
https://www.linkedin.com/pulse/cognitive-dissonance-changing-minds-humanner-world/
.
R2/ Multi Layered Social BPM Semantic PLANET PLATFORM
Weaving a Decentralized Semantic Web of (Personal) Knowledge
https://www.researchgate.net/publication/334126329_Weaving_a_Decentralized_Semantic_Web_of_Personal_Knowledge
Creating Knowledge out of Interlinked Data -
https://www.slideshare.net/lod2project
Knowledge transfer - knowledge communication & translation -
https://www.researchgate.net/publication/220363358_Knowledge_communication_and_translation-A_knowledge_transfer_model
MiND Technical background of the global brain -
https://devpost.com/software/mind-iw8qvl
Impact of Social Business Process Management -
https://core.ac.uk/download/pdf/77239761.pdf
Business Model Canvas Facebook -
https://innovationtactics.com/business-model-canvas-facebook/
R3/ Social Business Digital Infrastructure Ecosystem
Alibaba business model canvas -
https://vizologi.com/business-strategy-canvas/alibaba-business-model-canvas/
Business Model Canvas of Alibaba -
https://www.slideshare.net/Siddhanthdoniv/business-model-canvas-of-alibaba-191408041
Building entrepreneurial ecosystems in peripheral places
https://www.researchgate.net/publication/335458367_Challenges_of_building_entrepreneurial_ecosystems_in_peripheral_places
Entrepreneurial Ecosystem Elements -
https://www.researchgate.net/publication/339602397_Entrepreneurial_Ecosystem_Elements
Social Data Economy - Array of Things -
https://arrayofthings.github.io/
Built With
api
distributed
html5
javascript
knowledgegrahp
microservice
node.js
php
react
semantic
Try it out
www.humanner.world
www.facebook.com
www.linkedin.com
twitter.com
www.facebook.com
sonetmark.com
ventahost.com
github.com
core.ac.uk
bsc.cid.harvard.edu | Humanner | Solving Complex Problems Together | ['Elena Lopez-Gunn', 'Gudrun Dara Müller', 'Janos Deak', 'AKSHITA GUPTA', 'Puneet Purohit', 'Dmitri Zaitsev', 'Katerina Zourou', 'Michael Keating', 'shubham bhatnagar', 'Shalom Nyende', 'Isidora Beatriz González Ríos', 'River and Lake Toys', 'Mahak Rathi', 'prabitha urwyler', 'Simardeep Singh', 'Lena Khenriksen', 'Srujana Munamala', 'Yenegh Badimayalew', 'Saffat Aziz', 'Donatas Starkus', 'joehay-yorkshire', 'Mohamed Farah', 'Simone de Wijn', 'Muhammad Hanif Fauzan', 'Panos Karas'] | ['1st Choice'] | ['api', 'distributed', 'html5', 'javascript', 'knowledgegrahp', 'microservice', 'node.js', 'php', 'react', 'semantic'] | 70 |
9,890 | https://devpost.com/software/covid-heal | Home page
Information on how to stay healthy
Face Touch Reminder with Artificial Intelligence
Live news
Remedies, memes, quotes, and videos
Inspiration
As a result of the current situation, the COVID-19 pandemic, our fellow family and friends are in a state of urgency. As of right now, the problem is that we have not been able to find a cure for COVID-19. With lockdowns happening everywhere, people are staying at home, quarantined, we realized that the world is in a dire situation and requires a lot of help. So, we (three high school students) decided to help out by creating a web app.
What It Does
COVID-HEAL encompasses many different and helpful functionalities:
•Built-In Artificial Intelligence that will detect when you are touching your face when on your computer and then notify you. This functionality works even in the background and is accurate at catching when you are picking your nose.
•The latest news about the COVID-19 virus based on your location.
•A live meter of the number of cases, deaths, and recoveries.
•A relax page that takes away the "corona-anxiety" that everyone is getting. This page includes light-hearted jokes and memes, special music that is proven to heal disease, and motivational quotes. On top of that, this page provides helpful tips and strategies credited by nurses and doctors to help you stay healthy at home.
How We Built It
This app is built using many languages and frameworks. The frontend is built with HTML, CSS, JQuery, and Bootstrap and the backend is built using Node.js. The frameworks and APIs we used were Tensorflow, Smartable, Bootstrap, Express.js and Forismatic. These languages & frameworks allowed us to make the web app responsive, use accurate data on COVID-19, and effectively use Artificial Intelligence.
Since we couldn’t meet each other, we planned through voice chats & voice calls. For the version control system, we used Git, and we deployed the web app using Heroku.
We decided to divide and conquer when building the app in order to use our time efficiently. Labdhi worked on a majority of the frontend, which involved designing the web app. Ashay worked on the backend and Artificial Intelligence feature, which involved implementing the Machine Learning models. Sohum worked with the APIs that gave us data on news, statistics, and memes.
Challenges We Ran Into
We found that it was a bit difficult planning & coding when we weren’t able to see each other in person, but we became flexible and learned to use Google Docs, voice calls, and Git.
Often, we came into issues when we weren’t able to merge/combine our code without conflicts. We solved this problem by using Git and using an iterative process when combining our code.
Accomplishments That We Are Proud Of
Within 4 days, we accomplished many coding feats and incorporated unique features. We were able to use machine learning models that could effectively detect when someone touches their face. We were able to pull data from numerous sources using APIs and display this information. Most importantly, we were able to deploy the web app, obtain a custom domain name for the app, covidheal.org, and spread awareness.
What We Learned
Throughout the 4 days we worked on COVID-HEAL, we had the opportunity to learn a lot. Coding-wise, we sharpened our knowledge on the application development process, from brain-storming to deploying. We learned how to effectively use Git to develop a web app. We also learned how to deploy the app on Heroku and add a custom domain name to the app.
Along with that, we learned how to use our coding skills to help out and spread awareness. We got to ask people what they are really in need of to fight against COVID-19. With the current situation, no one holds an antidote to save COVID-19 patients and that has caused a lot of unnecessary fear. We made it our goal to take this fear away, and provide a sense of positiveness and security to people around the world. This is a new and unique idea different from the existing media already in place because it gives a positive vibe rather than a cynical one.
What's Next For COVID-HEAL
Even though our web app is already up and running, there are several future steps we have in mind. We hope to make this web app more specific & interactive in future versions. The website can be made more specific to the user by giving more local news such as events occurring in the users hometown that are related to the coronavirus. More information about clean practices that limit the spread of the virus for people in quarantine would be beneficial as well as more references to numbers that can be called for further questions about the virus. Also, we believe that adding more at-home precautions and treatments would spark a lot of interest.
Built With
bootstrap
css3
html5
javascript
node.js
opencv
smartable
tensorflow
Try it out
covidheal.org | COVID-HEAL | An all-in-one tool, COVID-HEAL reminds you every time you touch your face, will keep you updated to the latest news based on your location, and provide helpful tips and strategies to avoid COVID-19. | ['Labdhi Jain', 'Sohum Bhole', 'Ashay Parikh'] | ['Challenge Winner'] | ['bootstrap', 'css3', 'html5', 'javascript', 'node.js', 'opencv', 'smartable', 'tensorflow'] | 71 |
9,890 | https://devpost.com/software/immunolynk | Cover
Cloud Infrastructure
Healthcare Administrator Interface - Dashboard View
Healthcare Administrator Interface - Employee Tracking View
Mobile Application - Sign Up Page
Mobile Application - Risk Assessment Questionnaire
Mobile Application - Symptom Survey
Mobile Application - Test Scanning Screen
Mobile Application - Calculation Splash Screen
Mobile Application - Sample "True Immunity" Score Screen
About
Antibody tests are the key -- but only with intelligent guidance.
Rapid antibody tests have the potential to
empower frontline healthcare workers
. With a simple finger-prick, they have the power to
identify immunity in just 15 minutes
. And combined with wide-scale deployment, they have the ability to deliver
crucial insights
to hospitals and governments in the battle against the pandemic.
But without the proper tools, this potential will remain untapped. We must overcome three key barriers to harness the power of immunity testing:
Uncertainty
- Hospitals must be strategic in their test deployment. Timing is critical -- if the test is performed too soon after exposure to the virus,
the results can be completely inaccurate
. And even with perfect timing, lateral flow immunoassays have a PPV of as low as 55%— a dangerous shortcoming if not augmented with data on individual exposure risk.
Disorganisation
- Health care workers will need to be retested at regular intervals. This is
essential to combat the scientific uncertainty surrounding sustained immunity
and the variable accuracy of current tests. An easy-to-implement, easy-to-use, and interoperable platform is vital for tracking this unprecedented volume of immunity tests.
Privacy Concerns
- Healthcare workers need to feel confident that their data is secure. Immunity results must not only be FDA and HIPAA-compliant, but stored by an entity that users can trust won’t use it for the wrong reasons — now and forever.
Introducing ImmunoLynk
Effortless + Trustless = Fearless.
Healthcare workers use our mobile app to take a photo of their immunity test results. Our image classification algorithm, built on Keras over a ResNet50 model, automatically reads the test result as accurately as a physician, removing any possible user interference. The corresponding photo, test result and metadata are stored securely on a corresponding IPFS Blockchain node, guaranteeing the test validity and immutability.
The virus can adapt—so can we.
Healthcare workers fill out a brief, but thorough, questionnaire about their potential exposure risk factors. Combined with daily symptom surveys and hyper-local prevalence data, our proprietary machine-learning algorithm determines the ideal time to administer an antibody test. The result is fed into a Bayesian regression model, resulting in a single, easy-to-understand “True Immunity” score. This multimodal data integration overcomes the inherent sensitivity and specificity limitations of immunoassays, creating a tailored diagnostic test capable of accurately conveying the amount of uncertainty. Best of all, all logs are immutable, distributed, and instantaneous — so healthcare workers can worry less about the privacy of our data, and more about conquering the crisis.
We scale with the pandemic.
As our network of healthcare workers and the number of completed antibody tests grow, so does the strength of our algorithms. Our blockchain operating expenses cap at the ultra-low-cost of $25/month per healthcare facility, regardless of the number of employees or tests. IPFS nodes can be deployed and run completely on cloud elastic containers, presenting no additional data transfer, data storage, or uptime cost fees. Every machine learning evaluation is processed through an optimized gateway server so requests are readily processed and delivered to the Blockchain.
Connecting the world so we can reconnect.
Widespread adoption of the ImmunoLynk platform can effectively construct a distributed worldwide research network, creating the largest-ever study on the time-course of COVID-19 and its respective antibodies. This could provide the key data necessary to intelligently lift quarantine restrictions alongside community immunity testing, as well as prepare for future pandemics.
Caring for healthcare.
Priority allocation of immunity tests to healthcare workers aligns with European Centre for Disease Prevention and Control (ECDC) and Centers for Disease Control (CDC) recommendations, and with ImmunoLynk's help, it can put health providers' minds at ease. They can care for patients, perform procedures, and return home to their loved ones with less worry about contracting or communicating the virus. Our U.S. Equal Employment Opportunity Commission (EEOC) and American Disabilities Act (ADA)-compliant dashboard software for administrators also permits easy-to-implement tracking of employee symptoms and immunity tests, allowing them to strategically tackle staffing shortages in the wake of looming surges.
What we have done over the past few weeks
By integrating two initially discrete teams based around the globe and collaborating across diverse areas of expertise, we developed a feasible solution for the problems presented above in the context of a realistic business plan. These teams initially connected as participants in the
MIT COVID-19 Hackathon
, where one was announced a
winner of the challenge
. This joint team was subsequently named the
first prize winner
in the
Lumiata COVID-19 Global AI Hackathon
.
So far, we have created a working prototype of (1) a mobile app dashboard connected to a gateway server that (2) leverages machine learning to (3) combine questionnaire answers, location data and antibody test results to provide users with a "True Immunity" score registered to IPFS Blockchain.
(1) - The mobile app uses Expo.io API with React components to interact with users. It consists of a login and registration page, a symptom and risk questionnaire, and finally a test scanning section. This
interface is currently functional
. It takes a photo of the test placed on a QR barcode, sends it to a Gateway server, and displays the processed test result with an access link to the blockchain data.
(2) - The gateway server was built using the
lightweight Flask framework
with an exposed REST API, which receives both images and data from two distinct endpoints. Images are then submitted through the Keras model and, ultimately, uploaded along with the result and metadata to the
IPFS Blockchain node through Infura's API
.
(3) - Our image processing pipeline was built with Tensorflow, Keras and OpenCV2 on the ResNet-50 architectural network. It was extensively optimized to achieve an
accuracy rate of 91.8% for reading & recognizing the test result (a rate either very near to or better than the human baseline)
for feeding forward to the Bayesian Hierarchical Model.
We have also partnered with healthcare providers to gain access to anonymized symptom and exposure risk questionnaire data linked to antibody test results. This allows us to now predict infection with some degree of certainty purely from questionnaire data.
Our solution’s impact on the crisis
Accelerates the antibody testing process.
The manufacturing capacity of inexpensive and accurate lateral flow immunoassays is increasing exponentially, shifting the bottleneck from test availability to test administration rate. ImmunoLynk enables decentralized testing, alleviating the burden on hospital departments and research facilities. Our effortless data collection platform is woven into a privacy-focused decentralized storage solution, the nature of which was
recently promoted by hundreds of scientists
and
endorsed by Apple and Google
. This data is impossible to alter, hack, or forge by any bad actors.
Helps answer the question: Do antibodies provide immunity?
Studies of the effects of direct exposure of people who have previously caught COVID-19
are being planned
, but are fraught with ethical concerns and high associated costs. By collecting data on healthcare workers, one of the highest risk populations, ImmunoLynk can assess whether there is a substantial reinfection rate and if higher levels of antibodies can reduce that rate. This is a key question that needs to be addressed prior to the implementation of any wide-scale immunity-based solutions.
Inform the immunity passport debate and provide solutions.
As we assess the protective effect of antibodies against reinfection, we can provide much-needed information on whether immunity passports would have a significant societal benefit. Our decentralized storage of information would also make passport data safe and immutable.
The value of our solution after the crisis
Our systemized data from tracking antibody levels & predicting the level of immunity will remain important for possible
future waves of COVID-19 over the coming months to years
. It will also help to determine if antibody count actually confers immunity and, if so, to what extent. Even as large scale vaccinations are rolled out, our solution could easily be deployed for individuals to assess their immune response.
ImmunoLynk is just as applicable to other pandemics
for which lateral flow immunoassays are widely used, such as malaria, hepatitis, and dengue. ImmunoLynk can also help
aid diagnosis of an active illness
if adapted to use antigen immunoassays. Both could even be incorporated into a single test.
At the US level, our use of decentralized technology is in line with the nature of the
The Cures Act
, which "aims to empower Americans with their health data, delivered conveniently to computers, cell phones, and mobile applications". We are actively pursuing integration with the standardized API denoted in The Cures Act. Moreover, it allows a platform for managers to silo their employee's healthcare information,
a requirement of the ADA
. This easy-to-deploy management of healthcare information is
massively scalable to outside industries
, such as nursing homes, factories, travel & hospitality, and so much more.
At the EU level, our use of decentralized technology will be instrumental in advancing the
EU’s health data sharing initiative.
The European Commission has previously recommended that, in order to make further progress in interoperability, developments in digital technologies such as artificial intelligence, high-performance computing, decentralized technologies, and cybersecurity solutions should be carried out, noting that it would
increase trust and general feelings of accountability in government
.
Globally, our approach of tapping into a constant stream of data will
greatly benefit research specifically linked to antibody testing (e.g. virology) and, more generally, immunity.
Considering healthcare workers experience the most regular exposure to COVID-19, they are arguably the best individuals for identifying correlations & trends in the study of immunity. Epidemiologists could also use our data for pandemic modeling (SIR & SEIR), allowing
better preparation for future pandemics
. Lastly, it could
propel future healthcare adoption of blockchain technology
— possibly dispelling the popular notion that it is only useful in cryptocurrency, financial technology, & supply chain management.
What's next?
We need more images of lateral flow immunoassays
to improve the accuracy of our image classification algorithm. We already have thousands of images of lateral flow immunoassays, but more images of positive results and of assays from different manufacturers will help to ensure maximum reliability of our ResNet-50-based CNN classifier.
We need more questionnaire answers
linked to immunity test results to improve our prediction algorithm. We are in discussions with UC Berkeley (The University of California, Berkeley) and UCSF (The University of California, San Francisco) to utilize our platform in their large scale studies to further improve our prediction algorithm. We have also been collaborating with
COV-CLEAR
for the integration of our platform into their clinical trials in the UK.
We need introductions to hospital administrators and government officials
. Our market research demonstrates considerable widespread interest in a platform like ours that streamlines and improves the testing process; we just need the opportunity to prove that our system works and iron out any minor problems. We are currently working with Lumiata to introduce our platform to one of their healthcare partners.
Built With
amazon-web-services
blockchain
expo.io
flask
infura
ipfs
javascript
keras
opencv
python
react
react-native
resnet
tensorflow
Try it out
immunolynk.com
github.com
github.com
github.com
github.com
immunolynk.herokuapp.com | ImmunoLynk | Immunity testing meets AI + Blockchain. | ['Anurag Koyyada', 'Veeresh Shringari', 'Niamh Higgins | ImmunoLynk', 'Kaue Cano', 'Ruan Comelli', 'Matheus Tosta', 'Dmytro Ustianenko, PhD', 'Khush Tawar', 'Shivam Vedak', 'Excellence Ilesanmi', 'Tai Wu', 'Claudio Vilas Boas', 'Claire Louise Donnat', 'Frederick De St Pierre Bunbury'] | ['2nd place', 'First Prize, $10,000 and idea productionalized', 'Global SOS Finalist - Everything Remote Track'] | ['amazon-web-services', 'blockchain', 'expo.io', 'flask', 'infura', 'ipfs', 'javascript', 'keras', 'opencv', 'python', 'react', 'react-native', 'resnet', 'tensorflow'] | 72 |
9,890 | https://devpost.com/software/caretilt-fp7hys | Caretilt is fully functional on most mobile devices.
Caretilt: Encouraging communities to support each other every step of the way.
Inspiration
Third-world countries that are going to be impacted by CoViD-19 will likely suffer the most, I envisioned a listing platform that was quick to use and could connect people to services and supplies they need immediately, I began creating a structure for how Caretilt will work and I immediately got to work on creating it, I am launching in the U.S. and will branch out to other countries later down the line, I intend on making Caretilt easy to use and access--in order to get to the hard to reach places, I'll begin seeking volunteers and begin targeting areas where supplies are in disarray, Caretilt to will cater those countries and adding new languages will require a team and I'm the only one working on Caretilt at the moment.
How it works
Caretilt is a free to use public listing platform, users can immediately begin submitting ads donating their services and supplies or requesting supplies and services, the goal is to re-organize the distribution of supplies and services on the smallest scale and work up from there and prevent potential future cases of CoViD-19.
Caretilt is also fully-functional on a mobile device, the initial plan to create a mobile app alongside this submission was scraped and will be worked on in July. There's still work to be done and launch was early, but this Hackathon ends soon and I've been eager to participate in CovLab.
How I built it
I built the site using Wordpress, JS and PHP. I installed a theme and began editing and coding the modification requirements to meet a public listing platform.
Challenges I ran into
Ensuring a safe and reliable platform is key to the growth of Caretilt, I'm working on setting up a rating system so users can leave reviews on other profiles to ensure that the community takes part in some moderation.
I intend on adding more layers of verification, but right now I would like to gather more users in order to collect data to further improve Caretilt.
There is still much that needs to be done.
Accomplishments that I'm proud of
Prior to launching I shared my website with an Open Source Medical Supplies Team and was invited to register Caretilt on their roster and someone has already begun donating face shields on Caretilt.
What I learned
Organizing supplies and services on any scale is heavy work and I can't do this on my own, I've learned that the problem at hand is larger than I initially thought and making an impact requires a collective effort.
Amassing support from the PPE Creators
Many volunteers across around the world are contributing to the CoViD-19 relief efforts by mass printing personal protective equipment mainly for medical personnel, the ongoing efforts are impressive with over
3.9M+ items produced and counting
. The objective is to make the process of distribution quick and easy to anyone that needs these supplies, from household families that require PPE on a daily basis to workers on the front-line.
What's next for Caretilt
By mid-summer time I should have a mobile app released to accompany the website, but before then I'll continue improving Caretilt and begin seeking a team to collaborate with. I'll also begin reaching out to open source communities on Facebook as well as forming a marketing campaign to reach those who seek to donate and those who need supplies.
I'm also researching free meal distribution, I'd like to incorporate options for families and any hungry person to be able to find free meals in their area, right now food banks are operating on the knives edge of their limits due to the strained food processing facilities across the US, I believe no one should go hungry especially during this crisis.
Also making Caretilt a multi-language and international platform is one of the main objectives here, it's important to provide an open source community orientated platform for third-world countries where supplies aren't organized properly, this will hopefully be done in collaboration with larger corporations down the line, but before then it is up to the communities and individuals in those areas to be know what Caretilt is and how to use it to their full advantage.
Built With
css
html
javascript
php
sql
wordpress
Try it out
www.caretilt.com | Caretilt - Find Supplies Near You | Caretilt is a public listing platform that allows anyone to donate supplies or services and anyone can request supplies and/or services. | ['Suleiman Adam Belrhiti'] | [] | ['css', 'html', 'javascript', 'php', 'sql', 'wordpress'] | 73 |
9,890 | https://devpost.com/software/mindyourmind | Inspiration
To support University students.
What it does
The biggest contributor to mental illness is lack of emotional & social support. Many students suffer from loneliness and isolation. This App proposes peer to peer inclusion and social support for college and University students during this remote learning due to COVID 19 and beyond.
On the App, students globally can connect with their peers. Consult academically as well as chat anonymously with each other. This proposed solution mitigate against vices associated with University and college lifestyle. It will enable students find companionship, encouragement, help with projects, other academic learning as well as providing a place to share youth and adolescence challenges.
The app ensures data protection and encryption policies are adhered to, in addition to the anonymous chat option using avatar which encourages confidentiality.
Looking forward to full development of the App.
Built With
api | Comrade In Pocket | An App to connect University and college students with peers during the COVID 19 crisis and beyond. | [] | [] | ['api'] | 74 |
9,890 | https://devpost.com/software/intellihearts | COPD machine learning
COPD machine learning
Atrial fibrillation machine learning
COPD machine learning
Respiratory diseases test screenshot
IntelliHearts App screenshot with cardiac analysis response
Cardiac anaysis with heartbeats classification
Inspiration
Starting from the WHO document about needs, requirements and challenge we decide to develop our solution starting from this need:
"Optimize current delivery platforms and develop alternative delivery
platforms for essential health services
: develop remote work solutions,
boost home hospitalization programmes
and rapidly scale up existing e-Health strategies"
- WHO -
Problems:
we observed 3 main problems:
detect patients at risk
screening the whole population
monitor them
People with chronic pulmonary diseases have a higher risk of being infected by covid-19 and have a more negative trend. It is crucial to make a quick diagnosis and monitor these patients.
What it does
With our team competence we create:
Real-time analysis & AI elaboration
of
heart, respiratory diseases
and vital parameters (Single-derivative-ekg, Oxygen saturation, Heart rate, Respiratory rate, Blood pressure).
For the vital parameters we did a long search about the best device for us, we don’t provide hardware
WE USE EXISTING HARDWARE
, reducing the timing for ideation, prototype and create the chain for production. The real improvement is our knowledge on Software and machine learning
MAKING ABLE EVERY COMPATIBLE DEVICE TO WORK WITH OUR SOFTWARE
and scalable up to million of users right in a day.
Pre-triage online, home remote monitoring
, thanks to a test that evaluates symptoms, vital data, risks factor and processes them through a score to classify patients based on their stable, unstable or critical condition
Sharing and visualization
of scores, parameters and AI elaboration with physician and Hospitals.
The anonymous database is very flexible and make use of modern no-sql technologies for securely storing personal data anonymously and share it with physicians and integrate it with hospital and all third parties. Our technology is really flexible and easy to manage letting us able in few days to satisfy every request comes from this kind of organization.
How we built it
Scientifically proven Machine Learning and AI algorithms
:
Here we attach the 2017 position paper.
Essentially the technology used is that presented in that paper. From a technical point of view, the deep learning neural network used in that work was replaced with the
newer ResNet-152 giving a leap of accuracy of 93% in F1-Weighted score for arrhythmias classification and 95% F1-Weighted in atrial fibrillations
(not yet described in that paper).
With IntelliHearts we mainly use sensors related to ppg and ECG, and we are able to detect:
Bradycardia, tachycardia, fusion beats and ventricular and supraventricular ectopic beats.
In the experiment that was conducted we first acquired the signal and cleaned it with statistical techniques (e.g. wavelet) then inter-patient separation was made. Subsequently every single beat is plotted and shown in 2d centered in the peak R.
Consequently we have n beats that can fall into the categories: normal, ectopic, supraventricular and fusion. There are not always the same number of beats within the same class, for this reason we refer to the F1 microscore average.
This is the basis of the paper. From this moment on, the classifier has been changed, as each plotted beat is analyzed by a classifier, which in the paper was a Convnet, but which we have changed and used a ResNet 152 layers. Reaching from 90% of the ConvNet proposed in the paper to
93% of the current ResNet
.
Respiratory diseases detection was conducted in the following way:
We used the data from Paper: Α Respiratory Sound Database for the Development of Automated Classification
Filter noise and remove background sounds
We used several segmentation techniques based on spectrograms of respiratory audio
We used a custom deep learning neural network to predict binary healthy/unhealthy patients using only audio recorded by smartphone
Developed a web service and web page/app to record and show the result.
We use medical algorithms
already used in the wards of infectious diseases or pre-surgery to investigate the general state of the patient, taking into account oxygen saturation, temperature, heart rate, respiratory rate, neurological condition. Thanks to the test we investigate the symptoms, the risk pathologies and we can give indications to the patient about what to do
Shared databases
that could be used by physician for evaluation, motorize and have history of the patient.
Challenges we ran into
VAE/GAN needs lots of data:
the majority of reviewed papers use 70-30 split without using the official test set provided by the challenge. Thus they don't use a inter-patient separation scheme, revealing wrong results.
Find valid datasets, identify the right devices to work with, competition and hackathon dismissed for covid-19.
What we have done during the weekend
During the weekend we have developed the respiratory disease detection with deep learning algorithm, we develop a demo web app and created a temporary web service for breath audio analysis.
We hit the 95% of accuracy to dectect respiratory diseases.
You can test it at
https://159.203.68.29:5000/
do it from your smartphone and follow the instruction on website.
Accomplishments that we're proud
The segmentation works!! For the classification at moment I have 95% accuracy in binary classification (healthy vs pathology) with interpatient separation scheme testing on the official test set provided originally from the paper.
Accomplishments that we're proud in past
Be part and take the graduation for Y-Combinator startup school 2020 (SUS2020) in March with this team;
Exceed the state of the art, percentage of accuracy for COPD and heartbeat classification;
Find the right compatible devices and start to building partnership with hardware supplies and Hospitals
What we learned
Probably managing Audio deep learning as a 1D time series, instead of transforming it in images (as done in the majority of the research reviewed) is effective, but requires a specialized network architecture.
That our team has the skills to turn the emergency around and is fun do hackatlon!
The solution’s impact to the crisis
Doctors will already have all the triage data in their database, this will help giving a quick glance to all their patients’ overall being and evaluate in no time whether to suggest them an in-hospital consultation.
Thanks to our efforts into looking for subjects at risk, we can prevent and propound the doctor who to mostly keep monitored.
We would have a much better healthcare system organization, a newer and quicker doctor-patient online relationship, and more focus on prevention to avoid the aggravation of clinical conditions that could have been treated immediately, beforehand.
A further positive impact falls on patients not affected by covid-19, but with other pathologies. With this type of organization, even non-covid patients will have the right care and attention from doctors.
The Mews score allows to evaluate vital parameters, discriminating stable, unstable or critical clinical conditions and is applicable to a wide spectrum of pathologies.
In conclusion, thanks to IntelliHearts even those suffering from other diseases can constantly monitor their parameters from home and share them with their doctor, allowing a more efficient management of all patients.
The necessities in order to continue the project
Imaging to the have high numbers of visitors on the tests of respiratory disease and Online triage we could need more and stronger servers.
So we need to get funded to pay servers, services, device’s certification and software medical certification, hire additional engineers and physicians. Support medical trials.
The value of our solution after the crisis
According to WHO estimates, 251 million people in the world have chronic obstructive pulmonary disease (COPD) and cardiovascular diseases (CVDs) are the number 1 cause of death globally, taking an estimated 17.9 million lives each year.
Thanks to IntelliHearts we will help patients with chronic obstructive pulmonary, cardiovascular, infectious and Metabolic diseases.
The project was born before the crisis for automatic diagnosis with a smartwatch and AI enabled app. Thus, our solution is ready for the post-crisis era, where people will pay more attention to their health and people understood the value of medical doctors, nurses and health operators, thus will reduce the accesses to family doctors (primary care physician) and hospitals by continue using new technologies (which they were forced to use during the lockdown) and managing everything remotely with remote health platforms.
We estimate that our technology can reduce by 40% hospital costs, playing an important role in prevention more than treat them and following the vision of current medicine.
MEDICAL DISCUSSION
Cardiac injury is a common condition among patients with COVID-19, and it is associated with a higher risk of in-hospital mortality.The findings presented in the research on 416 patients in Wuhan, Shi and colleagues [2], highlighted the need to consider cardiac complication in COVID-19 management. The patients with Covid-19 have respiratory distress and low blood oxygen levels, consequently they have high risk of ischemia or heart attack that compromises myocardial contractility and this situation can cause severe arrhythmia.
Respiratory diseases detection was conducted in the following way: We used the data from paper [1] Filter noise and remove background sounds We used several segmentation techniques based on spectrograms of respiratory audio Segmentation is necessary to understand when the first respiratory cycle starts. We used a custom deep learning neural network to predict binary healthy/unhealthy patients using only audio recorded by smartphone Developed a web service and web page to record and show the result. The accuracy is tested on the official test set in [1]
Why is early detection of COPD important for covid19?
SARS-CoV-2 uses the angiotensin converting enzyme II (ACE-2) as the cellular entry receptor to infect the lower respiratory tract. ACE-2 expression in lower airways is increased in patients with COPD and with current smoking [3] . ACE-2 is expressed in a variety of different tissues including both the upper and lower respiratory tract and myocardium. Importantly, nearly all deaths have occurred in those with significant underlying chronic diseases including COPD, and cardiovascular diseases These findings highlight the importance of increased surveillance of these risk subgroups for prevention and rapid diagnosis of this potentially deadly disease.
References
1- Rocha, B. M., Filos, D., Mendes, L., Vogiatzis, I., Perantoni, E., Kaimakamis, E., ... & Paiva, R. P. (2018). Α respiratory sound database for the development of automated classification. In Precision Medicine Powered by pHealth and Connected Health (pp. 33-37). Springer, Singapore.
2- Shi S, Qin M, Shen B, et al. Association of Cardiac Injury With Mortality in Hospitalized Patients With COVID-19 in Wuhan, China. JAMA Cardiol. Published online March 25, 2020. doi:10.1001/jamacardio.2020.0950
3- ACE-2 Expression in the Small Airway Epithelia of Smokers and COPD Patients: Implications for COVID-19. Janice M Leung, Chen Xi Yang, Anthony Tam, Tawimas Shaipanich, Tillie L Hackett, Gurpreet K Singhera, Delbert R Dorscheid, Don D Sin. Published in European Respiratory Journal doi: 10.1183/13993003.00688-2020
4 - Halpin, D. M., Faner, R., Sibila, O., Badia, J. R., & Agusti, A. (2020). Do chronic respiratory diseases or their treatment affect the risk of SARS-CoV-2 infection?. The Lancet Respiratory Medicine.
What's next for IntelliHearts
05/2020: Relase the platform letting people be able to use Machine learning detection and medical scores.
06/2020: Get the necessary medical certification.
07/2020: Involve hospitals for trials.
08/2020: find partnerships and investors.
Our Links:
Presentation link:
IntelliHearts presentation for EUvsVirus 2020
Respiratory disease test:
https://159.203.68.29:5000/
Video of Cardiac Disease test
Our AI Application that work with one of the compatible Smartwatch, running a cardiac analysis and parameters visualization
Video explain respiratory Disease test
Our AI on respiratory audio
Covid Risk test
(based on Italian Guidelines)
Covid risk test online
GitHub
GitHub Repo
Check out Our website IntelliHearts.com to keep in touch and have more Info
Built With
api
css
firebase
github
html
html5
java
javascript
keras
python
tensorflow
Try it out
159.203.68.29
www.intellihearts.com
www.intellihearts.com
github.com | IntelliHearts | Use automatized algorithm to early detect Covid-19 complications at Home | ['Lorenzo Diomeda', 'Selene Colapietra', 'Antonio diomeda'] | [] | ['api', 'css', 'firebase', 'github', 'html', 'html5', 'java', 'javascript', 'keras', 'python', 'tensorflow'] | 75 |
9,890 | https://devpost.com/software/yourturn | Note
Everything you see in the video is implemented. Please see our public
GitHub repository
.
The problem our project solves
Social isolation is a major point nowadays. A huge variety of studies shows that social isolation is leading to severe mental health issues not limited to cognitive decline, the onset of Alzheimer’s Disease or dementia, depression, neuroticism and hallucinations [1][2][3].
Common ways of interacting with other such as going to the gym, heading out for a drink or being at work or at university are not possible anymore. The lockdown is causing health issues for every generation, no matter if we consider the smallest kids or the grandparents [4].
The solution we bring to the table
We enable everyone to stay connected during the Corona crisis. In our app, people can create challenge, invite their friends, find challenges in their neighborhood and win prizes. Do you have it to show everyone you're the best in removing trash in the forest or to show a bike champion that you can do a wheelie while wearing a face mask?
Furthermore, YourTurn users can create public and private challenges with or without prizes. Also, they can chose to donate a certain amount of the prize pool for social causes.
Our implementation details
We built a native App with React Native. In this way, we generated native Apps for Android, iOS and the web. We connected our App to a FastAPI backend that is deployed on Amazon Web Services. To ease the hosting and deployment, we are running the backend inside a Docker container. The backend accesses data from a MongoDB that is also hosted on AWS.
The entire code base is available open-source with a MIT license.
What we have done during the weekend
We have built the application from scratch. We split up in three different teams and took care of the conceptual design, the frontend development and the backend development. We learned JavaScript and mobile development as well as new APIs and tools. Also, we reached out to prominent people in Germany leveraging our and our friends' networks and even managed to acquire the German track bicycle champion to promote our application with his own challenge.
We have pitched to mentors across various domains and even received funding offers beside a lot of amazing feedback!
The solution’s impact to the crisis
YourTurn minimizes the risk of mental health issues during lockdown. Various studies have shown the immense need for an application like ours to ensure a healthy and functional situation. While YourTurn not only minimizes potential of mental health diseases, it connects people locally and remotely. What's better than doing games with your (grand-)parents or your friends and showing them you are the best?
The necessities in order to continue the project
We want to get in touch with health insurance providers and possible challenge partners. Most health insurance providers already have bonus programs to reward activity improving one's health and may be interested in collaborating with us. We already acquired challenge partners, but are also interested to get in touch with companies who could promote their challenge and, let's phrase it as it is, advertise through their challenges.
On the other hand, we are right now reaching out to networks such as the German food bank, school networks and ministries to see if they would potentially be interested in promoting our YourTurn and bringing a user base to the platform. We have a large network and will use it up to the last end, however, we are looking for active marketing partners. If you can help us generating outreach and attracting users, please get in touch with us!
From a technical point of view, the current implementation is in a great shape and ready to be used. However, for a go live we would like to encourage UI and UX designers to give us feedback about the current usability and design.
Technical enhancements
So far, we trust users that they only check challenges they completed. While it's quite straight forward to scrape public posts on social media, our team holds expertise in analyzing image and video data. Leveraging this knowledge, we will be able to automatically analyze social media images in order to determine whether images with a solution to the specific challenge have been provided or not.
Also, our challenge feed is right now based on certain fixed criteria, i.e. we show our challenges, the challenges of our friends and other challenges in random order. The challenges can be filter based on different tags and geographical information. However, to scale the platform, we want to implement a collaborative filtering based recommender system that ranks the challenges in an appropriate order, i.e. users will see challenges they might be interested in earlier in the feed.
The value of our solution after the crisis
All our team members have left their home towns to explore something new. We have been living in all parts of the globe covering most continents and know it can be difficult to join a new community without knowing anyone. Either for studying, for work or just to see something new, people are consistently changing their environments. While platforms like Jodel and Facebook groups give people an impression about certain activities that are going on, it's difficult to actually get in contact with others unless one is super extrovert. What's the solution for it? Easy, do a challenge with locals!
Also we lower the potential of mental health diseases by easing the contact to friends, family and any person on the planet having access to an internet connection. Mental health issues are a severe point during lockdown times, but can't be neglected afterwards. The WHO presented statistics that almost 5% of the world population suffered from depression before the 2020 lockdown to name only one of the various risks [5].
The potential of a challenge application
For now, such challenges are primarily done through social networks such as Facebook, Instagram or TikTok. While this way is very established, a global challenge platform will come with a lot of advantages over the decentralized challenge environment. Our platform comes from a social motivation. It is possible to track the bets and the results are taken seriously. We all remember the ice bucket challenge, but probably there wasn't anyone who actually made up for not completing the challenge. Our application is the perfect way to remind your friends that their debts haven't been paid. Furthermore, we add the opportunity to donate the money for social causes. A challenge profile, challenge stats, connecting to new people, exploring interesting challenges and competing together with and also against friends and family are only few of the advantages YourTurn comes with.
But is doing challenges really a thing? KFC recently started its
dothecolonel challenge
and already attracted over 350 million people. This did not only bring a lot of reach to the company, but also increased their Net Promoter Score from 13 to 53. Also, who doesn't know the
ice bucket challenge
, the
mannequin challenge
or Drake's
In My Feelings / Kiki challenge
? These are just few of the numerous challenge that went viral to demonstrate the enormous potential of our application. The success of "In My Feelings" made Drake the record holder for most number one hits among rappers in the history of the Hot 100 chart.
Our team
A large part of our team worked in previous hackathons together. For this hackathons, some of our friends helped us out to create YourTurn. Our team contains a variety of backgrounds that allows us to create meaningful products. Our team in detail:
Arne Fornell holds an Industrial Engineering MS from Karlsruhe Institute of Technology and works as strategy consultant applying his expertise in business management and finance. His broad experience allows him to analyze markets and business potential connecting many domains.
Malte Fornell is about to finish a Computer Science BS at University of Hamburg. During his time as a working student at Airbus, he has gained valueable insight into working environments aswell as improved his analytical abilities. Data analysis is one of his strong suits, including implementing business intelligence into user friendly dashboards.
Manuel Lang holds a Computer Science MS from Karlsruhe Institute of Technology and works as freelancer in machine learning and software engineering. He finished his MS thesis at Carnegie Mellon University, published his work at ICLR 2020 and obtained industry knowledge working with companies across various domains such as NASA.
Marius Bauer is about to finish a Computer Science MS at Karlsruhe Institute of Technology. He gained a lot of research experience in machine learning and computer vision and works on a freelance base since almost 10 years.
Minh-Kha Nguyen holds an Industrial Engineering MS from Karlsruhe Institute of Technology and is about to start his work as data analytics consultant applying his knowledge in data science and management. His experience allows him to sell a ketchup popsicle to a woman in white gloves.
Nils Keßler is about to finish an Industrial Engineering BS at Technical University of Darmstadt. His creative and curious nature allows him to easily learn new tools and technologies, and quickly adapt to new situations.
Timo Keßler holds a Management MS from WHU – Otto Beisheim School of Management. His management and consulting experience allows him to come up with possible use cases and to take the view from potential business partners. Also, his network allows us to get in touch with possible multipliers helping in marketing.
Quotes from us:
"To develop a concept and strategy from scratch, with creativity and fun in mind, has been a great experience". - Arne
"Streamlining ideas to find a cool solution that solves a social need and serves a large consumer market has been lots of fun!“ - Timo
"I really need some motivation to do my exercises. Let's just push it to the AppStore!" - Manuel
"Never thought I would have been able to craft a working app in 48 hours without prior experience in mobile app development!" - Minh-Kha
Sources
[1] Holt-Lunstad, J. (2017). The potential public health relevance of social isolation and loneliness: Prevalence, epidemiology, and risk factors. Public Policy & Aging Report, 27(4), 127-130.
[2] Klinenberg, E. (2016). Social isolation, loneliness, and living alone: identifying the risks for public health. American journal of public health, 106(5), 786.
[3] Santini, Z. I., Jose, P. E., Cornwell, E. Y., Koyanagi, A., Nielsen, L., Hinrichsen, C., ... & Koushede, V. (2020). Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. The Lancet Public Health, 5(1), e62-e70.
[4] Child, S. T., & Lawton, L. (2019). Loneliness and social isolation among young and late middle-age adults: Associations with personal networks and social participation. Aging & mental health, 23(2), 196-204.
[5] World Health Organization. (2017). Depression and other common mental disorders: global health estimates. World Health Organization.
Built With
amazon-web-services
docker
fastapi
javascript
python
react-native
Try it out
github.com | YourTurn | The global platform where friends can challenge each other. | ['Minh-Kha Nguyen', 'Manuel Lang', 'Arne Fornell', 'Marius Bauer', 'timokessler95', 'Malte Fornell', 'Nils Keßler'] | [] | ['amazon-web-services', 'docker', 'fastapi', 'javascript', 'python', 'react-native'] | 76 |
9,890 | https://devpost.com/software/covid-fyi | Why we built this-
Critical information
on where to go, who to call, and what to do is scattered across gazillion tweets, websites, Whatsapp forwards. There is
no one place
for all official information for Citizens. The information is
not standardized
- they exist in non-standardized circulars. These are often
too technical
for the common man. With cases rising,
Chaos
and
misinformation
might only aggravate.
What does it do-
COVID FYI brings updated information from all
official sources
at all levels of granularity (National to Hyperlocal Data) on resources a common man could access to alleviate their problems, report emergencies or provide help. It brings the list of labs, hospitals, helpline, telemedicine, fever clinics, grocery store numbers etc. at one place such that a common man is aware.
How we built this-
We created a
nationwide database
with a team of Data folks, collating data manually across government websites releasing circulars and announcements on a daily basis. This database is used to power a frontend UI that is
user friendly
to provide only relevant information to each stakeholder and use case. If you are a
citizen
you would be more interested in testing facilities and helpline. If you are a
supplier
you could access control room numbers etc.
Challeneges we face-
Data is of such
varying forms
, at so many places, far away from the reach of common man - finding them all was a tedious task. Every day these circulars were updated, thus we needed to keep a check. There were so many classifications and sub-classifications, making it all the more difficult to decide on the right user flow (Eg. Labs include - Private, Government, Only Testing facility, Only Sample collection facility etc.)
Achievements we are proud of-
Several
startups
showed positive interest to partner with us.
Mapmyindia
wanted to provide us APIs to link location for all data.
Livehealth
an international e-diagnostic startup wanted to provide sample collection facility through them, handling the Lab-side of the market with their team, putbnb a
crowdsourcing platform
offered to crowdsource our database. Our idea was demo-ed at
Coronathon.in
(Indian Corona-Hackathon) and was also selected for
HackTheCrisis India top 300
. We have received appreciation from
several VC firms
and startups in Indian space.
Indian Institute of Management Kozhikode
is our strong pillar of support to provide outreach and media help to promote our project.
Our team
grew from mere 5 member team to 25+ (including volunteers). Without investing a single penny we have created this product in
record 5 days
.
What we learned-
We learned that though important, it is difficult to access
high-level government officials
directly. We initially thought to
partner with big brands
to put our best foot forward and scale faster. However we realised we need
proof of concept
to show the validity of our idea. Hence now our focus is to not ask for any help, and first build the initial 1lac or 0.1million users, have some numbers to speak for idea. We learnt a lot from each other's skills and got to know a lot about other areas.
Our plans for the future-
In the near future we are implementing our website for
India-wide launch
using organic mediums. We will work on extra features- Location tagging, API integration to help other projects. Since the Database is a never ending
Work-in-progress
in times like these, we will constantly update and work to add more official data. For this we are looking to work for, and work with several
state governments
.
Built With
djangorestframework
flask
google-cloud
heroku
mongodb
netlify
postgresql
react
vuejs
Try it out
covidfyi.in
github.com
github.com | COVID FYI | One-stop platform for the citizens to access covid-related services and help from official government sources. | ['Prateek Katiyar', 'Mohammed Zeeshan Fatmi', 'Sujit Joshi', 'G Rohit', 'Yogesh Bhatt', 'Vishesh Agrawal', 'Tanmay Mundra', 'saathwik chandan', 'Aliasgar Kundawala', 'Utkarsh Gupta', 'Manan Gouhari', 'SIMRAN SONI'] | [] | ['djangorestframework', 'flask', 'google-cloud', 'heroku', 'mongodb', 'netlify', 'postgresql', 'react', 'vuejs'] | 77 |
9,890 | https://devpost.com/software/comercialmedapp | home page
Business model canvas
Inspiration
Nowadays, with the arrival of the new economic consequences of the coronavirus crisis, our main inspiration raised on how to increase the efficiency to reduce the transaction costs in every European business. Also, we wanted to promote internal business transactions between European companies.
What it does
CommerceMed helps companies to find products that they need to produce here own products, but making sure that those products are from the closest European Companies.
e.g. Imagine a company that manufactures sanitary napkins. Probably it's obtaining the raw material from some Chinese factory. Increasing Europe's internal economy has always been an important mission, but with the economic crisis caused by the coronavirus, it is even more essential. CommerceMed will help this company in the example to contact easily with other companies (in Europe) that produce the raw material that it needs.
How we built it
We build it with a lot of love and with expectations about a good (healthily and economically) future for all us.
Technically front-end and back-end are built with Django, knows as "the Web framework for perfectionists with deadlines". With this, the architecture of the application is an MVT architecture (Model-View-Template). Data is managed with Postgresql. We have used Docker for simplifying and to accelerate the workflow.
Challenges I ran into
Make an application usable enough, for connecting companies.
All the application has been made during the weekend.
What's next for ComercialMedApp
Making the search tool more powerfull in orther to make the search for the users as easy as possible.
Finish a couple of things that due to the lack of time we havent been able to implement, becouse ase we said the application was made entirely this weekend.
Implement directly communication (e.g. via chat).
Make a Mobile APP in orther to make easyer the communication.
Built With
admin-tle
crispy-forms
django
docker
i18n
postgresql
python
Try it out
github.com
commercemed.herokuapp.com | CommerceMed | A commerce mediator. The objective of the ComerceMed team is to help to increase the internal trade of the European Union, especially, during the Covid-19 crisis.. | ['Alba Lamas', 'horno Palacin', 'Sergi Simón Balcells', 'Joaquim Picó', 'Oriol Alàs'] | [] | ['admin-tle', 'crispy-forms', 'django', 'docker', 'i18n', 'postgresql', 'python'] | 78 |
9,890 | https://devpost.com/software/joyfuel-5ntu7b | Inspiration
I'm Silvia, a 37 years old Psychotherapist and 7 years ago I discovered I had a tumor. This fact, in my 30 years old head, generated a lot of anxiety and fear of being sick.
At that time I was an HR Consultant, Trainer & Business Coach and I decided to specialize to also become a psychotherapist just to help people who suffer from anxiety disorders and the stress that generates managing a disease, your own or a loved one.
How Big is the problem?
30% of European People have suffered Anxiety (
Craske, MG; Stein, MB - 2016 - "Anxiety"
) so it affects more than 220 millions people in EU and now, after lockdown and the Covid Pandemia, the prevalence rate for anxiety rises up to 44.7% (
A brief mental health screener for COVID-19 related anxiety
)... Anxiety is a big issue to be faced now!
What it does
JoyFuel make psychology and psychotherapy within everyone's reach, understandable and accessible to as many people as possible to help them feeling better, day after day!
JoyFuel is an online platform (accessible via mobile and pc) to help people improving their skills to manage Stress and Anxiety. They can rely on digital contents and real psychotherapists.
The digital content is based on a new a model of intervention focused on a two-dimensional approach: Body&Mind. The users learns how to:
be aware of their own level of stress/anxiety;
manage their own anxious thoughts and body-reactions to stressful events (eg the Covid19 emergency);
develop different and more functional strategies to manage stressful events with less anxiety;
build a better self-confidence and have a more positive and well supported lifelong journey.
JoyFuel provides a mix of self and assisted learning activities:
webinar, masterclass and video-training programs for self learning held by specialist doctors and psychotherapists;
digital psychological support meetings (both individual and group) with psychotherapists;
daily support with tips and news with small exercises to maintain focusing and motivation;
weekly programs of digital live classes of yoga, mindfulness practices and group meditations.
Our method/solution is a mix of integrable actions, not just one!
Our competitors (eg Headspace or Calm) only suggest "meditation", or "yoga", or "psychotherapy".
JoyFuel has an unique approach giving access to different contents and exercises, tailored on the person needs.
The psychological approach, that we promote and
distinguishes us from competitors
, is the psychotherapy approach of Bioenergetic Analysis (
search for “IIBA Bioenergetic Analysis” if you want to look something in details
): a more bodily based approach than mental (it differentiates JoyFuel from cognitive approach, or CBT or mindfulness).
JoyFuel provides a psychological support but it's not a substitute for face-to-face psychotherapy.
How we built it
During this Hackathon we analysed the process and made it more scalable defining the best balance between digital and live contents.
We build a “tailoring strategy” in order to ensure each user with a personalized experience.
We begin the production of the JoyFuel portal and aggregate all our services in one place.
Our team is composed by:
Silvia - CEO and Psychotherapist;
Riccardo - Process engineer;
Stefano - Developer;
Giovanni - Product manager.
Challenges we ran into
Silvia has studied and integrated different psychological, mental and bodily techniques that lower stress and anxiety levels and she developed a model of intervention focused on a two-dimensional approach: Mind&Body. A method supported by the Nobel Prize E. Blackburn.
But she wanted to help more people, and to achieve this we begin this adventure together creating this team and building JoyFuel!
We started from my previous digital products: an online course called "BastaAnsia" (that means "StopAnxiety") which aims to combine self learning (digital course) with psychological support in distant small groups. We improve it transforming a personal approach to a replicable model and sustainable method.
Accomplishments that we're proud of
Before starting the development of our platform JoyFuel, we validated the users needs and our business model. These are our results:
a blog
www.ilcorpoelamente.com
that
sold more than 800 “Joy Paths”
(a lighter version of JoyFuel);
a IG profile:
@ilcorpoelamente_
with
more than 11.000 followers
;
a Youtube channel:
ilcorpoelamente
with
more than 8000 subscribers
;
a podcast #unmillimetroalgiorno with
more than 120K listeners
on
Spotify
and
ApplePod
.
On these platforms we disseminated stress management techniques and anxiety management and reduction strategies.
What we learned
In these three days of hard work we:
Learned how to apply the
lean methodology
to understand the needs of our reference market, which in this case has provided us with a clear request to build a portal available 24 hours a day to have ad hoc paths and constant psychological support to manage stress and anxiety.
Saw the great engagement of people who landed live on the portal on Sunday morning at 10.30 a.m. We understood that there is a real need for concrete tools and instruments from a market ready to use them.
Defined a long term strategy to delight the community on a daily basis with content that keeps them engaged, active and motivated in their process of growth or difficulty management.
Impact on society
During the Covid emergency this online support space was fundamental to offer concrete help to 105 people, isolated at home, Covid19 positive and not, as they could continue to have psychological support, inspirational phrases, webinars and Live, exercises and practices to keep their anxiety level low or to have support in cases of increased stress.
When this emergency will be ended, JoyFuel will continue to help people. We aim to provide every EU citizen (and even world citizen) with an effective “companion” to fight and manage her/his anxiety.
The results we got so far are extremely encouraging. The messages of our patients and their Joy is the best reward for our work
.
Business Model
JoyFuel has 2 Business-lines:
A Freemium model for user -> "Free access" to most of the contents and “Premium Access” to specific contents or individual support;
We offer a Membership plan for professional psychotherapists to join JoyFuel patients network.
What's next for JoyFuel
The alfa version of the portal is already available;
The beta version will be ready by the end of this month;
On this Sunday we delivered a LIVE EVENT to get as many people on the beta platform as possible;
In May 2020 we will launch the platform in Italy and begin the translation in
English
;
By October 2020 all the contents will be translated and the expansion in Europe will begin;
In 2021 we will have enough data to change our algorithm-based tailoring to an AI-based.
Less anxiety means a more happy and sustainable society.
Thanks to this amazing team of engineers, developers, marketers and psychologists we will fight back anxiety with "JoyFuel - Unleash your Joy".
Built With
c#
google-sites
html
youtube
Try it out
sites.google.com | JoyFuel - Unleash your Joy | JoyFuel helps people to improve their skills to manage Stress & Anxiety Desease via Body & Mind self-training. | ['Giovanni Mosiello', 'Stefano Bruzzese', 'Silvia Pasqualini', 'Riccardo Arciulo'] | [] | ['c#', 'google-sites', 'html', 'youtube'] | 79 |
9,890 | https://devpost.com/software/covifight | Inspiration
The virus has affected humanity in various ways, be it our economy, our freedom of movement, and the loss of loved ones. Then how do we live on, comfortably, and safely with this virus around?
Even after the lockdown is over, there is a massive possibility that traces of the virus will remain, and it can spread again.
We wanted to bring people back their mobility and keep them safe at the same time. We wanted people to know about their status while they leave their houses.
What it does
CoviFight is not just a contact tracing application. It is a 3-tier solution which:-
• By using Machine Learning and Social Networking analysis, CoviFight alerts the users the risks of catching the virus if they have come in contact with an infected person within the past three weeks.
• Identifies the public place or the transport mode, be it a bus, a metro, or a McDonald's restaurant, that needs sterilisation.
No other application in the world is capable of doing so.
• It informs the medical system accurately about the spread of infection. The medical system rather than individuals handle data, and authenticity is maintained.
• It generates a map with hotspots for what places have virus traces so that people can prevent travelling at these places and authorities can sterilise or lockdown these places efficiently rather than having a complete lockdown of a country.
• CoviFight does not even need to compare data between people, thus making computation very cheap and exponentially faster and efficient.
If Contact tracing apps already exist:
Even if you have a contact tracing application launched in a region, our second and third-tier may supplement the existing application. These tiers may add to the architectural ecosystem of the app without modifying the app at all.
It creates a supplementary layer of the architecture of the existing contact tracing apps and creates an ecosystem. We create an environment where devices connect independently and irrespective of their running OS and the contact tracing app installed.
All our apps can seamlessly integrate with any existing app's architecture. Our apps are capable of detecting and communicating with other apps like Aarogya Setu and COVIDSafe. The flexible nature of CoviFight provides the user with a choice of selection. The dynamic nature of our product gives the users a choice to use our app with or without GPS tracking.
Currently, the global market is saturated with many products claiming efficient contact tracing, but not a single app in the industry is capable of detecting aggregation points. Our architecture is developed to flexibly integrate with the existing system created and adopted by the government.
Apple and Google Guidelines:
Tracking of aggregation points( stores and public transport) is a difficult task to perform. Tracking people who have access them is also a challenging task. Before it was feasible, though very complicated and requiring complex computation, these were feasible by geo-location and time stamping. But the recent guidelines issued by Google and Apple prohibit the use of these techniques, making indirect contact tracing infeasible. The fantastic thing is,
our three-tier solution allows us to do so even while following the concerned guidelines.
How we built it
We develop a three-tier app:
• A user's app
• A provider's app
• An official's portal.
Privacy:
While utilizing Bluetooth of your phone, CoviFight makes sure that the confidentiality and privacy of every individual are secured and can not be compromised. Data is encrypted using a secret key, and no one can view it without your permission. It only traces the past data of positively tested patients. This way, CoviFight also meets the
GDPR compliance.
Hence, we have made sure that the privacy of every individual is maintained and can not be compromised.
The encryption algorithms meet the standards of the leading social networking apps existing in the market.
Provider's app:
A provider's app for aggregation points like shops, restaurants and public transport synchronizes with the nearby user app. This interface is the key to the detection of infection points, be it a stationary workplace or a moving vehicle. If a restaurant installed CoviFight and had an infected customer in the past 15 days, all the customers after the positive tested patient would get alerted, and hence the restaurant can be sterilized.
Official's Portal/ Doc App:
Only the medical system may update a person's status over the official's portal, and the authenticity of the app is maintained, hence
preventing false positives
or self-reporting, which might lead to falsification of records. No one else can manipulate the data.
Hence, people can move around while being alerted about their status. They can stay away from the virus, and be free from the worry of their privacy maintenance at the same time.
Challenges we ran into
To maintain the authenticity of the predictions and analysis, we were initially in a fix as to how to update a person's status as positive or negative. Then we decided to come up with a three-tier system, and we developed a Doc App or official's portal, which is only accessed by the medical system so that the authenticity is maintained.
No one else can manipulate the data.
Accomplishments that we're proud of
• We have made sure that the
privacy
of every individual is maintained and can not be compromised. The encryption algorithms meet the standards of the leading social networking apps existing in the market.
• CoviFight not only alerts people about their own risks but provides heatmaps of the traces of the virus too. CoviFight also shows what
specific restaurant or public transport( like a bus or a train) may be infected
precisely.
• We do not need to compare data between people, thus making
computation very cheap and exponentially faster and efficient.
Our Journey So Far
•
Winners( Runner up) in the #EUvsVirus, a Pan-European Hackathon
Organised by the European Innovation Council to counter COVID-19 pandemic with more than 9k participants and 2000 teams. We stood second in the Real time Communication and Prevention challenge.
•
Top 6 finalists of The Global Hack, by Garage48, April 2020
The Global Hack is a hackathon designed to share and rapidly develop ideas for urgently needed solutions in the face of the COVID-19 crisis, and to build resilience post-pandemic, with over 12k participants from 100+ countries. The team developed a mobile application solution for the containment and tracking of this virus. We were in the top 6 teams in the Crisis Response Track.
•
Top 23 Student Innovators in COVID-19 SAMADHAN MHRD( Ministry of Human Resource Development, India) Mega Online Challenge
What we learned
It has been an enjoyable experience to work with people who have not even met each other before and still successfully develop this amazing app. We learned a lot through the hackathon, from interacting with the mentors and getting their guidance, to develop the app.
What's next for CoviFight
We plan
to get this deployed
at its earliest so that people may get their
safe mobility
back. We plan to deploy this on the Play Store and make a version for iOS as soon as we can.
We are also in touch with the Indian Government and we might be able to save lives in India also.
The necessities to continue the project:
• Approval from government authorities to implement and track data.
• Participation from Hospitals/government bodies to update the status of a patient so that system can generate realtime alerts and mark hotspots.
• Cloud resources to scale up the project. Currently limited by the free tier of cloud infrastructure.
The value of our solution after the crisis:
• This application can be used for any contagious disease management.
• It can be used in disaster management to understand the right victims and relief reaches all rightful beneficiaries( such as in the case of floods and storms).
• It can be used by Providers such as McDonald's and Public transport systems to implement targeted location-based marketing complying with data collection practices.
What we have done during the weekend
• Implemented masked identities for users to comply with GDPR and privacy requirements.
• Identification of hotspots in realtime based on the patient status update.
• Fixed bugs in the flow and to make it work E2E.
• Produced a product demo.
Built With
android-studio
blockchain
bluetooth
dockers
firebase
google-directions
graphdatabase
har
ios
java
kubernetes
machine-learning
maps
neo4j
node.js
python
Try it out
github.com
sidhantha.medium.com | CoviFight | CoviFight showcases how the integration of Bluetooth with Social Networking Analysis makes contact tracing highly effective and the world can come to a working state of normalcy as early as possible. | ['Devesh Todarwal', 'MANIT BASER', 'Sidhantha Poddar', 'Anshuman Saboo', 'Yash Bhagat', 'Neil araujo', 'Shikhar Mathur', 'vishwas puri', 'Tirth Jain', 'sarthak-choudhary', 'Navdeep Chawla', 'Mudit Shivendra', 'Trinadh K N', 'Mehul Jain'] | ['Challenge Winner'] | ['android-studio', 'blockchain', 'bluetooth', 'dockers', 'firebase', 'google-directions', 'graphdatabase', 'har', 'ios', 'java', 'kubernetes', 'machine-learning', 'maps', 'neo4j', 'node.js', 'python'] | 80 |
9,890 | https://devpost.com/software/discovid-ai-a-search-and-discovery-engine-for-cord-19 | discovid.ai - search interface
The Challenge
Since the beginning of the current pandemic, there has been a
rapid increase in scientific literature
around COVID-19, which is hard to keep up with. Additionally, the limitations of scientific conferences make it even harder to collaborate and stay up to date. But it is crucial for scientists to be aware of ongoing research to find relevant publications and to identify gaps in the current literature to set their new goals. That's why we have developed DISCOVID.AI.
The Solution
DISCOVID.AI is a
search engine specialized on the
CORD-19
corpus
- a collection of over 52,000 scholarly articles about COVID-19 and related viruses.
Our website evolved from the
COVID-19 Open Research Dataset Challenge
on kaggle.com where we've received a lot of positive feedback on our
topic model
. It's a machine learning approach that essentially learns topics in the corpus and thus helps to
uncover hidden semantic relationships
. We can then see each article as a mixture of these topics
(which themelves are distributions over words)
. By mapping each article into the topic space
(a simplex with a topic in each corner)
, we can then find related articles.
For this,
we analyze the full text of each paper
and not only use metadata or the abstract like most search engines.
This approach lies at the heart of our search engine and enables users to iteratively click their way through related research to
discover new insights
. It is also used for personalized reading suggestions based on the user's bookmarks.
For the initial search we use
whoosh
, which enables you to either search for simple keywords or use more complex
boolean queries
(AND, OR, NOT, etc.) and
phrase queries
to help you find exactly what you are looking for. We also provide the option to search in specific fields (title, abstract, authors, doi and methods) or use phrase queries with double quotes. Try for example:
journal:(The Lancet)
authors:Drosten
doi:10.1101/2020.01.31.929042
title:hydroxychloroquine AND methods:(randomized controlled trial)
title:("randomized controlled trial")
We've also performed
extensive data preparation
and cleaning to ensure a high quality output of our topic model. For example, for lemmatization, we used
scispacy
, which is useful for processing biomedical, scientific or clinical texts. Additionally, we've used
language detection
to remove non-English articles to reduce noise.
To ensure a pleasant user experience, we've designed a clean and
intuitive interface
. The website is realized as a react app and we used bootstrap to provide a
responsive design
.
Our Progress During the Hackathon
We've collected
feedback from medical researchers
and implemented new highly desired features, namely
bookmarks
,
personalized suggestions
based on the bookmarks and we've also added
links to clinical trials
registered in the WHO ICTRP whenever they are referenced in a paper.
(For this, we extracted all trial ids with manually crafted regular expressions)
. We've also released several minor
improvements to the user interface
.
Impact
We hope our discovery engine helps researchers around the world to navigate the current flood of publications and find what is relevant to their work. It could also
prevent duplication of research efforts
and help to
identify current evidence gaps in literature
.
Future Work
Our work can easily be expanded to other text files, that's why we plan to incorporate other data sources soon. Another important issue that we plan to work on is the
quality assessment of current publications
- for example, by automatically classifying the study design or extracting the sample size.
In the near future, we’d like to start closer collaborations, so we can implement further features that are useful to the research community and assist their workflow.
So, if you are interested, please get in touch via
[email protected]
.
Built With
bootstrap
css
docker
flask
html
javascript
nginx
python
react
scikit-learn
scispacy
ubuntu
whoosh
Try it out
discovid.ai | DISCOVID.AI - a search and discovery engine for COVID-19 | An ML-powered search engine to help researchers keep up with the rapid increase in literature around COVID-19. It supports complex queries and recommends related articles to discover new insights. | ['Daniel Wolffram', 'Tobias King', 'Rachel Gozal', 'Tobias Röddiger'] | [] | ['bootstrap', 'css', 'docker', 'flask', 'html', 'javascript', 'nginx', 'python', 'react', 'scikit-learn', 'scispacy', 'ubuntu', 'whoosh'] | 81 |
9,890 | https://devpost.com/software/safeplace-jio647 | raspberryPISideAngle
raspberryPIFrontAngle
DiscordBotInAction
SafePlaceHomePage
SafePlacePortfolio
SafePlaceAboutPage
SafePlaceLocationInput (manual and automatic)
SafePlaceDiscordChatBox
fireBase
GoogleMapsWithMarkers
Inspiration
Due to the Coronavirus, going grocery shopping has become more dangerous and challenging than ever for the everyday person as it puts the heath and safety of themselves and their families at risk. So, we created the SafePlace which uses hardware, web app and chat bot to make shopping during COVID-19 safer and more efficient.
What It Does
SafePlace uses a combination of hardware, firebase and two types of front end softwares (the web-app and chat bot) to interactively show regular people the locations, open/closing times and most importantly, the danger ratings of all of the stores around them. Our Raspberry Pi hardware uses computer vision to track people entering and leaving through a door. It then feeds that information to our two front end software (web app and Discord chatbot). There front end softwares display a variety of information, the key one being the danger rating of the store (chances of getting Corona) which is rated on a star system out of 10 and calculated using the formula 10-3-(square feet/number of people*3.14/1000). Ultimately, our mission is for you to feel safe during these times of distress.
How We Built It
First, we applied the Raspberry Pi 4, its Camera Module, and OpenCV to create a low-budget potential solution to the CDC’s recommended social distancing. By using a people-counting tracking system that can detect the number of people going into and out of a doorway, we are able to track how many people enter into a particular area. By recording the square footage of the building, we are then able to calculate the safety rating of the store, which basically says how far apart people are. Next, we linked that Raspberry Pi to a firestore that collected information about how many people are in the building, name of the building, location of the building, time and the square footage of the place. By using our web app, users can see the locations we are measuring on them around the map, and can check, based on our ranking system, which places are currently safe to go to and which ones are not. If a web app was not enough, we also created another piece of software, a Discord chat-bot, which provides another user-friendly front-end to our database’s information.
Challenges We Ran Into
We faced many challenges with our computer vision, which ultimately remained limited to the physical space of our Raspberry Pi hacker Justin. While our computer vision can successfully track people entering and leaving in a pre-recorded video, an appropriate viewing angle could not be achieved in-home, but we were are able to clearly demonstrate that the Pi Camera Module can be used, and that it does track humanoid objects like a LEGO figure. Furthermore, we had great difficulty coordinating the back-end server from Firebase with both the Raspberry Pi and the two front-ends. In addition, while creating the web app, one of the main difficulties was that we did not know how to add markers to the google maps that displayed all of the relevant real time firebase information. However, eventually, through a bunch of stack-exchange browsing, some old YouTube videos, and most importantly, sheer persistence to keep trying different solutions as a team, we were finally able to figure out the answers to these obstacles.
Accomplishments that we're proud of
Whether we win or lose this competition, we will surely be proud of ourselves for building such an extremely useful and interactive project from scratch. The feature of our project that we are most proud of is how fluent the interactions between our firebase, hardware, and two front-ends are. It was all of our first times creating such a complicated project and we are proud that we could have accomplished it.
What We Learned
We learned a lot of stuff during this competition including how to build a computer vision program using Raspberry Pi, how to create and connect a realtime database with both hardware and software and how to use Google Maps with React JS.
What's Next For SafePlace
Now that we have created our project's hardware, back-end and front-end, we want to publish our products and start commercialising this project with retail stores, shopping markets and a variety of other retailers to keep everyone safe and secure.
Other links
Google Slideshow
Built With
bootstrap
discord.js
firebase
firestore
html
javascript
node.js
raspberry-pi
react.js
Try it out
github.com | SafePlace | Our motto is Maintaining Our Social Distance Through Computing Vision. Inspired from the COVID-19 outbreak, we created a web app which determines your safety risk level in a certain, desired store. | ['Ayan Sayyed', 'jparrondo24 Parrondo', 'Dhir Kachroo', 'Mihir Kachroo'] | [] | ['bootstrap', 'discord.js', 'firebase', 'firestore', 'html', 'javascript', 'node.js', 'raspberry-pi', 'react.js'] | 82 |
9,890 | https://devpost.com/software/deep-learning-drone-delivery-system | Results of our CNN-LSTM
Accuracy after training our model on 25 epochs
MSE of our CNN-LSTM
How we preprocessed data for our model
Data preprocessing
Picture of Drone
Inspiration:
The COVID-19 pandemic has caused mass panic and is leaving everyone paranoid. In a time like this, simply leaving the house leads to a high risk of contracting a fatal disease. Survival at home is also not easy: buying groceries is frightening and online ordered necessities take ages to arrive. The current delivery system still requires a ton of human contact and is not 100% virus free. All of these issues are causing a ton of paranoia regarding how people are going to keep their necessity supply stable. We wanted to find a solution that garners both efficiency and safety. Because of this, drones came into the picture(especially since one of our group members already had a drone with a camera). Drone delivery is not only efficient and safe, but also eco friendly and can reduce traffic congestion. Although there are already existing drone delivery companies, current drone navigation systems are neither robust or adaptable due to their heavy dependence on external sensors such as depth or infrared. Because of this, we wanted to create a completely autonomous and robust drone delivery system with image navigation that can easily be used in the market without supervision. In a dire time like now, a project like this could be monumentally applied to bring social wellbeing on a grand scale.
What it does:
Our project contains two parts. The first part is a deep learning algorithm that allows the drone to navigate images taken with a camera which is a novel and robust navigation technique that has never been implemented before. The second portion is actually implementing this algorithm into a delivery system with firebase and a ios ecommerce application.
Using deep learning and computer vision, we were able to train a drone to navigate by itself in crowded city streets. Our model has extremely high accuracy and can safely detect and allow the drone to navigate around any obstacles in the drone’s surroundings. We were also able to create an app that compliments the drone. The drone is integrated into this app through firebase and is the medium in which deliveries are made. The app essentially serves as an ecommerce platform that allows companies to post their different products for sale; meanwhile, customers are able to purchase these products and the experience is similar to that of shopping in actual stores. In addition, the users of the app can track the drone’s gps location of their deliveries.
How I built it:
To implement autonomous flight and allow drones to deliver packages to people swiftly, we took a machine learning approach and created a set of novel math formulas and deep learning models that focused on imitating two key aspects of driving: speed and steering. For our steering model, we first used gaussian blurring, filtering, and kernel-based edge detection techniques to preprocess the images we obtain from the drone's built-in camera. We then coded a CNN-LSTM model to predict both the steering angle of the drone. The model uses a convolutional neural network as a dimensionality reduction algorithm to output a feature vector representative of the camera image, which is then fed into a long short-term memory model. The LSTM model learns time-sensitive data (i.e. video feed) to account for spatial and temporal changes, such as that of cars and walking pedestrians. Due to the nature of predicted angles (i.e. wraparound), our LSTM outputs sine and cosine values, which we use to derive our angle to steer. As for the speed model, since we cannot perform depth perception to find the exact distances obstacles are from our drone with only one camera, we used an object detection algorithm to draw bounding boxes around all possible obstacles in an image. Then, using our novel math formulas, we define a two-dimensional probability map to map each pixel from a bounding box to a probability of collision and use Fubini's theorem to integrate and sum over the boxes. The final output is the probability of collision, which we can robustly predict in a completely unsupervised fashion.
We built the app using an Xcode engine with the language swift. Much of our app is built off of creating a Table View, and customized cell with proper constraints to display an appropriate ordering of listings. A large part of our app was created with the Firebase Database and Storage, which acts as a remote server where we stored our data. The Firebase authentication also allowed us to enable customers and companies to create their own personal accounts. For order tracking in the app, we were able to transfer the drone’s location to the firebase and ultimately display it's coordinates on the app using a python script.
Challenges:
The major challenge we faced is runtime. After compiling and running all our models and scripts, we had a runtime of roughly 120 seconds. Obviously, a runtime this long would not allow our program to be applicable in real life. Before we used the MobileNet CNN in our speed model, we started off with another object detection CNN called YOLOv3. We sourced most of the runtime to YOLOv3’s image labeling method, which sacrificed runtime in order to increase the accuracy of predicting and labeling exactly what an object was. However, this level of accuracy was not needed for our project, for example crashing into a tree or a car results in the same thing: failure. YOLOv3 also required a non-maximal suppression algorithm which ran in O(n^3). After switching to MobileNet and performing many math optimizations in our speed and steering models, we were able to get the runtime down to 0.29 seconds as a lower bound and 1.03 as an upper bound. The average runtime was 0.66 seconds and the standard deviation was 0.18 based on 150 trials. This meant that we increased our efficiency by more than 160 times.
Accomplishments:
We are proud of creating a working, intelligent system to solve a huge problem the world is facing. Although the system definitely has its limitations, it has proven to be adaptable and relatively robust, which is a huge accomplishment given the limitations of our dataset and computational capabilities. We are also proud of our probability of collision model because we were able to create a relatively robust, adaptable model with no training data.
We were also proud how we were able to create an app that compliments the drone. We were able to create a user-friendly app that is practical, efficient and visually pleasing for both customers and companies. We were also extremely proud of the overall integration of our drone with firebase. It is amazing how we were able to completely connect our drone with a full functioning app and have a project that could as of now, instantly be implemented in the marketplace.
What I learned:
Doing this project was one of the most fun and knowledgeable experiences that we have ever done. Before starting, we did not have much experience with connecting hardware to software. We never imagined it to be that hard just to upload our program onto a drone, but despite all the failed attempts and challenges we faced, we were able to successfully do it. We learned and grasped the basics of integrating software with hardware, and also the difficulty behind it. In addition, through this project, we also gained a lot more experience working with CNN’s. We learnt how different preprocessing, normalization, and post processing methods affect the robustness and complexity of our model. We also learnt to care about time complexity, as it made a huge difference in our project.
Whats Next:
A self-flying drone is applicable in nearly an unlimited amount of applications. We propose to use our drones, in addition to autonomous delivery systems, for conservation, data gathering, natural disaster relief, and emergency medical assistance. For conservation, our drone could be implemented to gather data on animals by tracking them in their habitat without human interference. As for natural disaster relief, drones could scout and take risks that volunteers are unable to, due to debris and unstable infrastructure. We hope that our drone navigation program will be useful for many future applications.
We believe that there are still a few things that we can do to further improve upon our project. To further decrease runtime, we believe using GPU acceleration or a better computer will allow the program to run even faster. This then would allow the drone to fly faster, increasing its usefulness. In addition, training the model on a larger and more varied dataset would improve the drone’s flying and adaptability, making it applicable in more situations. With our current program, if you want the drone to work in another environment all you need to do is just find a dataset for that environment.
As for the app, other than polishing it and making a script that tells the drone to fly back, we think our delivery system is ready to go and can be given to companies for their usage with customers. Companies would have to purchase their own drones and upload our algorithm but other than that, the process is extremely easy and practical.
Built With
drone
firebase
keras
opencv
python
swift
tensorflow
xcode
Try it out
github.com | Autonomous Drone Delivery System | An autonomous drone delivery system to provide efficient and virus-free deliveries. | ['Allen Ye', 'Gavin Wong', 'Michael Peng'] | ['Best COVID-19 Hack', '2nd Place Hack'] | ['drone', 'firebase', 'keras', 'opencv', 'python', 'swift', 'tensorflow', 'xcode'] | 83 |
9,890 | https://devpost.com/software/lynz | Landing Page
View Busyness Levels Page
Inspiration
To flatten the COVID-19 curve, we're all doing our best to minimize social interactions. If possible, we've even barricaded ourselves moat and drawbridge at home hoping to wait this storm out. Even so, there is one necessity that nobody can wait out forever: grocery shopping.
Trying to socially distance and shop at the same time… Lengthy checkout lines and crowded supermarkets... Having to run a 3 hour errand at Walmart just for weekly groceries...
These challenges paired with the current global situation have led us to develop Lynz, an easy to use webapp that allows people to find out how busy any particular supermarket is based on live data provided by other shoppers. The hope is that by spreading accurate and actionable information, we can shop smarter and safer.
What it does
Users can view the busyness levels of nearby grocery stores. The busyness level is calculated using data from other users who reported the busyness level of the given store when they visited it. By providing this data to users, they can make informed decisions of when and where to go grocery shopping.
How we built it
This project was built using the MERN stack. On the front end, the React library was essential to design the UI and UX. When called upon, location data taken directly from Google Maps API enables Lynz to figure out a user’s current location and display all supermarkets within a given radius. The backend is built with Node.js and Express. The backend server sends busyness information from a MongoDB database to the user and also relays busyness reports from the user to store in the database.
To ensure that the busyness shown to users is accurate, we used a regression model based on exponentially moving averages. This means that database entries are depreciated based on the time elapsed, giving exponentially greater weights to more recent busyness entries. Our algorithm is geared to work in real-life situations based on the assumption of mass scale. This means that sufficient data is required before an accurate busyness is displayed to users.
Challenges we ran into
Working with new technologies including MongoDB, Express, React, Node.js, and integrating them together has been challenging. Being stuck at our homes, it has also been difficult coordinating with one another effectively. We also had difficulty deploying our webapp.
Accomplishments that we're proud of
Prior to this hackathon, our team had no experience working with the MERN stack. We did, however, decide that if we were to build something together, it would promote change through connecting communities in the midst of the global pandemic. Bouncing off one anothers' prior skills, strengths, and interests, we learned the MERN stack to build Lynz. Each one of us can resolutely say that "yes, this was a challenging experience and it has also been worth the while".
What we learned
We learned all about the MERN stack and how to effectively collaborate with each other despite being in our own homes.
What's next for Lynz
Moving past our hackathon project, we plan to spread awareness and engage people around local communities to use Lynz. With more users, the busyness level data will more more accurate and benefit everyone. We would also work on steps to build a mobile version of our platform to enable users to receive notifications on selected stores and streamline both convenience and accessibility.
Built With
axios
bootstrap
express.js
firebase
google-maps
heroku
mongodb
mongoose
node.js
react
Try it out
github.com
github.com
lynz-frontend.web.app | Lynz | Outsmarting lines, together | ['Nicholas Tao', 'Matthew Jiao', 'Adam Lam'] | [] | ['axios', 'bootstrap', 'express.js', 'firebase', 'google-maps', 'heroku', 'mongodb', 'mongoose', 'node.js', 'react'] | 84 |
9,890 | https://devpost.com/software/predict-19 | Solution Architecture
Do Europeans give consent to enabling location based services? YES
Solution Validation for individual app users
How many € per month would pay for a service offered by our solution
Snapshot about nationalities participating at the survey
Fundamentals of Data Privacy Strategy
GIF
App_Example
Problem
Quarantining positively diagnosed individuals and tracing their recent contacts to isolate potentially infected has been the current strategy to fight the pandemic in the early stages. However, if number of infections escalates quickly and become too large to be widely tested or contact traced, nations are forced to go into lockdowns.
As we know, nation-wide lockdowns are not a sustainable solution. The economic impact due to this drastic measure is devastating.
We have identified the reasons for a pandemic to quickly escalate out of control to be a lack of accurate insights on the risk of individual infections and lack of continuous and real-time information on virus hotspots. Only with accurate and real-time information, easily accessible for everyone, we are empowered to act effectively and sustainably in the fight against COVID-19.
We were driven by the thought,
can we successfully tackle the pandemic without going into nation-wide lockdowns?
Solution
Oh yes, in this hackathon we found that we can.
Our solution empowers individuals with
easily accessible, accurate and real-time information on individual risk of infection and virus hotspots
, by leveraging mobile technologies, health data and existing disease modelling research.
Mobile App
We have developed a mobile app that utilises GPS and bluetooth. We have also created end points where we can fetch pre-existing medical conditions of the users from the public health data. The app also periodically sends survey questions to those users who have been flagged under high risk by the app based on the data collected and analysed. We only collect a minimum of data and have outlined the specifics in our detailed GDPR guidelines. Notifications and interactions will be context based and will consider the users surrounding and mood to display questions and updates.
We use AWS as a backend cloud service, because it enables quick prototyping and also large simple scaling and security.
Do you own an Android phone and want to get a feel for the app?
Download it
here
Don't forget to set permission for installing third party apps in your android settings
No Android device but still want to test?
Play with the mock up
here
Risk prediction
In order to compute the risk of an covid-19 infection we use state of the art mathematical modelling and machine learning techniques
Individual Medical Care
Population Medical Data
Infection Data
Mobility Information
Monte Carlo Simulation
Reinforcement Learning
We take public large scale data sources such as the development of the virus spread and general health information about the population provided by governments and institutions into account.
Additionally we collect our own data via the surveys, the encounter and the movement analysis inside the app.
With Monte Carlo Simulations we predict future virus spread and use this as in input for our Reinforcement Machine Learning Agent, who will be able to predict infection risks. This agent will be trained by validation data coming from test results and contact tracing solution efforts e.g. from Android & Apple or Pepp-PT.
We base this technology on existing research on Coronavirus spread models from earlier epidemics like MERS from leading researches in South Korea
Kim et al. 2018
.
We adjust this model with novel ML techniques and more complex data, utilising the unique willingness of the population to fight this virus and their openness to help.
Updates about our implementation can be seen here
Business Model
We have a 2 sided solution with subscription model for users and with business and governments on the other side.
Our business research showed that the average user would be willing to pay 3.84€ per month for a solution like ours
So we decided to offer the service in the app store initially for about 4 euros. This is will allow us to grow our active user base. Once we have confidence in our model we will be able to sell our predictions to businesses and governments and this will allow us to break even and scale-up
Data Privacy Strategy
We are aware that reservations against the extensive use of data in the fight against COVID-19 exist, especially in Europe. But, with the devastating effects of the shutdown growing every day, solutions are needed that use data and technology to overcome COVID-19 fast and efficiently. At the same time, we believe in data privacy as a cornerstone of any free and democratic society.
Our business model allows for Data Privacy Strategy based on full transparency and user control. We are not relying on the exploitation of personal data, so we do not need to hide what we do from our users. Adapting a forthcoming, highly transparent communication strategies, will allow us to build trust, stay compliant and benefit from strict data privacy rules. We will clearly explain what we are going to do with the users' personal data and obtain explicit permission from users for processing. Because health data is very sensitive for most users, we will give users maximum control over their data, beyond what is legally required.
Consent is the most transparent, user friendly and least risky justification for processing personal data, including health data. Consent allows us to process all the data we need, to provide the risk predictions. With regards to health data, which is specially protected under the GDPR, consent is also the most practical and fairest justification for processing.
Since giving consent to data processing is a standard procedure for most end user applications, asking our users for it, will not significantly lower conversation rates. We will use transparency and fairness as competitive advantages, by building data protection into the DNA of the product right from the start.
At every development step we consult with our team member and legal adviser Bork Morfaw, who is determined to to ensure GDPR compliance to the fullest extent for a functioning solution.
Further detailed information
here
in this document
Scale-up Plans
We have developed the solution foreseeing future pandemics with different behavior and incubation period. We also plan to scale up the products to be a software solution for the public health domain across nations which can be used to easily reach out to its citizens for in-app surveys which is also valuable in containing seasonal epidemics.
Many epidemiology models work similarly, that our app will be able to adjust to different outbreaks all over the world.
This can help preventing future pandemics and following global recessions.
Socio-economic Benefits
Our solution has various socio-economic benefits, especially when deployed widely. For example, we can help avoid shutdowns, advance European Sustainable Development Goals, support the fight against infection diseases, and support resource allocation by providing insights into the spread of the disease.
Schools and Universities could remain open
Remote work can be reduced as only high risk infectious people will stay home
Unemployment will be reduced due to higher mobility
Travelling will be possible, since our system can be used world wide
Resources can be allocated to upcoming hotspots of the epidemic
Faster response times and preparation leads to fewer deaths
Further detailed information
here
in this document
Progress made during the hackathon
During this hack we develop the mobile-app and the risk estimation model from the ground up. We came in as a team of 7 people from 4 countries, we met most of us at different hackathons competing against each other. We decided to join forces to inspire, innovate and engineer a solution that would save nations from going into lockdowns.
We started with digging deeper into the news, mentors and webinar to identify the most important problem or the pain point. Further, we spend ample time on brain storming ideas over discord channel which was active for past 60 hours without break. Once we had clear path to the solution we divided the tasks and started developing the solutions from scratch. We kept on updating the progress every 2 hours and made sure everyone is motivated and kept in the loop.
In short, we together developed a functional app from not knowing each other during the weekend.
Necessities in order to continue the project
PREDICT - 19 solution was developed as a stand alone fully functional service without any dependencies. Our app is able to make predictions and give real-time virus hotspots without dependencies. However, in order to get better risk predictions and more accurate virus hotspots, access to the pre-existing medical conditions of the users from the public health data is needed.
This is to be done with strong data protection agreements signed.
Feedbacks please
We would love your feedback to improve our hard work. Please download PREDICT-19 android app from
link
or from the
Try it out
section below. Looking forward to your valuable comments.
Remember to grant access to foreign apps while installing it.
Built With
amazon-web-services
kotlin
node.js
python
swift
tableau
tensorflow
Try it out
www.figma.com | PREDICT-19 | We predict the risk of infection of individuals and provide accurate insights on real-time virus hotspots by leveraging mobile technologies, health data and existing disease modelling research. | ['Kevin Jacob', 'Marvin Mouroum', 'Gijs Wissing', 'Anu Kurian', 'Jonny Smyth', 'Jack Doyle', 'Bork Morfaw'] | [] | ['amazon-web-services', 'kotlin', 'node.js', 'python', 'swift', 'tableau', 'tensorflow'] | 85 |
9,890 | https://devpost.com/software/coronica-s413ig | Coronica
Coronica is your in-home best friend and life coach when your real best friends and life coaches could put you in danger of COVID-19!
We created Coronica because we understand that the COVID-19 crisis has led to unimaginable stress and tragedy for many members of our society, so we want to do our part in alleviating any of the unnecessary stress left in our lives.
Features
In order to do this, we created a mobile and web application combo aimed at helping users develop healthy habits during the quarantine. These recommendations come in many categories, all stemming from your daily mood. When a user opens the application, they will be prompted to sign up on a simple login form, and then they will be immediately asked to take inventory of their mood on a simple daily survey. Then, based on the response, users will be directed to a page with a list of activities to help with that mood, all with point values for finishing and a cute animation to show progress. There is also a destressing game on cookie clicker on board as well as a health inventory that both helps the user reflect on their current health and allows users with COVID-19 symptoms to opt into being shown on a heat map that shows where symptoms are most popping up on any given day (heat map is not yet implemented).
Tech stack
For the mobile application, we used React Native for the front end and Firebase for the back end, specifically user authentication. For the web application, we used React connected to the same Firebase application to sync user authentication.
Getting started
Clone the repo from
GitHub
and then navigate to that folder in a terminal.
Mobile app
Add a .env file with the configuration details from your Firebase project and make sure to set up Firebase email/password authentication
Navigate to coronica-app and run npm install & expo start
Web app
Click
here
Future Plans
In the future, we hope to finish the heat map feature and add even more categories. We also want to add a way to compete with your friends for points as a way to further connect people during the COVID-19 crisis.
Contributors
Harsha Srikara
Caitlin Tibbetts
Afrida Tasnim
Built With
firebase
font-awesome
google-fonts
javascript
react
react-native
typescript
Try it out
github.com | Coronica | A self-care companion app to track self-care while weathering the storm of COVID-19 and a web page to show general statistics of users of the app. | ['Harsha Srikara', 'Caitlin Tibbetts', 'Afrida Tasnim'] | [] | ['firebase', 'font-awesome', 'google-fonts', 'javascript', 'react', 'react-native', 'typescript'] | 86 |
9,890 | https://devpost.com/software/nyc-covaid | Logo 1 Blue
Logo 1 Black
Logo 2 Blue
Logo 1 White
Inspiration
Katelyn has always been used to volunteering in her community and outside in other parts of the world. However she is immuno-suppressed and cannot leave her apartment in NYC, right now the center of the COVID-19 pandemic in the USA. She was looking for ways to volunteer from home and found a group called Sunnyside Woodside Mutual Aid, a group set up in her local Queens neighborhood. She quickly signed up to become a dispatcher connecting in need community members with able bodied volunteers. Every shift she had she was continuously asked the same question, "Is there a map somewhere of what's open in NYC still?" The short answer is yes, but its neighborhood oriented which means each has smaller audiences. There's nothing out there right now that covers the entire city, catering to that larger audience.
Enter in NYC CovAid
Joseph and Katelyn decided to build a map based app that would show all the legally operating businesses in the end user's community. The idea of the app is for the end user to put in their zip code alone with the category of aid they are seeking and a map will populate in their area with all nearby open businesses fitting the aid they need. We have 30 categories of types of Services Offered with 18,042 records of open businesses that we were mostly able to source from Open Source NYC but the specific datasets we used can be found here:
Legally Operating Businesses NYC
Pharmacies
Hospitals
Urgent Care
Prepared Meals
SafeGraph Grocery Store Data
How I built it
We chose to build our app using React. We started with the Create React App program from the Facebook team. We then wrote the code to change the massive CSV file with the 18,042 records into JSON format so that it could be parsed by the app later on. We then added a list view of all the businesses we had gathered and added a filtering capability. Next we started on the map view sourced from Google Maps and worked on plotting the individual records on the map using different colors for the 30 different types of services we were able to find. We changed the filtering capabilities to include the categories of aid/services we have records to. Next we had to hit the GeoCode API in order to turn zip codes into lat long coordinates. Now all the end user has to do is enter their zip code and our program will iterate through our location data making both a listview and mapview of all available records within a half mile radius. The list view is exportable with all information including location, phone number or website, and hours of operation.
Challenges I ran into
One of the biggest challenges we ran into was sourcing the data. It's incredibly difficult to take a bunch of resources a ton of individual contributors have put together and make a database out of it. The sheer amount of data we were working with was massive for a 36 hour project and a lot of it was entered in manually. It's also hard to use opensource data because it's not exactly tailored to what you are working on. However we were able to discover a few really great datasets that gave us the results we have.
Once the data was entered into the app it started crashing every few seconds trying to load all the records. We were able to solve this problem by adding pagination to our app so that only a certain amount of records are loaded per page. This solved our main issue of the app crashing.
Another issue we ran into when we first attempted a dynamic way to hit the geocoding API to decode the zip codes, but that ended up being inefficient and difficult to stand up so we hardcoded the lat long coordinates for those zip codes, limited the dataset to those zip codes, so now we're doing a simple dictionary lookup to get the positions of a service for a given zip code.
Accomplishments that I'm proud of
We are really proud of how the app works. It does it job which was the main goal of this hackathon for us. The other thing we are really proud of is our logos. It's simple yet gives you a good idea of what the app does.
What's next for NYC CovAid
If given more time we would work on the look and feel of the app and of course the performance of the app. Being that we are iterating through 18k records it would be nice to have this as a hosted database of some sort so that we could work on and control performance. We also would like to work on sourcing more data. We know there are pantries or food banks we might have missed. We want this list to get as big as it can for the sake of NYCers in need during this pandemic.
Using the App for Demo Purposes
Yet another issue we had was the issue of time. If given the right amount of time we would have been able to get all zip codes programmed in but for demo purposes we only have 3 available for testing. We got the app working for this subset of zip codes for proof of concept but plan to add all zip codes to support all 18k records as a next step. Right now its programmed to look up the zip codes 10016, 11209, and 11377. The data on the map appears as a heat map showing over which location the most resources are available. On the right is still a list view of all businesses that fit into your category of choosing within a half mile of those 3 zip codes with relevant information on each.
Link to created dataset
Created Dataset
Link to GitHub Repository
GitHub
Powerpoint Presentation
NYC CovAid Powerpoint
Built With
api
css
data
html
javascript
opensource
react
Try it out
josephspens.nyc | NYC CovAid | Live in NYC & wondering what businesses are legally operating in your area during the pandemic? Just put in your zip code & the aid you need & get back a list of over 18k businesses open nearby! | ['Joseph Spens', 'Katelyn Hertel'] | [] | ['api', 'css', 'data', 'html', 'javascript', 'opensource', 'react'] | 87 |
9,890 | https://devpost.com/software/tidal-predictions-kxrijb | Inspiration
"Britons warned to prepare for power blackouts in coronavirus lockdown" is a headline we came across recently.
Upon reading the article that followed, we realised that the electricity demand is at its peak due to people being at homes all day and the approaching summer. As our team's interest lies in renewable energy systems, we thought of an idea to maximise power generation from existing systems such as tidal lagoons.
What it does
A tidal lagoon has key components such as a sluice gate (to allow for water to enter), a turbine and a generator. Currently, predicted tidal heights are far from what what they are observed on the day.
The total observed height on any given day is a sum of the predicted tide and a residual term, also called a surge tide. This residual term is a value that is brought about due to weather changes, winds, the moon's position etc. and is hard to predict. However, this term causes a change in observed water levels and leads to loss in power (value in terms of Mega Watts) that could have been generated.
To solve this, at a time when power output is needed the most due to lockdown, we attempt at predicting the total observed height by trying to predict the residual term. Essentially, this would help generate more power than is currently able.
We have used Machine learning to solve this problem.
Our predictor predicts the total observed height on the day with 93.59% accuracy, hence maximising power output.
How we built it
We built it using python.
A 7 step process was used.
1) Data collection: We downloaded this dataset from the British Oceanographic Data Centre website for a site named "Newport". The data contains values from 2015-2019 at 15-minute intervals.
2) Data preparation and visualization: Performed by cleaning the data and creating probability density plots and pairplots.
3) Model Selection: Upon evaluation, random forest turned out to be the best combination of accurate and fast.
4) Model Training: Did a 80:20 Train:test split and trained our random forest model on the training data. Then used this model to predict values based on test data inputs.
5) Evaluation: Compared the accuracy of the predicted values to the actual values.
6) Parameter tuning: Tuned parameters such as "n_Estimators" which is the number of decision trees the model would use. Found an optimum number to be 128.
7) Accuracy: Accuracy is 93.59% and Mean Absolute Error (MAE) is only 0.23 degrees.
Challenges we ran into
Certain challenges we ran into included collecting and preparing the dataset. Alongside, figuring out the correct model took time and effort as each model would take minutes to run.
Accomplishments that we're proud of
We are proud that we were able to increase the accuracy of our model from 42% to 93.59%.
What we learned
We learned and applied lots of Machine learning!
What's next for Tidal Predictions
Looking at fine-tuning parameters to further increase accuracy and make the program run quicker. We also wish to develop an app/website that could provide a close-to-actual prediction for anybody who is seeking to know tidal heights eg. fishermen.
Built With
python
Try it out
github.com | Tidal Predictions | Maximising energy harvest in a time where electricity usage is at peak due to lockdown. | ['Arushi Madan', 'Rohan Chacko', 'Arun Venugopal'] | [] | ['python'] | 88 |
9,890 | https://devpost.com/software/stayhome-c9bk23 | StayHome Pitch slide 1
StayHome Pitch slide 2
StayHome Pitch slide 3
StayHome Pitch slide 4
StayHome Pitch slide 5
StayHome Pitch slide 6
StayHome Pitch slide 7
StayHome Pitch slide 8
StayHome Pitch slide 9
StayHome Pitch slide 10
StayHome Pitch slide 11
StayHome Pitch slide 12
StayHome Pitch slide 13
StayHome Pitch slide 14
Home web app
Analysis web app
Report free web app
StayHome analysis 1 mockup app
StayHome analysis 2 mockup app
StayHome Score mockup app
Logo
Inspiration
It has been estimated that there are more severe consequences and even higher death rates for people with Covid-19 if they have pre-existing cardiovascular disease or metabolic syndrome.
Metabolic syndrome is defined as the presence of three or more clinical conditions such as central
obesity
,
hypertension
, and
diabetes
.
It is possible that people within the ideal weight range can also be affected by metabolic syndrome. This is why it is important to evaluate the distribution of body fat.
Our aim is to provide an anonymous easy-to-use tool and give people awareness on their personal risk in order to make the right decision between going back to normality or continuing with social isolation.
Prevention is the keyword
Keep monitoring your Metabolic Syndrome Potential Risks in an easier, faster and cheaper way
What it does
StayHome analysis
is based on over 30 years of clinical nutrition experience of the team founder Professor Paolo De Cristofaro.
Based on hundreds of scientific papers, evidence and comparison with traditional tools and medical devices, it provides a complete health check on your BMI, fat mass, lean mass, cardiovascular and metabolic syndrome potential risk by analysing the circumference of specific points on your body.
You just need a measuring tape, scales, and insert the required information into the app.
StayHome will provide a complete health report on your:
Lifestyle
Body composition
Metabolic syndrome Risk factors
Moreover, the
StayHome Index
will give you a score on your potential risk of getting serious consequences from Covid-19.
Studies showed that people with certain conditions had higher chances of getting severe consequences from Covid-19, such as Intensive Care and even death.
StayHome Index provides a score based on the sum of different risk factors, such as:
Age
Sex
Clinical condition (comorbidities)
BMI
Central adiposity
Cardiovascular and metabolic syndrome risk
You can share your StayHome Score with your family, friends, co-workers, boss, and doctor in order to inform them on your status.
Where the idea came from
As a company (
Nubentech Srl
) we already work in the nutritional and fitness industry, developing digital solutions for body composition analysis, nutritional status, evaluation of potential risks from metabolic syndrome (
Morphogram®
). Reading news, scientific and clinical papers, and talking with doctors who are working in COVID-19 Intensive care units, we have seen the correlation between people affected by COVID-19 in intensive care (intubation) and the risk factors related to bad nutritional status. In these two days, we have just put the pieces all together, invented a new formula that considers every information found in these scientific papers and make the test easier, faster and cheaper
What after COVID-19
We know that the pandemic will end, and we really hope as soon as possible!
Our vision after the COVID-19 is provide to users a low-cost tool to track their body composition conditions (lean mass, fat mass, fat distribution, and so on) and also develop API fo any 3rd party app/solution that wants to expand their portfolio of service with our evaluation and tracking system.
How we built it
The MVP is a web app built using Angular 8.
We won't collect any sensitive data unless the user decides to take a premium analysis, gives us his/her email and payment details.
Challenges we ran into
Covid-19 is a nowadays reality. But soon we will have to face a new chapter of this crisis which will be COEXIST with the virus. We built a solution that will help to manage this new challenge, giving people a tool to self-analyse their health status and prevent to get serious consequences from the Virus, such as Intensive Care and even death.
Accomplishments that we're proud of
Our method (
Morphogram®
) is based on 30 years of experience in clinical nutrition and 2 years of clinical tests. StayHome project is a spin off of the startup Nubentech which has been rewarded as Excellence of The Year 2020 at the Life Science Excellence Awards as Marketing & Digital Solution for the cardiology.
What we learned
We learned that simple ideas can make a difference.
What's next for StayHome
We will develop the Premium Analysis, which will provide new useful information to the user, such as the somatotype, abdominal volume, metabolism, sleep apnea risk factor, the energy expenditure and the daily protein need.
Also, we will develop API integration, to connect StayHome with other services and we will work to improve the UX.
To make all this possible we need resources and partnership.
If you are interested in our project join our network and contact us.
Stay Safe,
StayHome
References
Ashwell M, Gunn P, Gibson S: Waist- to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review
and meta-analysis. Obesity Reviews, 2012, 13, 275-286
Balkau B, Deanfield JE, Després Jp et al: International day for the evaluation of abdominal obesity (IDEA): a study of waist circumference, cardiovascular disease and
diabetes mellitus in 168,000 primary care patients in 63 countries. Circulation 2007, 116(17):1942-1951.
LL-Y Lim, Sam-ang Seubsman, A Sleigh, C Bain: Validity of self-reported abdominal obesity in Thai adults: A comparison of waist circumference, waist-to-hip ratio and
waist-to-stature ratio
De Koning et al.: Waist circumference and waist-to hip ratio as predictors of cardiovascular events: meta-regression analysis of prospective studies. 2007
Manolopoulos KN, Karpe F, Frayn KN. Gluteofemoral body fat as a determinant of metabolic health. Int J of Obesity, 2010;34:949-959
World Health Organization. Physical status: the use and interpretation of anthropometry. Geneva: World Health Organization; 1995.
Ashwell M: Charts based on body mass index and waist-to-height ratio to assess the health risk of obesity: a review. The Open Obesity Journal, 2011,3,78-84.
High prevalence of obesity in severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) requiring invasive mechanical ventilation. Obesity (Silver Spring).
doi:10.1002/oby.22831
Clinical Characteristics of Patients Who Died of Coronavirus Disease 2019 in China Jianfeng Xie
Built With
adobe-illustrator
angular.js
firebase
Try it out
stayhome-covid.web.app | StayHome | StayHome is an online service that provides an easy-to-use health check that evaluates if you are able to coexist with Covid-19 or if you had better stay home.You just need a measuring tape and scales | ['Andrea De Cristofaro', 'Cristian Currò', 'Giuseppe Mallamaci'] | [] | ['adobe-illustrator', 'angular.js', 'firebase'] | 89 |
9,890 | https://devpost.com/software/covid-feel-good | The "Secret Garden" VR experience
CNBC coverage of our work (25 April 2020)
The home page of the web site
The protocol page in the web site
The logo of the project
## Inspiration
Living in the time of the coronavirus means experiencing not only a global health emergency but also
extreme psychological stress
that puts a strain on our identity and our relationships. The coronavirus and the associated quarantine forces us to manage
three different psychological dilemmas simultaneously
:
the stress of the disease,
the disappearance of places,
and the crisis of the sense of community.
In fact, the coronavirus pandemic
is having a significant effect on people’s mental health that requires urgent support
.
On one side,
is currently difficult to find to any form of psychological support
. The social isolation of the quarantine and the economic downturn are limiting the possiblity of accessing to mental health specialist with the risk of a rise in conditions like anxiety, depression and addictions.
On the other side, one of the paradoxes of coronavirus is that
despite being a problem, it can also be a unique opportunity
. In fact, willingly or unwillingly, it forces us to change and manage new situations such as quarantine, close coexistence with children and spouse, lack of relationships, and so on.
This process of change can be dramatically boosted by transformative experiences
, forcing individuals to critically examine and eventually revise their core assumptions and beliefs. Unfortunately, most transformative experiences cannot be planned in advance, but happen suddenly in individuals’ lives, without a prior control on their contents and their effects.
## What it does
This protocol uses the power of virtual reality to provide a
transformative experience
that can help individuals in two ways:
By providing a digital place in which subjects can relax and reflect;
By facilitating a process of critical examination and eventually revision of core assumptions and beliefs.
In particular we used the rules defined for an effective design of Transformative Experiences, to develop the "Secret Garden" VR experience and its weekly protocol you can find in the
Covid Feel Good web site
.
For a long time,
the main barrier to a broad use of VR technology was its cost
. However, now the simplest and cheapest form of VR comprises nothing but a pair of magnifying lenses and a sheet of cardboard or a plastic box. These headsets
sell for 15–30 USD
. and use a standard smartphone as a tracker and display to generate the three-dimensional (3D) environment. Mobile-based VR is particularly suited to a specific VR content that can be very useful to address the coronavirus stress:
360-degree videos
.
360-Degree videos have the power to virtually transport users, immersing them in the video recording, allowing them to actively explore its content and experience the video from any angle.
As recently demonstrated by Li et al.
, these videos have the ability to induce specific emotions characterized by different levels of valence and arousal. More,
as shown by Robertson and colleagues
the neural representations of the part of the 360-degree video presented in VR (the scene within the current field of view) prime the associated representations of the full panoramic environment, facilitating subsequent perceptual judgments. In other words, 360-degree videos generate a dynamic interplay between memory and perception that can be used to improve the features of these cognitive processes and to update their contents.
## How we built it
"The Secret Garden" Virtual Reality immersive experience has been developed through an integrated process involving psychologists, 3D artists, musicians, storytellers and designers. This immersive experience storyboard has been:
written by
wellbeing psychologists
;
converted in a VR experience by
3D specialists
using the
Unreal engine
technology (with complex post-production activity related to VR conversion);
it has been sounded by
musicians
and then dubbed by a
professional dubber
. We used the Augmented Relaxation approach involving deactivating human threat protection system and activating soothing system (see
"Emotion Regulation Systems" theory
;
finally, the protocol has been designed by a team of
clinical and cognitive psychologists
.
## Challenges we ran into
A significant challenge for any psychological intervention during the quarantine is the
ratio between accessibility and efficacy
. Our goal was not to provide a full structured psychological intervention, but to
build the "surgical mask" of mental health support
.
Surgical Masks do NOT provide the wearer with a reliable level of protection against coronavirus (20%) versus the 95/99% of FFP2 and FFP3 masks.
However, they are very effective in protecting others from the wearer’s respiratory emissions, and their use is significantly better than wearing a scarf.
Here, we try to do the same.
The goal of this VR protocol is not to solve complex mental health problems, but rather to reduce the burden of the quarantine by relieving anxiety and stress and improving interpersonal relationships.
Additionally, by facilitating self-reflectiveness and constructive exchange with relevant others, it improves our ability to adapt to the challenges and take advantage of the opportunities offered by the Coronavirus.
## Accomplishments that we're proud of
We have already completed the protocol and the VR experience in English, Italian, Spanish and Catalan
. You can test it now in the
Covid Feel Good web site
. We are now working to produce the experience in other languages and with more contents.
## What we learned
Creating easy tools for mental health is not easy
. In particular it is difficult to
balance the difficulty and the involvement required with the efficacy of the process
. In this view, focusing on clear goals and simplifying as much as possible the technology needed improves the efficacy of the system.
## What's next for COVID Feel Good
We want to start a
multicentric controlled study to verify the efficacy of the presented approach
. Our goal is to reach at least 240 subjects in three months.
More, at the moment COVID Feel Good is a self-help VR experience. The next step is
to add the clinicians in the process, by developing a set of easy VR tools to empower them
.
Built With
unreal-engine
Try it out
www.covidfeelgood.com | COVID Feel Good | An easy self-help virtual reality protocol for coping with the psychological burden of Coronavirus. | ['Brenda Wiederhold', 'Luca Bernardelli', 'Riva Giuseppe'] | [] | ['unreal-engine'] | 90 |
9,890 | https://devpost.com/software/aceso-the-first-feasible-sarscov2-test-trace-network | Track your Virus tests & trace statistics.
Have conversations with a personal AI driven health assistant.
Scan the QR code in order to activate the digital Health ID.
As a government, test lab or other official entity, participate in the network and create automated policies with smart contracts
The Problem.
It is generally known that extensive and widespread testing as well as contact tracing to identify infection chains is crucial for overcoming the SARSCov2 pandemic and gradually returning to normality.
Currently, however, even though the testing itself is not that complicated, the logistics around testing and investigation (infection tracing) of positively tested patients requires Lots of effort and man-hours and goes beyond the borders of available capacities. The related processes are just not automated and digitized. As a consequence, lots of infections are not reflected in statistics making it extremely difficult to cope with the virus as well as spread and isolate it, lockdowns are inevitable in order to stay beyond the intensive care capacity borders.
For contact tracing, the EU has decided to follow the track of controversial software architectures and apps like PEPP-PT, or now "the decentralized" approach DP3T, which cause not only privacy issues but also don't deliver any direct value-add to the users. Another issue is, that tracking without integrated, optimized and automated end2end test rollout management still leads to data lacking behind the real time state and an inefficient value chain.
To sum up, it still lacks a feasible end2end test management and contact tracing platform, connecting governmental institutions, test labs, healthcare facilities and citizens in order to automate the prioritized rollout of test and trace back infection chains after positive test results, without significantly attacking the personality rights of citizens.
How we solve it.
We leverage the properties of the blockchain technology, artificial intelligence and state of the art cryptography to provide an end2end SARSCov2 testing and tracing network.
But how does this work?
The solution consists of three parts:
a permissioned blockchain network for governing test rollouts / logistics and access to personal data in case of infections
a dashboard for government, testing labs and healthcare facilities
a unique Health assistant and "passport"-type health id for citizens in form of a mobile app.
Additionally personal data is encrypted with a hash and stored off-chain in a decentralized cloud-database whilst solely a smart contract contains the key in order to decrypt and display the data to responsible entities in case of infections and direct contact to infected persons.
ACESO Healthpass
Each citizen is provided by the government with a unique digital Health ID which he maintains in an interactive app keeping an anonymized log of relevant events, for instance nearby contact with another person or visiting a public location, for instance a supermarket. Additionally citizens have other value added services like conversations with a chatbot (assistant), or seeing the current load of people on public places. The healthpass collects all the logs anonymously mapped to the non-personal blockchain health pass id and warns the citizen if he behaves too risky , like for instance having lots of contact with other people.
Sensors used for Contact Tracing
Instead of deploying expensive gateways, we believe there is already a mass of options available. For the purpose of not tracking personal data we do not use GPS sensors, but rather diverse options available on public places.
For people to people tracking our app leverages bluetooth technology and available WiFi Networks to register check-ins at public places.
Additionally, at public places, so called sound beacons can be used by registering a signal through the public speakers (for example in supermarkets), we also currently train a neural network, using IBMs Watson Studio, in order to identify different public places based on sound recognition.
As we want to be an open source solution, we want to offer a plug and play sensor interface for easily incorporating additional sensors. The deployment of new sensors has to be voted by the network in the blockchain.
ACESO Test & Trace Network
The blockchain network, at which governments, healthcare facilities and testing laboratories can take part, governs automated policies for data access and testing logistics through smart contracts empowered by Machine Learning and optimization algorithms in order to achieve ideal capacity planning and real time data transfer.
Even though data is anonymized outside the recognized infection chains, it still can serve as a very valuable data source for epidomologic research.
How this will impact the crisis.
ACESO Test & Trace network provides an ideal trade-off between value add for citizens, personal data protection, and effective insights and testing / infection chain management for governments. With the help of this technology, governments can isolate the spread of the virus by real-time capacity planning and logistic automation and quickly deploy and measure new policies whilst citizens stay informed and can stay safe with the help of their personal health assistant. Additionally it could be extended to manage Intensive Care Capacities cross-border through the whole European Union.
What we have achieved during 2 days.
During this weekend we have not only elaborated the idea, but also deployed a full scale blockchain network with already running smart contracts for privacy rules, and an off chain encrypted database as well as created the first fully functional prototype of the ACESO health pass for citizens with an AI-driven chatbot interface and all mentioned sensors for contact tracing.
How we want to continue and what could the solution bring after the crisis.
We want to get in contact with public entities as well as healthcare facilities to establish an open-source project with a longterm goal beyond the testing & tracing use case during the pandemic. With the help of the digital health pass for each EU-citizen we could automate cross-border patient information transfer and inter-country healthcare research knowledge transfer through smart contracts on a self-governed blockchain network.
Built With
fabric
hyperledger
ibm-cloud
ibm-watson
kubernetes
node.js
react
react-native
Try it out
github.com | ACESO - the first feasible SARSCov2 Test & Trace Network | ACESO digitizes and automates rollouts of extensive testing and contact tracing in compliance with personality rights through a self governed blockchain network. | ['Tin Stribor Sohn'] | [] | ['fabric', 'hyperledger', 'ibm-cloud', 'ibm-watson', 'kubernetes', 'node.js', 'react', 'react-native'] | 91 |
9,890 | https://devpost.com/software/project-fonix-ecd8uy | Project Fonix - Rebirth of social life
WHAT INSPIRED US:
We designed our complex solution starting from the idea, that crisis is not necessary a fully destructive phenomenon. We took a positive stance and assumed, that a big change of circumstances always has a potential transformative effect. We asked ourselves, what is the best that can happen when the quarantine ends?
Our answer was that the best that can happen is that businesses and their costumers get enriched by profound human experience of giving and receiving practical help born out of love and gratitude. These emotions are what we want to help dominate these times instead of fear and anxiety.
We came up with the idea of selling vouchers through the web a month ago, when the pandemic in Italy was still roaring, but in Hungary it was still quiet. The idea of adding a strong, emotionally uplifting media, and marketing campaign popped up at our first meeting already, and thus, the Day of the Fonix was born. A concept of a new holyday, a freely timed celebration of each place's reopening ceremony.
Furthermore, we immediately had a sense of creating an option for businesses to be able to tokenize a percentage of their business, thus enabling their costumers to buy a stakes. In some cases this will be the only sufficient solution to actually generate enough money to bridge the gap.
THE PROBLEM WE ADDRESS:
Life has left our social places. The restaurants, clubs, festivals, stadiums, concert halls, bars are empty. Their owners and their staff are struggling financially and emotionally. And their customers are struggling, too. They can’t do what makes us human: get together, enjoy each others’ company. They can’t be present as social beings in public places.
We created Project Fonix to allow customers to bring life back to their beloved social places through our platform. On our website, which is under beta testing now, owners register their businesses and sell future services through automatically generated social posts.
We know there are others who do something similar. What makes Project Fonix different is three main points.
First, we really focus on connecting social places with their audience emotionally. Customers are encouraged and enabled to name their favorite spots they want to see reborn.
Second, businesses themselves can apply for help registering on the site and automatically share their plea in social media.
Third, we went way further then vouchers creating a new ownership scheme through blockchain technology so that customers can buy stakes in their favorite social places.
WHAT WE ACCOMPLISHED:
Our website, technically a multi-vendor marketplace, potentially serving up to hundreds of thousand users and thousands of vendors, is under beta testing. We have the technology and specified the process that provides a blockchain system to buy stakes in businesses worldwide.
We have set up a management structure that makes us a business creating maximum social good for our partners, customers and ourselves instead of just profits.
We mobilised our professional network to bring together elements and modules of quickly applicable technologies, like
woocommerce multivendor marketplace,
PayPal Business account payment gateway,
a reliable authenticating system used by banks, to avoid fraud,
a forecaster AI that analyses vendor uploaded financial data and forecasts the potential in a particular business thus enabling investors to minimize risks and validate token investitions.
Facebook, Intstagram, and youtube integration to boost our campaign.
WHAT NEEDS TO BE DONE:
We need to launch our marketing campaign to pull in vendors and to build the blockchain ecosystem from already developed parts together to see the rebirth of social life.
SEE ALSO: OUR PITCHBOARD PDF
https://drive.google.com/open?id=1DaWQy0oEghseC_Rp4kSGa6aHxCU--v8e
Built With
ai
english
forecaster
hungarian
with-plugins
wordpress
Try it out
dev2.webforu.hu | Project Fonix | Rebirth of social life | ['Dániel Sinkó', 'naszalid', 'Dávid Soós', 'Csanád Lázár', 'heiszlera'] | [] | ['ai', 'english', 'forecaster', 'hungarian', 'with-plugins', 'wordpress'] | 92 |
9,890 | https://devpost.com/software/deepcrisis-qb6etu | Inspiration
The COVID-19 pandemic is holding the world in a medical, social and economic stranglehold and has forced world leaders to take unprecedented measures and actions. These measures help dampen the spread of the virus, however, have devastating impact on the economic activity resulting in possible recession, education gap, socio-economic problems, and high unemployment.
Managing how the lifecycle of a pandemic (COVID-19 likely will not be the last pandemic the world will face) develops is a primary concern of policy makers, industry leaders, multi-stakeholders, and the local and international community. As tools covering early warning, fast response, curve management and back-to-normal approaches become indispensable, so do the availability of the right data and ways to analyze such data.
In the current Covid-19 crisis, many national and international stakeholders notice that there is a lack of coordination and innovation capacity among existing public and private institutions on local, national and international level.
In this light, DeepCrisis is a crisis management operating system that works with the concept of "micro-services" inspired from the software/web/app development/technology sector.
What it does
DeepCrisis is an operating system (OS) for crisis management and navigation; that works with the concept of "micro-services" inspired from the software/web/app development/technology sector.
DeepCrisis consists of a data, AI, modeling, analytics and prediction platform that allow local and global multistakeholders to manage, respond, and fight Covid-19 and any future pandemic or crisis of any type.
Value Propositions:
Two main values for the Multi-stackholders, Users and the Community:
1- Providing an operating system for government and key stakeholders to navigate, manage
highly-used daily services and support to people in every country/national region/city/town to navigate the Coronavirus (and any other future crisis).
2- Providing a digital- and technology-driven platform that will help to engage multistakeholders to respond effectively to the current Coronavirus crisis, enhance collaboration and increase resilience on local, regional and global level to future crises.
DATA IMPACT: Potential Impact beyond Managing & Navigating the Crisis
The COVID-19 pandemic is holding the world in a medical, social and economic stranglehold and has forced world leaders to take unprecedented measures and actions. These measures help dampen the spread of the virus, however, have devastating impact on the economic activity resulting in possible recession and high unemployment.
Managing how the lifecycle of a pandemic (COVID-19 likely will not be the last pandemic the world will face) develops is a primary concern of policy makers and industry leaders. As tools covering early warning, fast response, curve management and back-to-normal approaches become indispensable, so do the availability of the right data and ways to analyze such data.
DeepCrisis: Data & AI Against Any Crisis
Modern digital technology, like mobile phones, data analytics, AI, platforms, and social media offer a unique set of tools to provide and analyze data, to model, communicate and influence behavior and help manage pandemic lifecycles.
For this our super-app/platform could hold a technology (DeepCrisis) that could provide the following data analytics and outcomes benefits:
Alert or monitoring system using mobile DATA.
Info provisioning and sharing DATA relevant to COVID-19 (and future crises).
Multi-systems modeling, data analytics, and prediction of the epidemic/crisis and and outcomes of the action system putted in place.
Systematic DATA analysis in different countries/regions, populations and age groups (allowing for more targeted responses).
Integration of epidemic and economic DATA in order to drive more accurate societal impact analysis.
The Impact of “Digital Innovation Driven Community & Multi-stakeholders"
In the current Covid-19 crisis, many societal stakeholders like citizens, local communities, national and local governments, schools, universities, companies, civil society organizations, NGOs, international organizations, etc. notice that that they have valuable resources to contribute to an effective, equitable and sustainable response to the crisis. But there is a lack of coordination and innovation capacity among existing public institutions on local, national and international level – the natural leaders for public good delivery collaboration.
In this light, the DeepCrisis will have a dedicated digital platform that works with the concept of "micro-services" inspired from the software/web/app development/technology sector.
The platform will have the following features:
1- Allowing the local and global multistakeholders to cordinate, empower, and engage their resources to better act against the crisis.
2- The creating of local, regional, and international digital- and technology-driven virtual communities that will help to respond effectively to the current Coronavirus crisis, enhance collaboration and increase resilience on local, regional and global level to future crises.
How We Built It
The tech stacks are:
For mobile - Native Android and iOS + Native Cross platforms (React Native + Flutter).
Infrastrcuture : AWS and Google Cloud platform.
Backend : NodeJS (ExpressJS) and Golang.
Frontend: ReactJS.
What We Learned
We learnt that this crisis is going to pass. But this solution is ALWAYS needed to tackle any other crisis in the future.
That's why we believe that this solution needs to be built to be prepared in the future.
What's Next for DeepCrisis
This solution will be deployed globally during the Coronavirus era and during any kind of other future crisis.
Built With
javascript | DeepCrisis | The Operating System for Navigating & Managing Future Pandemics and Crises (an open-source solution); | ['Samir Abdel', 'Mohamed Labadi', 'Yacine Bouchrika'] | [] | ['javascript'] | 93 |
9,890 | https://devpost.com/software/support-notes-for-seniors-v2 | home
how it works
send
Track: Health
Inspiration
Two years ago, I was in quarantine for a month due to a disease. During that time, I faced severe loneliness & anxiety, so get-well-soon cards from friends meant the world to me because it showed that I wasn't alone. Knowing that thousands of
senior citizens are now experiencing social isolation
, putting them at risk of many
chronic health conditions
, inspired me to create this project.
What it does
You submit a letter through the site.
The letters then get printed out & sent to meal service centers for the elderly.
The letters are distributed into the food baskets to reach senior citizens.
Proof of Concept
Experience
: I started
Notes for Support
, a website with a very similar premise but targeting an entirely different group (COVID-19 patients & healthcare workers). So far,
I've printed & sent 2,200+ letters across 30 hospitals
in the US.
Connection
: A close family member is a volunteer at one of the senior meal service centers in CA & have confirmed that a program like this would be much welcomed.
Research
According to a fifty year study conducted by Harvard University, human connection is the single most important component of happiness. That's why the concept of sending physical, individual notes is so powerful.
How I built it
I first built a digital prototype out on PowerPoint. I then built the site with node.js & some other programming languages. I've already had experience using this framework so it wasn't a huge challenge, but thinking about the general format was quite difficult.
Impact
There is something so powerful in receiving a personal, physical letter -- it reminds you that you're not alone. This is something that I've experienced myself & through my other project (
Notes for Support
), had thousands of patients & healthcare workers experience as well. Loneliness can kill while a personal letter can save a life.
Challenges, Accomplishments & Lessons
The biggest challenge was definitely the time constraint -- I found out about this hackathon late & would have loved to add more features. However, I'm proud of pulling an all-nighter to finish this project. I learned to just go for it instead of contemplating if you have enough time.
Budget
Raised $1,500 USD for Notes for Support, a partner program.
What's Next + Value after the Crisis
Getting a domain & putting up the site.
Printing & sending the notes received to senior meal service centers!
Even before the pandemic,
senior citizens were always at risk of health issues associated with loneliness
. It is just that shelter-in-place has exacerbated the issue. After the crisis, this program will still be continued to support the elderly population.
Design: lachlanjc.
Built With
html5
javascript
node.js
Try it out
notesforseniors.now.sh | Support Notes for Seniors [V2] | You send a letter to a senior. We'll print & send to meal service centers for distribution. | ['gina c'] | [] | ['html5', 'javascript', 'node.js'] | 94 |
9,890 | https://devpost.com/software/coronavoice | Voice Inflection Spectrogram
Graph of Past Symptoms
Inspiration
As COVID-19 continues to grow rapidly around the globe, the stock of testing supplies begins to dwindle, and hospitals are overloaded with patients. CoronaVoice was created to ease the load on health care workers, while also making it possible to detect potential cases of COVID-19 earlier and more efficiently.
What it does
With CoronaVoice, our ultimate aim is for doctors to be able to remotely monitor patient’s vital signs, and to flag them for testing if necessary. Our website uses a voice inflection test to detect if there are significant differences in a patient’s voice, indicating potential sickness/irregularity in health. The impact on healthcare workers and the spread of the virus will be significant; healthcare workers will be able to test a streamlined patient pool, and COVID-19 can be detected earlier, limiting the spread. In the workplace, this technology can be implemented as a health check for returning employees, protecting everybody's safety by preventing ill individuals from entering the building.
Along with this, CoronaVoice allows users to post daily logs of important medical data, including their body temperature, heart rate, and other symptoms relevant to the Coronavirus. These logs are instantly displayed in a series of graphs for a quick view of how your sickness is progressing. If you ever find yourself in the ER, the data gives doctors an accurate and fast patient history, lessening a burden from their normal patient processing.
How we built it
We built CoronaVoice on a Node.js server hosted by Google App Engine. Registration information, symptom history, and baseline audio recordings are saved in a MongoDB database. For the symptom logging page, Chart JS was used to construct a visual representation of temperatures and heartrate over time, and the command line tool 'Sox' was used to generate spectrograms on the classifier page.
Challenges we ran into
Many of us were unfamiliar to MongoDB, Google App Engine, and many of the other packages we used to make this project. It was a challenge to learn how to use these tools and implement them on the webpage in this short amount of time.
Accomplishments that we're proud of
We're proud of creating a fully-functional CoronaVoice webpage within the span of this hackathon.
What we learned
We've become well-versed in using Google App Engine along with Node.js, HTML, CSS, and MongoDB. It was a rewarding experience to have learned how to successfully integrate all of these different technologies.
What's next for CoronaVoice
We aim to conduct more researching regarding the inflection threshold and to make our flagging system more accurate. In addition, we would like to add a visual feature, so our system can potentially detect visual cues of COVID-19, flagging people even earlier.
Built With
chart.js
css
google-app-engine
html5
librosa
mongodb
node.js
python
socket.io
sox
Try it out
mycovidtracker.ue.r.appspot.com
github.com | CoronaVoice | The spread of Covid-19 has people around the world fearing for their health. Our hack, CoronaVoice, offers an effective triaging method and keeps both doctors and patients informed of general health. | ['Sarah Gu', 'Sanjana Meduri', 'Richard Zhan', 'Neeyanth Kopparapu'] | ['iCreate Challenge'] | ['chart.js', 'css', 'google-app-engine', 'html5', 'librosa', 'mongodb', 'node.js', 'python', 'socket.io', 'sox'] | 95 |
9,890 | https://devpost.com/software/myfy | Inspiration
The COVID-19 Epidemic has completely transformed our lives. It has limited our abilities to do the things we love to do and placed a restraint on the places we can go. With people having constant fears about going outside the home for basic necessities such as grocery shopping, we knew there was a solution that would mitigate this risk. Along with this, we especially saw this prevalent in our community. This is where MYFY comes in!
What it does
MYFY is a web platform that enables safe travel for individuals both during and after the COVID-19 epidemic. It allows users to search for grocery items that they need and the application presents the user with stores near them that have the items in stock, prioritizing stores that are less densely populated to help YOU socially distance yourself. MYFY also gives you opportunities to help your community during these times. The elderly and immunocompromised are having the hardest time now as they cannot risk to go out and obtain essential goods. MYFY will automatically match you with members of your community that can assist you and will allow you to easily help in gathering essential items such as groceries for other people.
How we built it
To build the web platform, we used React JS, Redux, and Material UI for the crux of the project. For the backend of our project, our project takes advantage of Firebase Cloud Firestore. MYFY is highly data driven and the specific libraries are the Square API, Target API, Wholefood API, etc, along with the CDC Heatmap Dataset.
Challenges we ran into
One of the biggest challenges that we ran into our project would definitely be the communication aspect being with COVID-19, conventional face-to-face communication was not possible. This is where efficiently using version/source control software came in handy. Some of the other challenges we faced were API integration making it efficiently streamlining these sources of data into our application while also keeping the UI clean and simple.
Accomplishments that we're proud of
One thing we are very proud of is that we were able to come together as a team and complete a lot of work. Not only did we develop a working demo of our project, we were able to create an amazing UI and a video that accurately portrays our project. Most of all, we are most proud of being able to create an application that not only we will use but something that individuals in our community will also benefit from and use.
What we learned
We learned a ton of stuff when working on MYFY! This ranged from learning to effectively use GitHub for collaboration across our team and using various API integrations in a streamlined approach. Aside from the technical aspects, we learned a lot about video production and editing and were able to improve and gain new skills.
What's next for MYFY
The sky's the limit. We plan to continue to build out this application and work on our machine learning algorithm to effectively give suggestions to our users. One aspect we plan to expand on greatly is our volunteering feature. We want everyone in our community to participate in this and desire to expand this feature to help as many people as possible even after this lockdown and quarantine has finished.
Built With
cdc-dataset
javascript
material-ui
python
react
react-native
redux
square
target-api
tensorflow
walmart-open
whole-food-api
Try it out
github.com | MYFY | Smart Insights to Ensure Your Safety | ['Nithin Anumala'] | [] | ['cdc-dataset', 'javascript', 'material-ui', 'python', 'react', 'react-native', 'redux', 'square', 'target-api', 'tensorflow', 'walmart-open', 'whole-food-api'] | 96 |
9,890 | https://devpost.com/software/carbonkarma | Popup
Dashboard
Annotations
CarbonKarma is a browser extension that is designed to inform users of the carbon emissions that are tied to the production, packaging, and transport of things they buy online. We annotate Amazon products with the carbon emissions so that shoppers are informed of how their shopping impacts the environment.
Inspiration
Almost everyone uses Amazon these days to shop for products online. What many people do not think about, however, is the impact of their shopping on the environment. This has caused a 15% annual growth in e-commerce, and with that, many more boxes and packaging waste used in shipping. For EarthxHack, we decided to build a Chrome Extension that puts the environmental cost in the forefront of the user's mind as they shop.
What it does
CarbonKarma integrates seamlessly with a user's shopping experience. It displays the carbon cost alongside the price, so the user sees exactly how much cost on the environment that this item has. Shoppers can also click into the icon in the top right, which shows the breakdown of the cost, including Manufacturing, Shipping, and Packaging. There is also a carbon equivalent conversion to common items like number of miles driven, burgers eaten, and hours used by a lightbulb. As shoppers add items to their cart, they are also added to our CarbonKarma dashboard. The dashboard shows the same carbon cost information, but now also shows ways to donate to charities to offset the carbon. We included highly vetted charities such as Cool Earth, Environmental Defense Fund, and National Geographic. After donating to these charities, the user can see their carbon offsets over time shown at the top of the dashboard!
How we built it
We built this project using the framework for Google Chrome extensions.
Video link:
https://youtu.be/1-DsUATPh0c
Challenges we ran into
It was sometimes difficult to use plain JavaScript without any libraries to achieve the functionality we wanted.
Accomplishments that we're proud of
We have a functional Chrome Extension that anyone can use and get a better sense of their carbon emissions from shopping!
What we learned
We learned how to make a Chrome Extensions and also a lot about how much we impact the environment when shopping online.
What's next for CarbonKarma
We want to add more detailed breakdowns of the product manufacturing!
Built With
bootstrap
chrome
javascript
Try it out
github.com | CarbonKarma | Offset your carbon footprint by being more aware of how you shop online. | ['Rashid Lasker', 'Aaron Gu', 'Brian Yu'] | [] | ['bootstrap', 'chrome', 'javascript'] | 97 |
9,890 | https://devpost.com/software/sequoia-tgnryh | Provider opening screen
Enter triage
Triage shows user text messages collected through SMS
Scrolling down
Language on provider side is adjustable
Translated to mandarin
Scrolling down
Back to english, select a triage level
Submit
Triage decision has been submitted and sent back to user
Click to receive next user in the queue
Enter resource log
Log hospital resources
User text messages - submit zip code
User text messages - questionnaire
User text messages - describe symptoms in detail
Triage message returned to user - lists 3 hospitals of the appropriate care level
Inspiration
During the COVID-19 pandemic, a key message has been to "flatten the curve." In this strategy, it is necessary to shift patient populations visiting hospitals to prevent the healthcare system from being overwhelmed. However, how do you "flatten the curve" when you exhibit COVID-19 symptoms? Although there is has been a global effort to combat the pandemic, COVID-19 is unfortunately still able to overwhelm the health care system. As the pandemic worsens, people are going to the wrong health centers - going to the emergency department in place of self-isolating for mild symptoms - and overloading health providers to a degree that is exponential in severity.
We found that this issue - the overloading of hospitals due to COVID-19, is a huge problem, one that could be solved by a triage system supported by remote providers who are located a distance from pandemic hotspots. There is a great need for physician-made decisions to triage patients and direct them to the appropriate level health center, and a system offering communication between a user and provider through remote means would solve this, both alleviating the load of providers at the front lines of the pandemic, and preventing continued overloading of the system by patients coming to hospitals unnecessarily.
What it does
Sequoia is a two-way system between a user and health provider. A user experiencing COVID-19 symptoms is able to provide details regarding COVID-19-related symptoms via SMS, send their messages to a triage, and receive a recommendation from a remote physician on next steps, which include whether to seek testing, see a specific level (1-4) of hospital, check in again after a marked number of hours, or stay at home. For recommendations involving testing locations or hospital levels to be sought out, our system texts the user a list of the 3 nearest hospitals at the appropriate triage level that have availability. As a result, Sequoia allows remote providers to distribute the load for hospitals already overloaded by the pandemic. This system is highly accessible and easy to use for users, who communicate via SMS, and providers, who triage users in a web app.
How we built it
Sequoia utilizes Twilio for programmatic SMS and Flask / waitress for a web server, running on a DigitalOcean droplet. To support large volumes of users, Sequoia uses a queue-based system to match a specific provider with a user to triage, and a secondary producer-consumer thread system for off-main-thread querying of web-based GeoJSON APIs. In addition, Sequoia uses an extensible and adaptable weighting system to match each user with a health care center, based on available resources, distance, and care level.
Challenges we ran into
Some initial challenges we faced included finding a list of hospitals spanning the nation that were ranked by triage level and marked by zip code. We were able to solve this by asking experts, who directed us towards hospital records in the CMS.
Accomplishments that we're proud of
We are proud of both sides of the two-way communication line! User data is sent to the electronic triage with great ease and speed, making for a solution that would benefit both users and providers during any setting with hospital overloading.
We are also proud of receiving positive feedback from experts in medicine, informatics, medical technology, and engineering. We interviewed a diverse group of people for feedback and were pleased to hear that we chose a problem with a great need, and developed a solution that efficiently combined medicine and technology.
What we learned
We learned how to implement the Twilio API, a powerful tool that brings great accessibility to users. We learned much more about hospital systems, before and after the pandemic, hospital triaging, and the COVID-19 pandemic at large.
What's next for Sequoia
Next, there are several add-ons that would enhance adoptability of our service. These include verification of physician licensing within each state or across state lines and multi-provider verification for each triage decision.
Built With
digitalocean
flask
geojson
python
twilio
waitress
Try it out
159.89.236.70
github.com | Sequoia | An SMS-based platform made to help hospitals handle the burden of exponential patient influx by remotely connecting users to healthcare providers for triage | ['Kevin Li', 'Rohit Kumar', 'Lily Xu', 'John Gibson', 'Aadit Shah', 'Ashwin Leo'] | ['Best Project Demo on Devpost', 'Highlighted Project', 'VINCITORE CATEGORIA: INFORMAZIONE (100€)'] | ['digitalocean', 'flask', 'geojson', 'python', 'twilio', 'waitress'] | 98 |
9,890 | https://devpost.com/software/covid-impact | GIF
Apart from university, I run a fintech startup with several close friends, we were interviewing dozens of businesses when Covid-19 was just starting to spread in Canada, we kept hearing the same things from local business owners like:
“How am I supposed to pay my rent?” And
“My revenue is gone down the drain, I lost everything”
People were watching the news every day, waiting to see what’s gonna happen next
wondering when they could reopen shop?
COVID-19 is impacting countless small businesses across the world, it was clear that we all face the same uncertainty.
I got on a virtual call with my team, and we asked ourselves what can we do to help?
After an intense sprint of non-stop coding we put together CovidImpact, an open-source Small Business Care Package which features:
A curated list of programs to help businesses get the support they need to survive through the crisis.
Real-time news relevant to Canadian small businesses.
And a Simulation tool to assess how their business may be affected across various scenarios.
We made this project open source and were blown away by the response. Within a day of launching messages started pouring in, "hey I’m a university professor, let me help with analyzing the impact", "I have an analytics company, let’s tie it in", "hey, can I add a chatbot."
It was nothing short of heart-warming, seeing how, though we were all socially distancing, the entire community was converging. To see more details, check out the video presentation for this hack!
Built With
ibm-watson
javascript
predictive
sentiment-analysis-online
serverless
vue
Try it out
covidimpact.ca
github.com | CovidImpact | A Smart Care Package - Immunizing Businesses | ['Bolat Khojayev', 'Ali Serag El-din'] | [] | ['ibm-watson', 'javascript', 'predictive', 'sentiment-analysis-online', 'serverless', 'vue'] | 99 |
9,890 | https://devpost.com/software/corona-bay | Inspiration
We initially wanted to help take off the load from healthcare workers by using neural networks to make it easier to diagnose COV1D-19 from CT scans of lungs. While we were working on Corona Bay, we realized that we could also help the general public by giving them the information and resources to understand the situation of the pandemic and self-diagnose for coronavirus.
What it does
Corona Bay has 4 main features.
1) Detection: a neural network model we created and trained from scratch that predicts if a patient has COVID-19 based on CT scans of their lungs. This was created and trained using Tensorflow and has 98.09% test accuracy.
2) Statistics: a dynamic map that lets users hover over each country to get the stats of the pandemic situation in all of the countries. This information is always updating and can help people understand where the situation is worse and how they might need to act based on the situation in their country.
3) News: a constantly updating feed of coronavirus-related news. This allows users to stay up-to-date on the latest information about the pandemic while staying on one site.
4) Symptoms: a checklist of symptoms that allows users to get a good idea what they need to look out for and what actions they need to take if they have any of those symptoms.
How I built it
We used React.js for most of the project, with Python 3 and Tensorflow Keras API for the machine learning model.
Challenges I ran into
Integrating Tensorflow in Python to the web app created so many issues. It was really hard to just migrate to JavaScript, and then we got memory leaks when trying to use it. Also, React.js doesn't make it easy to style components or have an overall layout, so it was hard to make the UI presentable. The datasource we used for the statistics page actually went down right when we were recording the video! It took a lot of effort and determination to conquer these challenges.
Accomplishments that I'm proud of
We are very proud that we were able to get all 4 features working well and that we were able to finally get rid of the errors. The ML model worked surprisingly well with a small dataset of less than 1000 images. We are also very proud that the interactive map is easy to use and visually shows the effect of the pandemic.
What I learned
We learned how to use machine learning to solve real-world problems with limited resources. We also learned how to use React.js to make a fast web app that can keep up with users' needs.
What's next for Corona Bay
We only had a short time to develop this project, so we didn't have the time to style everything well. We were mainly focused on getting the best features out there, which was especially hard due to the incredible number of errors that came up in the process. We are looking to shape the web app so that it can be used by the public and refine the minor styling details so that we can present our great features in the best way.
Built With
mongodb
react.js
tensorflow
Try it out
github.com
coronabay.org | Corona Bay | Stay home. Stay informed. | ['Shamith Pasula', 'Om Chaudhary'] | [] | ['mongodb', 'react.js', 'tensorflow'] | 100 |
9,890 | https://devpost.com/software/psychological-first-aid-online | Inspiration
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
Our Plan going forward/Business Plan
Built With
libraries | Helpers | Helpers Online | ['Prashant Jain', 'Anirban Biswas'] | [] | ['libraries'] | 101 |
9,890 | https://devpost.com/software/covsense-1j2ur7 | Logo
Dark theme screens
Light theme screens
Inspiration
According to medical journal "The Lancet",
many confirmed cases of Covid-19 are asymptomatic
. The virus therefore spreads quickly across the world with the help of people who unwittingly spread the disease. In order to solve this problem, an effective solution must be discovered that can quarantine targeted cases before they can infect others around them.
Solving the problem
The solution for this is based on the architecture of TraceTogether (first large scale deployment of a contact tracing app in Singapore), but uses more means of communication (Bluetooth, BluetoothLE, Wi-Fi, ultrasonic modem) to detect proximity to another person. Contact tracing is an established form of finding and isolating confirmed and potential cases of the virus.
It can be made scalable because data can be communicated over multiple channels of communication in case one channel is not available. Furthermore the solution can be embedded in existing apps as an opt-in library to scale the adoption. Lastly, in 2 months time an initiative can be started to create a publicly available key/value database with anonymized keys that would allow all contact tracing apps to share data and therefore expand their reach.
Working
After you sign in, you get an OTP generated using Firebase Phone Authentication. After you login, the application starts a background service that constantly publishes and receives the Firestore Database UIDs, by using the Nearby Messages API from Google. When two devices are in close proximity (approximately 4 metres to 5 metres for Bluetooth + Sonar) their meetup is registered in Firestore.
In the logged in screen, you can choose your current health status and press the button. This updates your health status in the database. Using Firestore Cloud Messages, there is a JavaScript function that triggers when this update happens and sends a push notification to the users that you have interacted with.
Technical components
Android codebase in Java
Firebase Authentication (authenticate requests)
Firestore (database)
Nearby Messages API (contact tracing)
Maps (displaying point of contact)
Firebase messaging service (push notifications)
Firebase Functions (serverless code)
Built with Android Studio version 3.6.3
What's next for CovSense
Remove the need to authenticate with a telephone number. Anyone should be able to install the app without sharing personal information. A unique anonymous device ID can be created (instead of a user account) that can be shared with other devices and be stored in a public database.
Currently the demo is made for Android devices. It needs to be expanded for cross-platform use (both Android / iOS). The Nearby Messages API that is used for contact tracing can be implemented on iOS as well.
Make the database public to be used by other contact tracing apps, or contribute to an existing global database for contact tracing. The simplicity of a NoSQL key/value database can increase the speed of lookups if we expand the database to be made available to other apps. API documentation needs to be created to so that it's easy for other developers to get access to the database.
With more resources and developers in the long term it's better to shift away from using Google's database and APIs, because many people inherently do not trust an application with a Big Tech underlying infrastructure. This has been shown by the responses to TraceTogether, which people are wary of since it's a governmental institution that people perceive as a untrustworthy. In order to facilitate this shift software needs to be developed that communicates data over Bluetooth classic, BluetoothLE and WiFi. And a public database (preferably from a trusted supplier) needs to be set up.
Documentation and installation guide
For detailed guide on how to run this project, please visit
README.md
in our GitHub repo -
https://github.com/saivittalb/covsense
Built With
android
android-studio
firebase
firestore
java
javascript
Try it out
firebasestorage.googleapis.com
github.com | CovSense | An Android based contact tracing app which enables people to self-isolate if they have been in close proximity to someone tested positive for COVID-19. | ['Sai Vittal Battula', 'Yandapalli Sailahar', 'Pranay Bandaru', 'Shanmukh Sreenivas'] | ['Community Choice'] | ['android', 'android-studio', 'firebase', 'firestore', 'java', 'javascript'] | 102 |
9,890 | https://devpost.com/software/data-structure-generator | This is the view of the generator after running the jar file.
After all parameters have been selected or input, the generator outputs the code.
If the code is too long for the output box, click the "Expand" button for a full view.
Clicking the "Export as .txt file" button will write a file with the generated code.
The output box notifies you when the file is written, and where it can be found on your computer.
You can create a lab for students to become more familiar with the selected data structure by clicking the "Generate Custom Lab" button.
The output box notifies you when the file is written, and where it can be found on your computer.
Inspiration
While learning about array lists last year the first step in a lab we were given was to create an ArrayList with some given data and it was a pain typing everything out with the right formatting. This tool is a result of that twinge of annoyance. When I got to thinking about what else could be done with custom data I realized our application could be used in an educational context to to create custom labs.
What it does
This tool can be used to convert data into data structures for easy copy and paste to avoid the hassle of typing. Simply input text, select parameters, and press the "Generate" button. It can also be used to create custom Java labs based on the input data and parameters. Computer Science teachers can easily create multiple forms of randomized Java labs to prevent cheating and file sharing among students by pressing the "Generate Custom Lab" button. We also coded sample answers to labs for each data structure available in the "Sample Answers" folder in the GitHub repo.
How we built it
We made a Java Swing GUI that references two classes we created. One class converts the input text and parameters into a data structure and the other uses those same inputs to create custom randomized Java labs.
Challenges we ran into
While creating the custom labs we had to make sure the names of the files weren't the same as previously generated versions. Additionally, completing the GUI was challenging because we were unfamiliar with how to do so.
Accomplishments that we are proud of
A completed GUI with functional buttons, drop-down menus, and input fields
What We learned
Java GUI and file creation/manipulation
What's next for Data Structure Generator
We would like to expand the types of Java Data Structures that our program can handle and add compatibility for Data Structures in other programming languages such as Python.
Built With
java
swing
Try it out
github.com | Data Structure Generator | Convert data into data structures to save the hassle of typing and formatting | Educational tool for teachers that easily generates custom Java Labs based on custom inputs to prevent cheating | ['Dhruv Batra', 'Nathan Goldberg'] | [] | ['java', 'swing'] | 103 |
9,890 | https://devpost.com/software/covid19-real-time-tracker-1ileth | Main Page
Inspiration
Everyone is aware of covid19 and one day one of my friend came and ask do you know about any new cases in our country I said you can check on google he said but as you are developer so you need to build an app which can help to others to track real-time cases with some graphics at that point I started building wireframe and prototype for the web app.
What it does
Users can use my web app to check real-time covid19 cases with various charts and tables.
How I built it
I used Angular.
Challenges I ran into
Collecting the data and checking it that data is correct or not then implementing it in graps as data is not coming in proper formate so I have to manipulate data in formate which charts expect.
Accomplishments that I'm proud of
I got appreciation from senior developers and users who use this app are very happy with UI.
What I learned
I learn how to deal with data.
What's next for Covid19 - Real-Time Tracker
I am working on distance calculation so the user can check the distance from nearby corona positive case
Built With
angular.js
css
html
javascript
typescript
Try it out
covid19-himanshusharma.web.app | Covid19 - Real Time Tracker | Stop worrying about Corona cases here is my real time web app to help you track COVID19 cases | ['Himanshu Sharma'] | [] | ['angular.js', 'css', 'html', 'javascript', 'typescript'] | 104 |
9,890 | https://devpost.com/software/behale | Inspiration
bnn
What it does
How I built it
Challenges I ran into
Accomplishments that I'm proud of
What I learned
What's next for
Built With
angular.js
bootstrap
css3
firebase
html5
ionic | nm | bn | ['Michael Quaynor'] | [] | ['angular.js', 'bootstrap', 'css3', 'firebase', 'html5', 'ionic'] | 105 |
9,890 | https://devpost.com/software/corona_detect_app | Corona_Detect_App
Corona_Detect_App is a command line based app with json scraping code to detect current covid effected patients in all over the world.Corona_Detect_App is a simple project initiative from me in
Hackerearth : Covid19 Hackathon
which is dedicated to the covid crisis.
Installation
pip3 install -r requirements.txt
Extracted Data
CovidScraper
is a
Spider inside the Corona_Detect_App
, which will show table data. You can run it using
scrapy runspider CovidScraper.py
:
>> scrapy runspider CovidScraper.py
2020-04-12 19:19:09 [protego] DEBUG: Rule at line 16 without any user agent to enforce it on.
2020-04-12 19:19:10 [scrapy.core.engine] DEBUG: Crawled (200) <GET https://www.worldometers.info/coronavirus/#countries> (referer: None)
[ Country,Other TotalCases NewCases TotalDeaths NewDeaths TotalRecovered ActiveCases Serious,Critical Tot Cases/1M pop Deaths/1M pop TotalTests Tests/ 1M pop
0 World 1796414 +16,671 110030.0 +1,251 412102.0 1274282 50533.0 230.00 14.1 NaN NaN
1 USA 533470 +591 20595.0 +18 30523.0 482352 11471.0 1612.00 62.0 2696143.0 8145.0
2 Spain 166019 +2,992 16972.0 +366 62391.0 86656 7371.0 3551.00 363.0 355000.0 7593.0
3 Italy 152271 NaN 19468.0 NaN 32534.0 100269 3381.0 2518.00 322.0 963473.0 15935.0
4 France 129654 NaN 13832.0 NaN 26391.0 89431 6883.0 1986.00 212.0 333807.0 5114.0
5 Germany 125452 NaN 2871.0 NaN 57400.0 65181 4895.0 1497.00 34.0 1317887.0
CovidjsonScraper
is a full command line based script inside
Corona_Detect_App
, which will show data in json format , You can run it using
python3 CovidjsonScraper.py
:
>> python3 CovidjsonScraper.py
╔
:::::::: :::::::: ::: ::: ::::::::::: ::::::::: ::: :::: ::: ::: ::: ::: ::: ::::::::: :::::::::: :::::::::
:+: :+: :+: :+: :+: :+: :+: :+: :+: :+: :+: :+:+: :+: :+: :+: :+: :+: :+: :+: :+: :+: :+:
+:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ :+:+:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+ +:+
+#+ +#+ +:+ +#+ +:+ +#+ +#+ +:+ +#++:++#++: +#+ +:+ +#+ +#++:++#++: +#+ +#++: +#+ +#++:++# +#++:++#:
+#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+#+# +#+ +#+ +#+ +#+ +#+ +#+ +#+ +#+
#+# #+# #+# #+# #+#+#+# #+# #+# #+# #+# #+# #+# #+#+# #+# #+# #+# #+# #+# #+# #+# #+#
######## ######## ### ########### ######### ### ### ### #### ### ### ########## ### ######### ########## ### ### v1.0
>>> Press 1 : Covid Data according to gender in JSON file
>>> Press 2 : Covid Data from districts of India in a JSON File
>>> 1
File will be saved as
GenderWise_StateWise.json
in current
Corona_Detect_App
, You can see it by using
cat GenderWise_StateWise.json
:
{
"State_ID": "1",
"NIC_State_ID": "1",
"male": "79",
"total": "118",
"female": "32",
"State_Name": "Jammu & Kashmir"
},
{
"State_ID": "2",
"NIC_State_ID": "2",
"male": "2",
"total": "13",
"female": "1",
"State_Name": "Himachal Pradesh"
},
Built With
python
Try it out
ankitdobhal.github.io
github.com | Corona_Detect_App | Command LIne Corona Detect app with json spider | ['Ankit Dobhal'] | [] | ['python'] | 106 |
9,890 | https://devpost.com/software/mapchat-aynwu8 | Home Page
Home Page
Contacts Page
Friends Page
Chat Page
For personal Use:
MapChat - Android App
MapChat help users to connect with anyone from around the globe.
Installation
clone this project to your local machine and import it into android studio.
https://github.com/sarimk80/MapChat.git
Configuration
This project is dependent on firebase so to run it you have to add your project to firebase console.
Firebase Setup
download the google-services.json file and add it to the module (app-level) directory of your app.
Create an account on
MapBox map
to get the access-token-key
Permissions
The app only requires two permissions.
Network access
The location from the GPS
Libraries
Firebase (Auth and database)
ViewModel
Koin (for dependency injection)
Jetpack navigation component
rxPermission
Coroutines
MapBox map
Glide
Application Flow
Built With
kotlin
Try it out
github.com | MapChat | Connect with your friends and family in this hard time and check if the person is in quarantine or not. | ['sarim khan'] | [] | ['kotlin'] | 107 |
9,890 | https://devpost.com/software/covnatic-covid-19-ai-diagnosis-platform | Landing Page
Login Page
Segmentation of Infected Areas in a CT Scan
Check Suspects using Unique Identification Number (New Suspect)
Check Suspects using Unique Identification Number (Old Suspect)
Suspect Data Entry
COVID-19 Suspect Detector
Upload Chest X-ray
Result: COVID-19 Negative
Upload CT Scan
Result: Suspected COVID-19
Realtime Dashboard
Realtime Dashboard
Realtime Dashboard
View all the Suspects (Keep and track the progress of suspects)
Suspect Details View
Automated Segmentation of the infected areas inside CT Scans caused by Novel Coronavirus
Process flow of locating the affected areas
U-net (VGG weights) architecture for locating the affected areas
Segmentation Results
Detected COVID-19 Positive
Detected Normal
Detected COVID-19 Positive
Detected COVID-19 Positive
GIF
Located infected areas inside lungs caused by the Novel Coronavirus
Endorsement from Govt. Of Telengana, Hyderabad, India
Endorsement from Govt. Of Telengana, Hyderabad, India
Generate Report: COVID-19 Possibility
Generate Report: Normal Case
Generated PDF Report
Inspiration
The total number of Coronavirus cases is
2,661,506 worldwide
(Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and rapid testing is the only option to battle with the virus. McMarvin took this opportunity as a challenge and built AI Solution to provide a tool to our doctors. McMarvin is a DeepTech startup in medical artificial intelligence using AI technologies to develop tools for better patient care, quality control, health management, and scientific research.
There is a current epidemic in the world due to the Novel Coronavirus and here
there are limited testing kits for RT-PCR and Lab testing
. There have been reports that kits are showing variations in their results and false positives are heavily increasing.
Early detection using Chest CT can be an alternative to detect the COVID-19 suspects.
For this reason, our team worked day and night to develop an application which can help radiologist and doctors by automatically detect and locate the infected areas inside the lungs using medical scan i.e. chest CT scans.
The inspirations are as below:
1. Limited kit-based testings due to limited resources
2. RT-PCR is not as much as accurate in many countries (recently in India)
3. RT-PCR test can’t exactly locate the infections inside the lungs
AI-based medical imaging screening assessment is seen as one of the promising techniques that might lift some of the heavyweights of the doctors’ shoulders.
What it does
Our COVID-19 AI diagnosis platform is a fully secured cloud based application to detect COVID-19 patients using chest X-ray and CT Scans. Our solution has a centralized Database (like a mini-EHR) for Corona suspects and patients. Each and every record will be saved in the database (hospital wise).
Following are the features of our product:
Artificial Intelligence to screen suspects using CT Scans and Chest X-Rays.
AI-based detection and
segmentation & localization of infected areas inside the lungs
in chest CT.
Smart Analytics Dashboard
(Hospital Wise) to view all the updated screening details.
Centralized database (only for COVID-19 suspects) to
keep the record of suspects and track their progress
after every time they get screened.
PDF Reports,
DICOM Supports
, Guidelines, Documentation, Customer Support, etc.
Fully secured platform
(Both On-Premise and Cloud)
with the privacy policy under healthcare data guidelines.
Get Report within Seconds
Our main objective is to provide a research-oriented tool to alleviate the pressure from doctors and assist them using AI-enabled smart analytics platform so they can
“SAVE TIME”
and
“SAVE LIVES”
in the critical stages (Stage-3 or 4).
Followings are the benefits:
1. Real-world data on risks and benefits:
The use of routinely collected data from suspect/patient allows assessment of the benefits and risks of different medical treatments, as well as the relative effectiveness of medicines in the real world.
2. Studies can be carried out quickly:
Studies based on real-world data (RWD) are faster to conduct than randomized controlled trials (RCTs). The Novel Coronavirus infected patients’ data will help in the research and upcoming such outbreak in the future.
3. Speed and Time:
One of the major advantages of the AI-system is speed. More conventional methods can take longer to process due to the increase in demand. However, with the AI application, radiologists can identify and prioritize the suspects.
How we built it
Our solution is built using the following major technologies:
1. Deep Learning and Computer Vision
2. Cloud Services (Azure in this case)
3. Microservices (Flask in this case)
4. DESKTOP GUIs like Tkinter
5. Docker and Kubernetes
6. JavaScript for the frontend features
7. DICOM APIs
I will be breaking the complete solution into the following steps:
1. Data Preparation:
We collected more than 2000 medical scans i.e. chest CT and X-rays of 500+ COVID-19 suspects around the European countries and from open source radiology data platform. We then performed validation and labeling of CT findings with the help of advisors and domain experts who are doctors with 20+ experience. You can get more information in team section on our site. After carefully data-preprocessing and labeling, we moved to model preparation.
2. Model Development:
We built several algorithms for testing our model. We started with CNN for classifier and checked the score in different metrics because creating a COVID-19 classifier is not an easy task because of variations that can cause bias while giving the results. We then used U-net for segmentation and got a very impressive accuracy and got a good IoU metrics score. For the detection of COVID-19 suspects, we have used a CNN architecture and for segmentation we have used U-net architecture. We have achieved 94% accuracy on training dataset and 89.4% on test data. For false positive and other metrics, please go through our files.
3. Deployment:
After training the model and validating with our doctors, we prepared our solutions in two different formats i.e. cloud-based solution and on-premise solution. We are using EC-2 instance on AWS for our cloud-based solution.
Our platform will only help and not replace the healthcare professionals so they can make quick decisions in critical situations.
Challenges we ran into
There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself.
One of the challenge is “Validated data” from different demographics and CT machines.
Due to the lockdown in the country, we are not able to meet and discuss it with several other radiologists. We are working virtually to build innovative solutions but as of now, we are having very limited resources.
Accomplishments that we're proud of
We are in regular touch with the State Government (Telangana, Hyderabad Government). Our team presented the project to the Health Minister Office and helping them in stage-3 and 4.
Following accomplishments we are proud of:
1. 1 Patent (IP) filled
2. 2 research paper
3. Partnership with several startups
4. In touch with several doctors who are working with COVID-19 patients. Also discussing with Research Institutes for R&D
What we learned
Learning is a continuous process. Our team learnt
"the art of working in lockdown"
. We worked virtually to develop this application to help our government and people. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government.
What's next for M-VIC19: McMarvin Vision Imaging for COVID19
Our research is still going on and our solution is now endorsed by
the Health Ministry of Telangana
. We have presented our project to
the government of Telangana for a clinical trail
. So the next thing is that we are looking for trail with hospitals and research Institutes. On the solution side, we are adding more labeled data under the supervision of Doctors who are working with COVID-19 patients in India. Features like
Bio-metric verification, Trigger mechanism to send notification to patients and command room
, etc are under consideration. There is always scope of improvement and AI is the technology which learns on top of data. Overall, we are dedicated to take this solution into real world production for our doctors or CT and X-rays manufacturers so they can use it to fight with the deadly virus.
Built With
amazon-web-services
flask
google-cloud
javascript
keras
nvidia
opencv
python
sqlite
tensorflow
Try it out
m-vic19.com | M-VIC19: McMarvin Vision Imaging for COVID19 | M-VIC19 is an AI Diagnosis platform is to help hospitals screen suspects and automatically locate the infected areas inside the lungs caused by the Novel Coronavirus using chest radiographs. | [] | ['1st Place Overall Winners', 'Third Place - Donation to cause or non-profit organization involved in fighting the COVID crisis'] | ['amazon-web-services', 'flask', 'google-cloud', 'javascript', 'keras', 'nvidia', 'opencv', 'python', 'sqlite', 'tensorflow'] | 108 |
9,890 | https://devpost.com/software/masked-ai-masks-detection-and-recognition | Platform Snapshot
Input Video
Model Processing
Model Processing
Output Video Saved
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Output Video Snapshot
Inspiration
The total number of Coronavirus cases is 5,104,902 worldwide (Source: World o Meters). The cases are increasing day by day and the curve is not ready to flatten, that’s really sad!! Right now the virus is in the community-transmission stage and taking preventive measures is the only option to flatten the curve. Face Masks Are Crucial Now in the Battle Against COVID-19 to stop community-based transmission. But we are humans and lazy by nature. We are not used to wear masks when we go out in public places. One of the biggest challenges is “People not wearing masks at public places and violating the order issued by the government or local administration.” That is the main reason, we built this solution to monitor people in public places by Drones, CCTVs, IP cameras, etc, and detect people with or without face masks. Police and officials are working day and night but manual surveillance is not enough to identify people who are violating rules & regulations. Our objective was to create a solution that provides less human-based surveillance to detect people who are not using masks in public places. An automated AI system can reduce the manual investigations.
What it does
Masked AI is a real-time video analytics solution for human surveillance and face mask identification. Our main feature is to identify people with masks that are advised by the government. Our solution is easy to deploy in Drones and CCTVs to “see that really matters” in this pandemic situation of the Novel Coronavirus. It has the following features:
1. Human Detection
2. Face Masks Identification (N95, Surgical, and Cloth-based Masks)
3. Identify human with or without mask in real-time
4. Count people each second of the frame
5. Generate alarm to the local authority if not using a mask (Soon in video demo)
It runs entirely on the cloud and does detection in real-time with analysis using graphs.
How we built it
Our solution is built using the following major technologies:
1. Deep Learning and Computer Vision
2. Cloud Services (Azure in this case)
3. Microservices (Flask in this case)
4. JavaScript for the frontend features
5. Embedded technologies
I will be breaking the complete solution into the following steps:
1. Data Preparation:
We collected more than 1000 good quality images of multiple classes of face masks (N95, Surgical, Clothe-based masks). We then performed data-preprocessing and labeled all the images using labeling tools and generated PASCAL VOC and JSON after the labeling.
2. Model Preparation:
We used one of the famous deep learning-based object detection algorithm “YOLO V-3” for our task. Using darknet and Yolo v-3, we trained the model from scratch on 16GB RAM and Tesla K80 powered GPU machine. It took 10 hours to train the model. We saved the model for deploying our solution to the various platforms.
3. Deployment:
After training the model, we built the frontend which is totally client-based using JavaScript and microservice “Flask”. Rather than saving the input videos to our server, we are sending our AI to the client’s place and using Microsoft Azure for the deployment. We are having on-premise and cloud solutions prepared. At the moment, we are on a trail so we can’t provide the link URL.
After building the AI part and frontend, We integrated our solution to the IP and CCTV cameras available in our house and checked the performance of our solution. Our solution works in real-time on video footage with very good accuracy and performance.
Challenges we ran into
There are always a few challenges when you innovate something new. The biggest challenge is “The Novel Coronavirus” itself. For that reason, we can’t go outside the home for the hardware and embedded parts. We are working virtually to build innovative solutions but as of now, we are having very limited resources. We can’t go outside to buy hardware components or IP & CCTV cameras. One more challenge we faced was that we were not able to validate our solution with drones in the early days due to the lockdown but after taking permission from the officials that problem was not a deal anymore.
Accomplishments that we're proud of
Good work brings the appreciation and recognition. We have submitted our research paper in several conferences and international journals (Waiting for the publication). After developing the basic proof-of-concept, We went on to the local government officials and submitted our proposal for a trial to check our solution for better surveillance because the lockdown is near to be lifted. Our team is also participating in several hackathons and tech event virtually to showcase our work.
What we learned
Learning is a continuous process. We mainly work with the AI domain and not with the Drones. The most important thing about this project was “Learning new things”. We learned how to integrate “Masked AI” into Drones and deploy our solution to the cloud. We added embedded skills in our profile and now exploring more features on that part. The other learning part was to take our proof of concept to the local administration for trails. All these “Government Procedures” like writing Research Proposal, Meeting with the Officials, etc was for the first time and we learned several protocols to work with the government.
What's next for Masked AI: Masks Detection and Recognition
We are looking forward to collaborating with local administrative and the government to integrate our solution for drone-based surveillance (that’s currently in trend to monitor internal areas of the cities). Parallel, The improvement of model is the main priority and we are adding “Action Recognition” and “Object Detection” features in our existing solution for even robust and better solution so decision-makers can make ethical decisions as because surveillance using Deep Learning algorithms are always risky (bias and error in judgments).
Built With
azure
darknet
flask
google-cloud
javascript
nvidia
opencv
python
tensorflow
twilio
yolo | Masked AI: AI Solution for Face Mask Identification | Masked AI is a cloud-based AI solution for real-time surveillance that keeps an eye on the human who violates the rule by not using face masks in public places. | [] | [] | ['azure', 'darknet', 'flask', 'google-cloud', 'javascript', 'nvidia', 'opencv', 'python', 'tensorflow', 'twilio', 'yolo'] | 109 |
9,890 | https://devpost.com/software/umid-collaborate-help | Pillars of UMID
Splash Screen
Home & Emergency Listing
Create Emergency
Chat
Login
Website:
www.umid-corona.in
Project Documents & App:
https://drive.google.com/drive/u/1/folders/1H0vg7mzHvCBLHvNXVI_0QDwlkwE591U3
Google Playstore Link:
https://play.google.com/store/apps/details?id=com.umid
LinkedIn:
https://www.linkedin.com/company/35920451/admin/
Inspiration
When the whole world is crippling and the responsibility of fighting COVID-19 only lies in the hands of few individuals/authorities, we wanted to pass on this responsibility in the hands of the common public to collaborate & help each other. We really wanted to increase community engagement and social quotient to let people step up and fight for each other.
Problem you are solving?
Centralization: Does responsibility of fighting Corona only lie in the hands of few individuals.
Emergency Spotting: Can we spot emergencies near-by to our location and help the needy?
Food & Supplies Distribution: Is there a way that we can optimize how the resources/food are being distributed to
the needy, or can we assist organizations/ NGOs with real-time needs of public?
Mental Wellbeing: Human beings are social animals and owing self-isolation aanxiety levels are on the rise.
The solution you bring to the table
Decentralization: UMID being a Peer-to-Peer platform, can enable users to act on the near-by emergencies and provide necessary help.
Emergency Spotting: Any user can raise an emergency by using SOS button and filling up necessary details like category (food, medicine, emotional support). All nearby users will be notified of this emergency as soon as the needy user submit his/her details
• Food & Supplies Distribution: Users can see areas/places where the demand for items/food is coming from, entities like NGOs or restaurants can optimize their resource distribution process, thereby balancing the demand & supply curve.
• Mental Wellbeing: UMID lets its users to chat anonymously to people nearby, thereby avoiding fear of being judged.
What it does
It's a Peer-to-Peer platform where users can collaborate and connect to the people around them to help each other in emergency - food/supplies, mental health & other ad-hoc support.
We are enabling each & every individual to make a difference, a user can spot emergencies in nearby areas with exact location & people can coordinate for food/supplies, any ad-hoc help or emotional support (this issue has been least addressed during the COVID-19 scenarios) with the other users around. Users can also coordinate with nearest Kirana/Medical stores to order items for them to be delivered at your doorstep or coordinate with the shop owner to assign a designated time for pickup.
What’s in for the public?
UMID enables people to chat by clicking on an emergency to coordinate for more details; it also lets users to
chat anonymously for emotional support, you would be talking to a stranger without fear of being judged. We are
also enabling users to talk anonymously to their peers globally, this feature could help us talk to few people and l
lighten our mood if someone is feeling low.
What’s in for the authorities/entities like NGOs?
We are helping people to optimize the resource distribution process, entities like NGOs can track
where the demand of food/supplies is coming from, thereby balancing the demand/supply curve.
What’s in for shopkeepers?
We are encouraging people to support these small entities to order items through our chat, to be delivered at their
door step or decide a predefined time for pickup from the shop, thereby avoiding overcrowding and reduce waiting
time in lines.
How I built it
3 people including me started our journey to make a platform that would run on the principles of decentralization, community engagement and collaboration to enable people to help each other. We started to brainstorm on the ideas of web vs mobile and considering the real life scenarios and ease of using mobile, mobile won.
One thing that was clear from day 1 was that we would be putting equal weightage on user flow, his/her psych and actions & app designs as we would put on building the app architecture.
We started to develop this app on react native but considering the tight timeline and our office commitment, we thought of on boarding a few people, who would work in tandem to develop and design the app. We also started parallel work on our website for us to be able to market the idea (considering google play store and apple store are taking time to approve a new app these days).
We are using GCP suite, firebase as a DB, Google API to tag your location and identifying people near to you, google API to get the shops/stores data, React Native Gifted chat for 1-1 & many-many chat option & D7 sms service for OTP authentication. We are also enabling user to login through FB & gmail directly into the app.
We have create a static website for marketing and are working on creating a dynamic website to replicate apps functionality, will be ready in next 2 days.
Challenges I ran into
Juggling between office hours and working on this initiative (that too initially juggling different hats of programming, designing & social outreach) after 10 hours of office work we are banging our hands on this novel idea to bring people together in these difficult times gets us going.
Considering the current situation, we were very clear to not spend money on product development, convincing people to join the team with just an idea was again a challenge, but as people were on boarded this idea turned into a vision and now i can proudly say that we have a team of 13 with varied experience in programming, machine learning/AI, design, graphics, content & digital marketing.
User acquisition is still a challenge as we are still figuring out on how to position this app so people make it a habit of using this to address the problems we are trying to solve.
Accomplishments that I'm proud of
In spite of these challenges we have been able to develop a native mobile app and a website within just 2 weeks, we have been able to successfully onboard 50 beta testers for their feedback on functional and user flow components of the app. We have also been able to on board 200 users including few NGOs.
We have launched "I want to volunteer" program to onboard people to join us for mass outreach. We have been able to gather around ~200 requests from different areas for people to help in these difficult times.
What I learned
When you start something with uncertainty, vision is the only thing that keeps you going and also lets other people follow you to make it a reality, irrespective of your background or expertise, clear vision can help you traverse the route.
Ideas are never important, the way you execute them is what makes a difference. We have been fortunate enough to have a dedicated team of passionate individuals who really wanted to make a difference.
What's next for UMID - Collaborate & Help
We have launched the app & targeting people from organizations (working professionals) and universities. Considering the demographics of these people we could expect a flat product adoption curve, and these people could be the flag bearer of bringing this new concept to their close associations.
We would also be focusing on expanding the team to include more developers, BD & branding people for us to reduce the go to market time for new versions, and would seek help from mentors to position & brand this product to make it widely accessible to common public.
We are also working on building an AI based chat that gives solutions to your problems, post current crises our plan is to include this AI bot along with clinical approved psychologists to help users overcome anxiety help them overcome any challenges related to mental health.
Built With
css
firebase
gifted
google-compute-engine
google-geocoding
google-maps
html5
react-native
Try it out
www.umid-corona.in | UMID - Collaborate & Help | UMID is a peer to peer platform promoting collaboration and community engagement to help others, it lets users to track/create emergencies or coordinate with nearest shop/stores. | ['Tameesh Sood', 'avinav kandhway', 'Atul Kumar', 'Manish Kumar'] | [] | ['css', 'firebase', 'gifted', 'google-compute-engine', 'google-geocoding', 'google-maps', 'html5', 'react-native'] | 110 |
9,890 | https://devpost.com/software/covid-19-detection-system-57wzbc | Inspiration
As the coronavirus pandemic sweeps the world, more and more governments are imposing lockdowns on their citizens. As a result, me and my team tried there had on artificial intelligence we have tried to build a web-based application which detect covid-19.
Problem Faced
Main issue in this situation is lack of detecting resources. We have provided a simple dataset of x-rays to train the system.
About Project
Our website is a platform which can be used by any one of us from the society from a Civilian to a Medical expert. Just one must have to provide their x-ray of the lungs and the result will show the report instantly on
Just 1 Click
.
Which will help medical experts to detect and provide necessary treatment on time. You will have your reports in your hand within a second.
Technology Used
We have used Python, machine learning, to build the back end and html, CSS, JavaScript for the front end.
The challenges we have faced are mostly related to the modules in machine learning.
Today team
AaKAR
is proud that we have overcome every hurdle and completed the project.
What We Have Learned ?
This project is an extraordinary challenge we dealt with , especially in the time of COVID 19 . We believe that the most meaningful thing we learned is “ TEAMWORK ” .
During working on this project we encountered with a lot of technical and non technical issues and sometimes we fell off as well But
We Climbed and Shined in UNITY by learning something new every time.
As They Say
“Coming Together is a Beginning, Staying Together is Progress, and Working Together is Success”
Team
AaKAR
is working on adding Chatbot to our system, we are working on the latest update that can be done in the project.
Built With
css3
flask
html5
javascript
machine-learning
opencv
os
php
python
Try it out
covid19detection.pythonanywhere.com | COVID-19 Detection System | It is a web based application.You need to Upload Your X-RAY And you will Get Results : COVID + or COVID - if you get detected with COVID+ then you must contact to specialist !! STAY HOME ,STAY SAFE !! | ['Anukriti joshi', 'Arayaman Dubey', 'Kirti Paithankar', 'ruchir toshniwal'] | [] | ['css3', 'flask', 'html5', 'javascript', 'machine-learning', 'opencv', 'os', 'php', 'python'] | 111 |
9,890 | https://devpost.com/software/generating-electricity-by-walking-fyhn7u | The primary hardware components used.
A bunch of piezoelectric sensors!
An inside view of the shoe. 17 piezoelectric sensors can be seen in this side. There is an additional 16 sensors on the other side.
The top down view of the shoe (without the styrofoam)
Summary
The average American walks approximately 3,500 steps per day; each step creates mechanical energy, energy which ends up being wasted and dispersed into the environment. Tapping into this wasted energy opens a door for opportunities to supplement the user’s actions. Varying amounts of piezoelectric sensors were used to generate this energy which gets stored in a LiPo battery through the aid of the BQ25570 chip. My design used 33 piezoelectric sensors, which generated, approximately 0.27 volts or 23.625 mAh just after 60 steps. If a user wore this shoe and walked the average amount of steps per day, they would generate 1,378.125 mAh! In addition, I developed an add-on to this project that adds an Arduino Nano with an Accelerometer and Gyroscope sensor. The data from these sensors are run through a neural network that predicts the behavior the user is doing. For example, if the user is jumping it will predict they are jumping.
How I built it
The hardware component of this project has one layer of styrofoam on the top and bottom. This protects the piezoelectric sensors and increases comfort for the user. Then there are two layers of cardboard, each side of the cardboard has 8-9 piezoelectric sensors, connected in series. The two cardboard pieces are connected in parallel. There is then a thin piece of paper between the two cardboard pieces, to make sure no wires short out when they touch each other.
The software uses Keras with TensorFlow. I created a Google Cloud Virtual Machine Instance, which runs a python script that reads in data regarding user's motion and then with Keras and TensorFlow creates a model of the data that can be used for prediction.
Challenges I ran into
Developing the hardware of the shoes took the bulk of my time. I have never used Piezoelectric sensors before, so I had to learn how to use them. In addition, it took me a while to optimize the energy outputted from the shoe. The green BQ25570 chip helped me do that though.
Accomplishments that I'm proud of
This is the world's most efficient shoe that generates electricity! Other solutions mostly use different means to generate electricity. My solution used Piezoelectric sensors, and then the BQ25570 chip to control the flow of electricity from the two capacitors on the chip to the battery. This minimizes the electricity wasted.
What I learned
I learned a lot! In general, I am better at software related projects, this project, being a hardware-first project, increased my skills in dealing with hardware. I got better at soldering, understanding the mathematical calculations of voltage and current, Piezoelectric sensors, Arduinos and various hardware compounds. On the software side, this was my first time using Google Cloud. I am now comfortable in creating complex Virtual Machines in the cloud that can run various advanced scripts.
What's next for Generating Electricity By Walking
I want to add a wifi/Bluetooth chip into the Arduino Nano, this will enable the data from the accelerometer and gyroscope to transfer to a web server in the cloud without the need of a wire. With this advancement, I could develop a mobile/web app that tracks various foot-related fitness activities, including jumping, running and walking.
Built With
google-cloud
keras
piezoelectric
tensor-flow | Generating Electricity By Walking | Generate a lot of electricity just by walking! | ['Tarun Ravi'] | [] | ['google-cloud', 'keras', 'piezoelectric', 'tensor-flow'] | 112 |
9,890 | https://devpost.com/software/novid-20-a8qmjk | Inspiration
The ongoing pandemic and the crisis that humanity is going through , motivated us to come up with this solution and provide people with an interface where they can have all their queries resolved, get the chances of them being infected and help them.
What it does
Let us know your symptoms and medical issues and we will inform you about your health status. So, avoid yourself going out to visit clinics when you are only anxious due to seasonal changes and the common diseases brought about by them.
Simple, yet quite effective, NOvid-20 was developed to reduce stress and havoc amongst people as seasonal changes and pollution lead to the onset of seasonal flu and allergies which can show symptoms similar to that of COVID-19.
How I built it
I built it as a progressive web application using HTML, CSS, bootstrap for the frontend, used firebase for the backend
and for prediction results, made a machine learning model which predicts the results on basis of various symptoms. A chatbot was also made using dialogflow, which helped in resolving all the queries of the registered user .
Challenges I ran into
1.User unable to read and update the database.
2.Model accuracy = 75%
3.Chatbot unable to process all kinds of user input.
Accomplishments that I'm proud of
The project idea, execution, improvement and the things we learnt
What I learned
Firebase and BootStrap
What's next for NOvid-20
Improving the project further for higher scalability, better interface and more features
Built With
css3
dialogflow
flask
gunicorn
heroku
html5
javascript
machine-learning
numpy
python
scikit-learn
scipy
webdev
Try it out
github.com | NOvid-20 | The novid-20 is an innovative solution to act like an online doctor whenever and wherever you need. Let us know your symptoms and medical issues and we will inform you about your health status. | ['Simrann Arora', 'Drishti Agarwal', 'Vividha Rawat', 'Rishika Garg'] | [] | ['css3', 'dialogflow', 'flask', 'gunicorn', 'heroku', 'html5', 'javascript', 'machine-learning', 'numpy', 'python', 'scikit-learn', 'scipy', 'webdev'] | 113 |
9,890 | https://devpost.com/software/beloved-sparkle-seqmwf | Site Home Page
Healing the Unknown logo
Inspiration
Healing the Unknown was created in April 2020 to provide support to individuals facing grief, loss, and economic anxiety.
What it does
During a challenging time of physical distancing, Healing the Unknown creates space for hope and healing through the delivery of biweekly interfaith content and peer support for individuals dealing with grief and loss and economic anxiety. We train volunteers to provide support to their peers and create an outlet for creative expression and healing.
How I built it
Squarespace, Google Suite
Challenges I ran into
Had to change original site name in order to increase organic traffic.
Accomplishments that I'm proud of
Securing 20 content development, peer counseling, and administrative support volunteers to date. Creating a partnership with CLUE.
What I learned
The importance of SEO before naming a website.
What's next for Healing the Unknown
Growing our user base, as we just launched. We are also still developing our peer support volunteer training around economic anxiety and grief and loss. We will host our first virtual event on May 1st with CLUE. In July, we will decide on next steps regarding organizational incorporation depending on community growth at that point.
Built With
squarespace
Try it out
healingtheunknown.com | Healing the Unknown | A platform connecting individuals facing end-of-life loss and economic anxiety with peer support and uplifting content. | ['gloria-guisbert', 'Angie Mendoza', 'Phuonggy Pham', 'Lucy (WenLu) Xiao'] | [] | ['squarespace'] | 114 |
9,890 | https://devpost.com/software/circle-b4r9ez | Inspiration
Circle is a space where compassion and integrity facilitator will be able to hold group and navigate support or other group exploration together.
Council - an ancient way and modern practice whose roots are within the natural world, spanning diverse cultures and religions. This practice elicits an experience of true community, recognizing that each voice needs to be heard, that every person has a gift, a story to share, a piece of the whole.
Forum- participants generally take one of three vital roles; the presenter or protagonist, the forum 3facilitators and the "mirrors". The group gathers in a circle.
Man & woman circle etc!
You might be wondering how this difference than zoom or other video options. They key with circle is like any circle ~ this will add specific tools for those needing and desiring structures akin to sitting in a circle. Literally we want the group to feel as if they are sitting in a circle.
Where their image is a circle, and the tools available to the facilitator support specific needs in this type of sitting arrangement.
What I learned
The platform will have the option to be in different setting like a circle of Participants with serval room theme
What's next for Circle
Built With
firebase
vue
Try it out
circlelarity.com | Circle | A group therapy platform | ['Nir Asaf'] | [] | ['firebase', 'vue'] | 115 |
9,890 | https://devpost.com/software/healthcarechatbot | Make an impact to the healthcare system
Inspiration
More than 10 million American's lost their jobs due to Coronavirus and there are so many who can't afford insurance! People who need to go to regular consultation are scared to go to the hospital. We felt a need to find a way to solve this problem.
What it does
Our solution is an Online Hospital that offers consultation through a video call from best in class doctors based on your symptoms at the safety of your home which includes,
Health status by connectivity to your Fitbit and iOS devices
Provides you Mental Health tips
Enables you to donate to COVID 19 response fund
You will instantly connect to a doctor through a video call if you have Corona like symptoms
Apply for a free health insurance
How I built it
Using Oracle Autonomous Database, Analytics Cloud, APEX, Digital Assistant, Object Storage, Google APIs, Fitbit, and iOS Health
Challenges I ran into Designing the solution:
Coming up with a simplified solution to improve the user experience.
Accomplishments that I'm proud of
Coming up with a solution that helps every individual to have access to certified doctors irrespective of their insurance status and instantly respond to those in need.
Even doctors with health complications and Veteran doctors who would like to help can contribute by providing consultations.
Building a fully integrated solution where a patient can interact with a chatbot in the most natural and conversational way.
What I learned
Build a solution to make a difference!
What's next for HealthCareChatbot
Integrating it with WHO data and analyzing the data to notify people based on their health status by continuously monitoring their health through historic and real-time data.
Built With
analytics-cloud
apex
autonomous-database
digital-assistant
fitbit
google
google-caldav
health
ios
machine-learning
object-storage
oracle
Try it out
150.136.137.1
150.136.137.1 | SRM Health - An online healthcare system | Scared of going to the hospital? Need to stay sane? 27.5+ million uninsured people! Every person deserves safe and better healthcare, whether uninsured or insured. | ['Saipriya Thirvakadu', 'Rabia Gunonu', 'Megha Gajbhiye'] | [] | ['analytics-cloud', 'apex', 'autonomous-database', 'digital-assistant', 'fitbit', 'google', 'google-caldav', 'health', 'ios', 'machine-learning', 'object-storage', 'oracle'] | 116 |
9,890 | https://devpost.com/software/pneumoscan-an-ai-radiology-tool-for-covid-19-pandemics | Fig. 1: Map of Covid19 cases around the world (as of 4/30/2020)
Fig 2: Top 10 countries with most COVID-19 deaths
Fig 3: Current chest X-ray diagnosis vs. noval process with CovidScan.ai
Chart of wait-time reduction of AI radiology tool (data from a simulation stud reported in Mauro et al., 2019).
Fig. 5: Process of CovidScan development
Demo of web-app:
https://www.cv19scan.site/
(Please use Internet Explorer, or Firefox, our web-app currently doesn't support Chrome)
Dataset:
For the data analytics of COVID-19 pandemics, we used data collected by the Johns Hopkins University Center for Systems Science and Engineering updated on 4/30/2020.
For the chest X-ray detection models, we used combined 2 sources of dataset:
The first source is the RSNA Pneumonia Detection Challenge dataset available on
Kaggle
contains several deidentified CXRs with 2 class labels of pneumonia and normal.
The COVID-19 image data collection repository on
GitHub
is a growing collection of deidentified CXRs from COVID-19 cases internationally. The data is collected by Joseph Paul Cohen and his fellow collaborators at the University of Montreal
Eventually, our dataset consists of 5433 training data points, 624 validation data points and 16 test data points.
Inspiration
What will be working situation for medical staff in hospitals during and after the COVID-19 pandemic? How can the medical staff quickly and securely log in and perform PPE safety check while dealing with a huge influx of patients in critical conditions? How can we automate the process of COVID-19 diagnosis so precious time can be saved for both medical doctors and the patients? How can our solution for hospital later be scaled and implemented to be a essential tool for automating the daily operation at hospital even after the COVID-19 pandemics is over?
To answer these core questions, we did some background research to identify the main challenges in order to develop the best solutions around those:
COVID-19 Pandemic:
Fig. 1: Map of Covid19 cases around the world (as of 4/30/2020). Our team created the map based on data collected by the Johns Hopkins University Center for Systems Science and Engineering.
As we see from the map above and the pie chart below, COVID-19, previously known as the novel Coronavirus, has killed more than 63,860 people and infected over 1,067,061 people in the United States alone, topping all other countries around the world. This number is continuing to grow every day.
Fig. 2: Top 10 countries with most COVID-19 deaths.
The 3 main problems occur in the healthcare system during the pandemics are:
1. Confidentiality:
As you may see on the news, hospitals all over the U.S. (New York, Chicago,California…) and other countries (Italy, Spain…) are flooded with a huge influx of patients with critical conditions. With the increasing workload for the medical staff, patients’ confidential information may be put at risk if unauthorized personels can hack into the electronic medical record system. Thus, there is a need for a fast and secured method for medical staff to log in to the electronic medical record platform, so that the staff can move quickly with patients’ information inputting and still remain compliant with HIPPAA (Health Insurance Portability and Accountability Act). Badge scanning will be highly secured solution for this problem.
2. PPE Safety Check:
According to CDC, during COVID-19 pandemics, all healthcare workers should follow strict guidlines and protocols from OSHA regarding wearing PPE. All of the PPE prevents contact with the infectious agent, or body fluid that may contain the infectious agent, by creating a barrier between the worker and the infectious material. Gloves, protect the hands, gowns or aprons protect the skin and/or clothing, masks and respirators protect the mouth and nose, goggles protect the eyes, and face shields protect the entire face. N95 masks are the PPE most often used to control exposures to infections transmitted via the airborne route. Therefore, checking medical staff’s PPE safety protocol is especially crucial during this pandemics.
3. Long wait time for COVID-19 chest X-ray result:
Fig 3: Current chest X-ray diagnosis vs. novel process with CovidScan.ai
Patients can first be screened for flu-like symptoms using nasal swap to confirm their COVID-19 status. After 14 days of quarantine for confirmed cases, the hospital draws the patient’s blood and takes the patient’s chest X-ray. Chest X-ray is a golden standard for physicians and radiologists to check for the infection caused by the virus. An x-ray imaging will allow your doctor to see your lungs, heart and blood vessels to help determine if you have pneumonia. When interpreting the x-ray, the radiologist will look for white spots in the lungs (called infiltrates) that identify an infection. This exam, together with other vital signs such as temperature, or flu-like symptoms, will also help doctors determine whether a patient is infected with COVID-19 or other pneumonia-related diseases. The standard procedure of pneumonia diagnosis involves a radiologist reviewing chest x-ray images and send the result report to a patient’s primary care physician (PCP), who then will discuss the results with the patient.
_Fig 4: Chart of wait-time reduction of AI radiology tool (data from a simulation stud reported in Mauro et al., 2019). _
A survey by the University of Michigan shows that patients usually expect the result came back after 2-3 days a chest X-ray test for pneumonia. (Crist, 2017) However, the average wait time for the patients is 11 days (2 weeks). This long delay happens because radiologists usually need at least 20 minutes to review the X-ray while the number of images keeps stacking up after each operation day of the clinic. New research has found that an artificial intelligence (AI) radiology platform such as our CovidScan.ai can dramatically reduce the patient’s wait time significantly, cutting the average delay from 11 days to less than 3 days for abnormal radiographs with critical findings. (Mauro et al., 2019) With this wait-tine reduction, patients I critical cases will receive their results faster, and receive appropriate care sooner.
What it does
Using the power of pretrained machine learning models from open source, CovidScan.ai is created as a full-scaled AI tool for radiology clinics and hospitals. It can automate the process of security log-in, PPE safety check for medical staff and assist radiologists determine sign of COVID-19 on chest X-ray images with high accuracy indicates pneumonia. This tool of cutting edge technology can be used to reduce the workload for clinicians, and speed up patients’ wait time for pneumonia lab results in this critical time of the COVID-19 pandemic.
Fig 5: Deployment process of pretrained ML model to the web-app
As explained in the figure above, the CovidScan web-app includes 3 main AI components:
1. ID Badge Scanner:
For security purpose, only authorized personel can access to the web-app, which contains patients’ confidential health information (name, date of birth, chest X-ray, medical history…). Hence, the web-app will use pretrained scan the medical’s badge to grant them access to the software.
2. PPE Safety Check:
Due to hospitals/clinics’ strict guidelines in PPE usage, especially during this COVID-19 ourbreak, the web-app will ask the medical staff if he/she is in direct contact with patients for chest X-ray taking. If yes, then the web-app witll use AWS pretrained to check for medical staff’s PPE to see if the staff follow the safety protocols to minimize any exposures to the disease. If the medical staff passed both the secured check and safety, he/she can move on the the next step.
3. COVID-19 Chest X-ray Testing:
In the last step, the medical staff take patients’ chest X-ray images using the specialized machine and then upload the taken images to the database of web-app for testing for sign of COVID-19 infection or bacterial pneumonia.
It is due to the fact that an AI system can review, highlight the pneumonia sign and classify each X-ray image all in less than 10 seconds (comparing the radiologist’s 20 minutes that we mentioned earlier), and it can do that same task effortlessly for 24 hours without taking a break. This time cut is especially critical in the time amid the pandemic of COVID-19. With this spreading rate, it will be overwhelming for radiologists to review a massive number of chest X-ray images of potential COVID-19 infected patients. With the assistance of CovidScan.ai, it can automatically highlight the suspected signs of pneumonia for the radiologists and speed up the process of chest X-ray review. Therefore, more COVID-19 positive-tested patients will get their result back faster and receive appropriate care sooner to prevent the spread of the virus.
How we built it
Employee Badge Scanner:
We developed this feature using the open-source python library Pyzbar. We have written the script in the JQuery which sends the snapshots from the live camera feed to the inference model at the backend. It can read one-dimensional barcodes and QR codes present on the employee’s ID badge. We implemented this feature to work with a snapshot of employees’ ID badge.
Link:
https://pypi.org/project/pyzbar/
PPE Safety Check:
We developed this feature using the open-source TensorFlow model for face mask detection. The backbone network only has 8 Conv layers and the total model has only 24 layers with the location and classification layers counted. The dataset is composed of WIDER Face and MAFA datasets. We have written the script in the JQuery which sends the snapshots from the live camera feed to the inference model at the backend. It works with live footage from any sort of cameras and detects people not wearing a face mask.
Link:
https://github.com/AIZOOTech/FaceMaskDetection
Chest X-ray Classification:
For this feature, we developed a Pytorch model. This project’s goal is to draw class activation heatmaps on suspected signs of pneumonia and then classify chest x-ray images as “Pneumonia” or “Normal”. For this project, we are going to use a dataset available at Kaggle consisting of 5433 training data points, 624 validation data points and 16 test data points. C. For the model, we load the pre-trained Resnet-152 available from Torchvision for transfer learning. ResNet-152 provides the state-of-art feature extraction since it is trained on a big dataset of ImageNet. ResNet-152, as the name sounds, consists of 152 convolutional layers. Due to its very deep network, the layers are arranged in a series of Residual blocks. These Residual blocks skip connections to help prevent the vanishing gradients which are a common problem with networks with deep architecture like ours. Resnet also supports Global Average Pooling Layer which is essential for our attention layer later on. For the attention layer to draw the heatmap, we use the global average pooling layer proposed in Zhou et al. Global average pooling layer explicitly enables the convolutional neural network (CNN) to have remarkable localization ability. We achieve 97% accuracy on the training dataset and 80% on the testing dataset.
Web development: The trained weights of the deep learning models are deployed in a form of Django backend web app CovidScan.ai. While the minimal front-end of this web app is done using HTML, CSS, Jquery, Bootstrap. In our latter stage, the web-app will then be deployed and hosted on Debian server.
Technical Requirements:
The packages required for this project are as follows:
Torch (torch.nn, torch.optim, torchvision, torchvision.transforms)
Django
Numpy
Matplotlib
Scipy
PIL
Tensorflow
jQuery
Challenges we ran into
This hackathon project was a very different experience for us which challenged us throughout this project with the AWS sagemaker. This is the first time we all were working with AWS sagemaker and creating endpoints of the pre-trained TensorFlow model. Also, understanding curated models and determining their accuracy was a little bit challenging for us. Even after successfully deploying the model’s endpoints, calling Amazon SageMaker model endpoints using Amazon API Gateway and AWS Lambda gave us a very hard time.
Accomplishments that we're proud of
We manage to finish the project in such a limited time of 2 weeks in our free time from school and work. We still keep striving to submit on time while learning and developing at the same time. We are really satisfied and proud of our final product for the hackathon.
What we learned
Through this project, we learn to implement a complicated image-recognition deep learning models from AWS marketplace. We also learn the process of developing a mini data science project from finding dataset to training the deep learning model and finally deploy & integrate it into a web-app. This project can’t be done without the efforts and collaboration from a team with such diverse backgrounds in technical skills.
What's next for CovidScan:
In the next 2 months, our plan is:
We will raise fund to invest more into the R&D process.
We will partner with research lab to collect more dataset and find hospitals to test our solution. One of our memeber has published his newly collected dataset on this open-source github:
https://github.com/nihalnihalani/COVID19-Detection-using-X-ray-images-/
Regarding our R&D, we plan on improving the performance of the platform, preferably by reading more scientific literature on state-of-art deep learning models implemented for radiology.
We also plan to add the bound box around the suspected area of infection on top of the heatmap to make the output image more interpretable for the radiologists. We are working to implament the multilabeling model of COVID-CXR on our dataset to improve our application. This model is published by The Artificial Intelligence Research and Innovation Lab at the City of London's Information Technology Services division and has accuracy 0.92, precision 0.5, recall 0.875, auc 0.96.
In many pieces of literature, they mentioned developing the NLP model on radiology report with other structured variables such as age, race, gender, temperature... and integrating it with the computer vision model of chest X-ray to give the expert radiologist’s level of diagnosis. (Irvin et al., 2019; Mauro et al., 2019) We may try to implement that as we move further with the project in the future.
With the improved results, we will publish these findings and methodologies in a user-interface journal so that it can be reviewed by expert computer scientists and radiologists in the field.
Eventually, we will expand our classes to include more pneumonia-related diseases such as atelectasis, cardiomegaly, effusion, infiltration, etc. so that this platform can be widely used by the radiologists for general diagnosis even after the COVID-19 pandemics is over. Our end goal is to make this tool a scalable that can be used in all the radiology clinic across the globe, even in the rural area with limited access to the internet like those in Southeast Asia or Africa.
References:
Crist, C. (2017, November 30). Radiologists want patients to get test results faster. Retrieved from
https://www.reuters.com/article/us-radiology-results-timeliness/radiologists-want-patients-to-get-test-results-faster-idUSKBN1DH2R6
Irvin, Jeremy & Rajpurkar, Pranav & Ko, Michael & Yu, Yifan & Ciurea-Ilcus, Silviana & Chute, Chris & Marklund, Henrik & Haghgoo, Behzad & Ball, Robyn & Shpanskaya, Katie & Seekins, Jayne & Mong, David & Halabi, Safwan & Sandberg, Jesse & Jones, Ricky & Larson, David & Langlotz, Curtis & Patel, Bhavik & Lungren, Matthew & Ng, Andrew. (2019). CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison.
Kent, J. (2019, September 30). Artificial Intelligence System Analyzes Chest X-Rays in 10 Seconds. Retrieved from
https://healthitanalytics.com/news/artificial-intelligence-system-analyzes-chest-x-rays-in-10-seconds
Lambert, J. (2020, March 11). What WHO calling the coronavirus outbreak a pandemic means. Retrieved from
https://www.sciencenews.org/article/coronavirus-outbreak-who-pandemic
Mauro Annarumma, Samuel J. Withey, Robert J. Bakewell, Emanuele Pesce, Vicky Goh, Giovanni Montana. (2019). Automated Triaging of Adult Chest Radiographs with Deep Artificial Neural Networks. Radiology; 180921 DOI: 10.1148/radiol.2018180921
Wang, L., & Wong, A. (2020, March 30). COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images. Retrieved from
https://arxiv.org/abs/2003.09871
Built With
matplotlib
numpy
pil
pytorch1.0.1
torchvision0.2.2
Try it out
gitlab.com
www.cv19scan.site | CovidScan-An AI Radiology Tool For COVID-19 Pandemic | CovidScan.ai is developed to be a secured AI platform with the purpose to assist radiologists with fast and accurate pneumonia dectection amid this COVID-19 pandemic. | ['Moksh Nirvaan', 'Nihal Nihalani', 'Vi Ly'] | ['Second Place', '2nd Place - Website Feature'] | ['matplotlib', 'numpy', 'pil', 'pytorch1.0.1', 'torchvision0.2.2'] | 117 |
9,890 | https://devpost.com/software/private-extractor | Exposure Report Page
Initial Report Page
Export Log Page 1
Export Log Page 2
Basic GUI for Extraction
Private Extractor
Find your COVID-19 risk.
Inspiration
I wanted the ability to continously monitor my exposure as new cases came out. Doing this manually or with existing systems was not that easy.
What it does
Private Extractor allows you to build a simple exposure report from your day to day movements using MIT PrivateKit's information.
Import your data from a plugged in Android device (in debug mode)
Import your data from a text file.
How I built it
Using a mixture of Android Studio (for the Android Extractor) and Eclipse (for the Desktop GUI)
Challenges I ran into
UI Automator kept having issues exporting the MIT Private Kit files. MIT Private Kit changed the format halfway through the hackathon. JLR would not use pdflatex correctly from Miktex Portable so I had to hack it together.
Accomplishments that I'm proud of
I was very excited to have a working version people can download and use.
I feel this will be useful for medical professionals processing exports from people.
What I learned
UI Automator on Android can be a very interesting source of fun.
What's next for Private Extractor
I am currently trying to build an enhancement to make this command line driven.
Once this is done, I will be hosting it somewhere where a web page can accept your text file directly.
Built With
Android, Android Debug Bridge, Android Backup Extractor, JLR, Gson, GeoTools, tar, MIT PrivateKit, and Miktex Portable Edition
Requirements
File Extractions: Modern version of Windows (with Java and tar in PATH)
Android Extractions: Add a version of adb in PATH
Videos
Built With
android
java
latex
Try it out
github.com | Private Extractor | Find your COVID-19 risk. | ['Lazaro Herrera'] | [] | ['android', 'java', 'latex'] | 118 |
9,890 | https://devpost.com/software/notes-for-support | Submit a note page
How our project works
Homepage
https://www.notesforsupport.org/
Inspiration
Two years before the Coronavirus pandemic (during freshman year of HS), I was in quarantine for about a month due to a rare eye disease. During this time, I felt an overwhelming amount of loneliness and disconnection from the world, so receiving cards from friends and family meant the world to me. I want to use my project to help the thousands of people who need it the most right now.
What it does
Through this website, anyone can anonymously write an encouraging note to a healthcare worker or coronavirus patient. Once enough notes come in, they will be printed and shipped to hospitals to be distributed to one medical worker or patient.
Accomplishments that I'm proud of
I launched this site a few days ago, &
so far, 1400+ cards have been sent & we are partnered with 13 hospitals around the US!
Why does it work?
According to a fifty year study conducted by Harvard University, human connection is the single most important component of happiness. That's why the concept of sending physical, individual notes is so powerful.
Currently, one in two health care workers are struggling with depression amid Covid-19 and many patients have to die alone due to the infectious nature of the virus. Hence, for health care workers and patients, receiving an encouraging, personal message can mean the world.
And beyond this, this is free, quick, and no personal information is required, meaning that
anyone
can contribute to the COVID-19 effort.
How I built it
I edited and designed all the graphics on Canva & PowerPoint, then created the site on Wix, using a little bit of HTML along the way.
Challenges I ran into
Balancing high school & this project and learning how to use the resources I have (PowerPoint, Canva & Wix) to create a website and a video!
What's next for Covid Support
Creating software that will automate the sorting of messages by their hospitals.
Built With
canva
html5
powerpoint
wix
Try it out
www.notesforsupport.org | Notes for Support | Anyone can send notes of encouragement to COVID-19 patients & health care workers, which we'll print & send it to hospitals! | ['gina c'] | ['Second Place - Donation to cause or non-profit organization involved in fighting the COVID crisis'] | ['canva', 'html5', 'powerpoint', 'wix'] | 119 |
9,890 | https://devpost.com/software/notboringevents | As you may know, 3 BLN of people are stuck at their homes. Zoom's traffic increased 200 times. Online events industry has boomed. People even started drinking online.
So I have asked 43 people who organized online events during COVID19 and they answered that there is no good platform to market online events. So all of them had to spend money on marketing on facebook and some other channels.
So we are thinking to build online events marketplace to let anyone list and market online public events (webinar, concert, translation, hangout-call and so on) so more people can find a way to spend their free time more efficiently.
We want to do it multilingual so that people will get automatic translation to their own language during the event
During the project development, we see the main obstacles:
-Contacting the event platforms to get an API for listing all their events
-How to get 100k users in a month with a growth hacking (our goal)
My main accomplishment is being 500 Startups accelerated and creating a product that got 20k registered users (bootstrapping)
During my 5 years of experience, I learned that the most important is people, not the idea or the product
Our next goals are to:
Get 100k users, add a payed-events feature, blog, setting up marketing campaigns and A/B testing of growth hacking features
Built With
nocode | Not Boring Events | A marketplace of online events for people who are stuck at their homes | ['Maxim Motin', 'Alex Vernik', 'Nik Shevchenko', 'avkrikunenko'] | [] | ['nocode'] | 120 |
9,890 | https://devpost.com/software/covid19-chromecast-dashboard | Inspiration
Motivation is very necessary for any job. At this time, the healthcare professionals need it most. This is an analytics dashboard prototype to keep them aware of their positive impact, hence inspiring them to keep going.
How I built it
Quite complicated with a lot of moving pieces. High-level architecture looks like:
Sequence of backend application
Third Party Covid19 API
FETCH by NodeJS server (on Heroku)
STORE Data File (on Google Cloud Storage)
USE Data File in Chromecast "Custom Receiver" App (on Heroku)
User flow of front-end application
User accesses Chromecast "Sender" App (USER INTERFACE) (on Heroku)
(NOTE: THE APP RUNS ON A CHROME BROWSER ONLY)
Clicks cast button
Chromecast fetches dashboard from internet
Challenges I ran into
Chromecast app is not very straight-forward to build, deploy, publish
Trying to maintain low-cost by using multiple free-tiers
New to Google Charts
New to Google Cloud
Not a UX expert, so poor design
Accomplishments that I'm proud of
I could finish a basic chromecast App
I could experiment with various new technologies
If this gets traction, I can spend more time to improve the app
What I learned
Google Cloud Services
Google Charts
Google Cast SDK
What's next for covID19 Chromecast Dashboard
Improve Dashboard Design
User Choice to change country
Add more analytics
Refresh interval
Switch between different analytics views (Feature)
Built With
css3
google-cast-sdk
google-cloud
heroku
html5
javascript
node.js
Try it out
covid19-sender.herokuapp.com | covID19 Chromecast Dashboard | A chromecast dashboard for Hospitals for real-time analytics to keep healthcare professionals motivated. | ['Akshit Singla'] | [] | ['css3', 'google-cast-sdk', 'google-cloud', 'heroku', 'html5', 'javascript', 'node.js'] | 121 |
9,890 | https://devpost.com/software/remote-elderly-home-care-via-privacy-preserving-surveillance-tzp2ab | Privacy Preserving Person Face Detection at Home
Plug and Play AI Device Discovery
Person Detection Indoors
Home Page
Person Detection Outdoors
Inspiration
COVID19 isolated at home many of us, including our elderly parents and grandparents. Not being able to check on them regularly elevates the risks that they are exposed to such as falls, gas leaks, flooding, fire and others.
What it does
Ambianic.ai is an end-to-end Open Source Ambient Intelligence project that removes the stigma associated with surveillance systems by implementing privacy preserving algorithms in three critical layers:
Peer-to-Peer Remote access
Local device AI inference and training
Local data storage
Ambianic.ai observes a target environment and alerts users for events of interest. Data us only available to homeowners and their family. User data is never sent to any third party cloud servers.
Here is a blog post that goes into the reasons why we started this project:
https://blog.ambianic.ai/2020/02/05/pnp.html
And here is a technical deep dive article published in WebRTCHacks. It clarifies that it is absolutely possible to build a privacy preserving surveillance system, despite popular cloud vendors making us believe that all user data belongs safely on their cloud servers:
https://webrtchacks.com/private-home-surveillance-with-the-webrtc-datachannel/
How we built it
Ambianic.ai has 3 main components:
Ambianic.ai Edge: a Python application designed to run on an IoT Edge device such as a Raspberry Pi or a NUC. It attaches to video cameras and other sensors to gather input. It then runs inference pipelines using AI models that detect events of interest such as objects, people and other triggers.
Ambianic.ai UI: A Progressive Web App written in Javascript using Vue.js and other front end frameworks to deliver an intuitive timeline of events to the end user.
Ambianic.ai PnP: A plug-and-play framework that allows Ambianic UI and Ambianic Edge to discover each other seamlessly and communicate over secure peer-to-peer protocol using WebRTC APIs.
Challenges we ran into
Challenges include selecting high performance, high accuracy and low latency AI models to detect events of interest on resource constraint edge devices.
Another challenge is taking into account user local data to fine tune AI models. Pre-trained models can perform reasonably well, but they can be improved with privacy preserving federated learning on unique new local data.
Accomplishments that we're proud of
Ambianic.ai has been in public Beta for several weeks helping a number of users in their daily lives. Some users report success in keeping an eye on their elderly family members:
https://twitter.com/mchapman671/status/1230931722650423299
What we learned
Although the project sets ambitious goals, there seem to be sufficient enabling Open Source frameworks and community momentum to drive the ongoing success.
What's next for Remote Elderly Home Care via Privacy Preserving Surveillance
We need to work on these major areas:
Recruit volunteers in the home care community to test the system and provide feedback
Select more models to address open use cases such as fall detection, gas leaks and others
Work on implementing Federated Learning infrastructure to fine tune initial pre-trained models.
Built With
javascript
pwa
python
raspberry-pi
tensorflow
Try it out
docs.ambianic.ai | Remote Elderly Home Care via Privacy Preserving Surveillance | COVID19 isolated at home many of us, including our elderly family members. Left unattended they are prone to risks such as falls, gas leaks, flooding, fire and others. | ['Wong Piu Yee Christopher How Feng', 'Nasr Galal', 'hira khalid', 'Ivelin Ivanov', 'Björn Kristensson Alfsson', 'Srujana Munamala', 'Cassie Hudson', 'Samad Ahmed', 'Shashwat Prasad', 'vatsa srivastava', 'Priyam Gupta', 'Ali Hussain', 'Peter Gelderbloem', 'Abdullateef Almohamad', 'Alexis Leveratto', 'Chukwuemeka Ome', 'mahak bisht', 'jitendra kumar', 'Yusuf Suleiman', 'Jackline Mboi', 'P. Nair', 'Nurudeen Hamdana Kankia', 'King Adedoyin', 'Yana Vasileva', 'Kevin LOGNONÉ', 'Michael Quaynor', 'Meghan A. Lockard, Ph.D., P.M.P.', 'Lavanya Chellam', 'ayush kasera', 'henchik _', 'pattern project', 'Anshuman Sharma', 'Mahima Mallikarjuna', 'Yen Low', 'Amit Dandawate'] | [] | ['javascript', 'pwa', 'python', 'raspberry-pi', 'tensorflow'] | 122 |
9,890 | https://devpost.com/software/internationale-forschungsplattform | Logo Forschungplattform
Diese Plattform ensteht in Kooperation mit der Crisis_Magament_Plattform
sodass dem Wissenschaftler, Forscher und Erfinder dieser Forschungsplattform
immer die aktuellsten Daten zur Verfügung stehen.
Beschreibung des Projektes:
Es handelt sich bei diesem Projekt um eine neue Art der Forschung und einem noch nie dagewesenen weltweiten Projekt. Diese Plattform verbindet Wissenschaftler, Forscher und Erfinder in einer Form, die es weltweit noch nicht gegeben hat.
Die Daten, die entscheidend für Forschung sind, werden International zusammengestellt und stehen allen Forschern, Wissenschaftlern und Erfindern dieser Plattform zur Verfügung.
Dies ist aber nicht nur ein Internationales wissenschaftliches Projekt, sondern beinhaltet gleichermaßen ein soziologisches Projekt. Es ist ein dreifaches wissenschaftliches Projekt, welches eine große Zukunft hat. Stellen Sie sich selbst die Frage, was Sie für Fortschritte in allen Bereichen der Forschung machen können, wenn mit einmal statt 200 Wissenschaftlern 50000 Wissenschaftler, Forscher und Erfinder zusammen an den Projekten forschen. Mit einem unglaublichen Wissensspektrum und Ideen gebündelt auf einer Plattform zum Wohle aller Menschen.
Die Planung und organisatorischen Maßnahmen, ergeben sich aus dem Verbund internationaler Wissenschaftler, Forscher und Erfindern. Diese organisieren mithilfe dieser Plattform virtuelle Meetings um gemeinsam zu Forschen.
Daten werden durch ein Expertenteam analysiert und in der Plattform eingebunden, sodass jeder auf dieser Plattform immer und jederzeit auf den neusten Stand der aktuellen Forschung ist. Die Nutzer dieser Plattform haben zudem die Möglichkeit eine bestimmte Seite zum Beispiel eines Forschungsinstitutes unserem Team vorzuschlagen und nach einer Prüfung durch ein Team und der Absprache mit dem Betreiber der Seite wird diese integriert,
zudem können Forschungseinrichtungen ihre Seite mit einbinden lassen, sodass alle auf der Plattform befindlichen Nutzer die Informationen der Seite sehen können.
Das Gute an dieser Plattform ist, dass die Nutzer sich selbst organisieren, Meetings planen können und sich jederzeit austauschen können.
Gleichzeitig haben Nutzer die Möglichkeit Ihre Entdeckungen international mit allen Nutzern der Plattform zu veröffentlichen.
Wer steht eigentlich hinter dieser ganzen Aktion?
Mein Name ist Michael Rhein, ich leitete die Patentverwertung TIZ-NORD Wilhelmshaven Technisches Innovations Zentrum für Forschung und Patentverwertung. Als die Pandemie losging, überlegte ich welche Möglichkeiten es gibt, um schnellstmöglich diese Krise zu bewältigen. So suchte ich am Anfang alleine nach Lösungen bis ich bemerkte, dass genau hier die Lösung zu finden ist. Aufgrund dessen überlegte ich wie ich es schaffen könnte, so viele Menschen wie möglich zusammenzubringen, um eine Lösung gemeinsam zu finden. Hierbei war mir aber von Anfang an klar, dass dies nur mit Experten auf dem Gebieten der Wissenschaften, der Forschung und mit den daraus resultierenden Erfindungen oder medizinischen Mitteln der Erfinder möglich sein würde und so entwickelte ich das Projekt der Forschungsplattform. Mir war bewusst, dass dies mich vor enorme Herausforderungen stellen würde und wird, doch diese werde ich gemeinsam mit allen zusammen bewältigen können.
Wie ist es mit den Rechten?
Ich habe lange überlegt wie man den rechtlichen Teil dieser Plattform Organisieren könnte. Jetzt habe ich auch hierzu eine Lösung gefunden. Alle die sich auf dieser Plattform registrieren, willigen einfach ein, dass die Rechte an den auf der Forschungsplattform gemachten Forschungsergebnisse, Erfindungen zum Wohle der Menschheit, auf alle auf der Plattform registrierten Nutzer die an der Erstellung dieser Erfindung, Lösung oder den Forschungsergebnissen gleichmäßig verteilt werden. Dies ist eine optimale Lösung um gemeinsam zu Forschen. Zur Umsetzung dieser Regelung Akzeptieren die Nutzer die AGBs der Plattform, in denen alle rechtlichen Punkte festgelegt sind.
Wie sieht es Finanziell aus? Wer Finanziert das ganze?
Es geht bei dieser Plattform nicht darum, den größtmöglichen Gewinn zu erzielen, vielmehr geht es darum gemeinsam Dinge zu entwickeln die eine solche Situation wie Coovid-19 vermeiden können oder zumindest eine schnelle Lösung dafür herbeiführen können. Ich versuche über politische Wege von den Regierungen aus den die Wissenschaftler, Erfinder und Forscher stammen finanzielle Fördermittel zu erhalten, diese werden dann an die jeweiligen Forschungsinstitute, Wissenschaftler, Forscher und Erfinder aufgeteilt, hierzu werde ich ein Finanz Team zusammenstellen.
Wir sind alle Menschen, wir Leben alle auf diesen Planeten, lassen Sie uns gemeinsam Technologien und Möglichkeiten entwickeln, die wir uns heute eventuell noch nicht einmal vorstellen können.
Diese Plattform lebt wie wir alle, von stetiger Veränderung und Anpassung. Gemeinsam entwickelt sich diese Plattform durch uns alle. Und so werden wir Teil dieser Plattform.
Description
This project is a new type of research and an unprecedented global project that connects scientists, researchers and inventors in a way that has never existed before.
The data, which is crucial for research, is compiled internationally and is available to all researchers, scientists and inventors of this platform.
However, this is not just an international scientific project, it also includes a sociological project. It is a triple scientific project that has a great future. Ask yourself the question of what progress we can make in all areas of research if, instead of 200 scientists, 50,000 scientists, researchers and inventors research the projects together. With an incredible range of knowledge and ideas Thum bundled on a platform for the benefit of all people. The planning and organizational measures result from the association of international scientists, researchers and inventors. With the help of this platform, they organize virtual meetings to conduct research. Data is analyzed by a team of experts and integrated into the platform, so that everyone on this platform is always up to date with the latest research. Users of this platform also have the opportunity To propose a certain page, for example a research institute, to our team and after an examination by a team and consultation with the operator of the page, this is intrigued, and research institutions can have your page integrated so that all users on the platform see the information on the page The good thing about this platform is that users can organize themselves, plan meetings and exchange information at any time. At the same time, users have the opportunity to publish their discoveries internationally with all users of the platform.
Who is actually behind this whole campaign?
My name is Michael Rhein, I managed the patent exploitation TIZ-NORD Wilhelmshaven Technical Innovation Center for Research and Patent Exploitation. When the pandemic started, I thought about the options for dealing with this crisis as quickly as possible. So in the beginning I looked for solutions on my own until I noticed that the solution can be found right here. Because of this, I thought about how I could bring as many people as possible together to find a solution together. However, it was clear to me from the beginning that this would only be possible with experts in the fields of science, research and the inventions or medical means of the inventors resulting therefrom and so I developed the project of the research platform. I was aware that this would and will present me with enormous challenges, but I will be able to master these together with everyone.
What about the rights?
I have long considered how to organize the legal part of this platform. Now I have found a solution for this too. All those who register on this platform simply agree that the rights to the research results made on the research platform, inventions for the benefit of mankind, to all users registered on the platform who are involved in the creation of this invention, solution or the research results are evenly distributed . This is an optimal solution for doing research together. To implement this regulation, users accept the general terms and conditions of the platform, in which all legal points are defined.
What does it look like financially? Who finances the whole thing?
This platform is not about making the greatest possible profit, rather it is about developing things together that can avoid such a situation as Coovid-19 or at least bring about a quick solution. I try to get political funding from the governments from which the scientists, inventors and researchers come, which are then distributed to the respective research institutes, scientists, researchers and inventors, for this I will put together a finance team. We are all human beings, we all live on this planet, let's develop technologies and opportunities together that we may not even be able to imagine today. This platform, like all of us, lives from constant change and adaptation. Together we all develop this platform, so we become part of it.
Built With
css3
deutsch
englisch
google-analytics
google-translate
japanisch
javascript
php
php5
russisch
spanisch
strato
weltweit-alle-spachen
zoom
Try it out
forschungsplattform.com | Internationale Forschungsplattform Research platform | für weltweit alle Forscher, Wissenschafer und Erfinder for everyone Inventor researcher and scientist | ['Michael Rhein', 'Carola Günther'] | [] | ['css3', 'deutsch', 'englisch', 'google-analytics', 'google-translate', 'japanisch', 'javascript', 'php', 'php5', 'russisch', 'spanisch', 'strato', 'weltweit-alle-spachen', 'zoom'] | 123 |
9,890 | https://devpost.com/software/covid-19-chart | Interactive Chart showing trends of COVID-19 of different locations in the world.
Always up-to-date COVID-19 data
Table showing data of all the countries with COVID-19 cases. (184)
Bar Chart showing countries with most COVID-19 cases
Inspiration
The inspiration started from our simple wondering: why can’t every country have the same level of access to the data we need? Why is it so difficult to get our regional data of CoVID infection? We wanted to help people to know the exact information in their region and their loved ones so that they can feel safe and know what is going on. As the situation gets serious in Europe and the whole globe, the most important thing is to get the exact data.
That is why we came up with this idea of our application:
CoVID 19 Dashboard
.
Until now, we had a lot of inconvenience of accessing the data we needed. There were a lot of problems because of this. For example:
Could not quickly get to the information you need
Could not understand the infection rate for your community
Could not check the curve and overall status.
Could not access the exact number of CoVID statistics by categories.
Could not search the region that you wanted to check.
Could not see both the world’s status and the regional status at the same time.
We thought we could fix these problems and make it better. We fixed most of the problems above and provided:
Easy access to the COVID-19 statistics by country categories.
Convenient User Interface
The number of German cities’ active COVID-19 patients.
The flow of the curve of new patients in German cities.
Total confirmed, total active, and total death in the top 6 countries
Users can search their region and get the information they need.
Our application can help people in various aspects. We believe that we can defeat COVID-19 by collaborating and staying healthy. We want to be part of the fight.
What it does?
Visualizing the COVID-19 data in as a Chart. Update every 24 hours
The chart informs people of the valuable trend of the COVID-19 situation better than only the latest updated value.
Collection of trustworthy data source in one place as a dashboard.
To help the German and other citizens, understand the trend of COVID-19 better. I started with the data in German cities as I am now studying in Stuttgart, Germany.
To help people understand the daily trend of COVID-19 what is going on in their own regions.
How I built it?
Node.js server helps to retrieve data from several trustworthy datasource.
HTML5 with Bootstrap framework.
Challenges I ran into?
I knew about this hackathon only half an hour before the deadline.
Built With
apex-chart
bootstrap
css
css3
datatable
express.js
html5
javascript
node.js
Try it out
www.covid19dashboard.org | COVID-19 Dashboard | The web application dashboard for visualizing the COVID-19 data. | ['Chaeyun Kim', 'Joe Thunyathep S.'] | ['Mr. Worldwide'] | ['apex-chart', 'bootstrap', 'css', 'css3', 'datatable', 'express.js', 'html5', 'javascript', 'node.js'] | 124 |
9,890 | https://devpost.com/software/papure-2tpv60 | paPURE Setup - Angeled View - Utilizing Snorkeling Mask
paPURE Setup - Front View - Utilizing Snorkeling Mask
paPURE Setup - Side View - Utilizing Snorkeling Mask
paPURE Setup - Back View - Utilizing Snorkeling Mask
Original Prototype of paPURE Design View
paPURE Base - Top View - Inserted Compressor Fan and Fan Shroud
paPURE Base - Top View - Empty
Abstract:
The Filtrexa paPURE is an affordable, 3D printed powered air-purifying respirator (PAPR) that provides our healthcare providers with better protection than even N95s, especially in high-risk and confined environments (E.g. ICUs, ERs). It incorporates readily available components and can be easily manufactured locally. We can thus increase accessibility of PAPR technology by enabling hospitals to produce and purchase it as per their need, optimizing the 3D-print to produce it at a cost that is over ten times cheaper than PAPRs currently offered on the market, and using exchanging highly specific components for readily available and affordable components. The Filtrexa paPURE also has made design changes to improve comfort, ease of use, and longevity of PAPR technology.
Introduction
One of the most immediate and impactful effects of the COVID-19 pandemic are global shortages of proper personal protective equipment (PPE), forcing healthcare providers (HCPs) to consistently work in high-risk environments and unnecessarily place their own lives at risk. Our product is a powered air-purifying respirator (PAPR) that creates a positive pressure field with filtered air to protect frontline healthcare workers from airborne threats such as SARS, TB, measles, influenza, meningitis, and most immediately COVID-19. This technology improves upon current PAPR devices in terms of cost-efficacy, ease of access, and ease of implementability. Our solution not only serves to combat general PAPR shortages across the country, but also eases PPE shortages that arise from COVID-19 and future patient surges through an on-demand 3D printing process.
Value Proposition
Powered, air-purifying respirators (PAPRs) are currently the gold standard in medicine when treating patients diagnosed with COVID-19 and other highly infectious respiratory diseases[1] due to their positive pressure system. This system filters air extremely effectively before it reaches the airway. However, this technology package is costly, often totaling over $1800[2] and requires highly specific components which are currently in short supply. Both well-established hospitals such as the Mayo Clinic (with a ratio of 4500 physicians to 200 PAPRs)[2] and smaller county hospitals such as the Hunterdon Medical Center (where not a single PAPR is available to physicians) are facing critical shortages of personal protective equipment (PPE). Evidently, the aforementioned barriers render PAPR technology inaccessible to most frontline HCPs, leaving them far more vulnerable to infection.
Alternatives to PAPR technology include N95s, surgical masks, and currently, homemade masks due to a worldwide shortage of PPE. Although they provide a barrier against aerosols, standard and surgical N95s are easily compromised with an improper fit and have an assigned protection factor (APF) of ten[4], while PAPRs have an APF of 25 to 1000, rendering PAPRs far more effective at protecting HCPs. Additionally, physicians tend to prefer PAPRs over N95s because PAPRs are reusable, easier to breathe through, do not require fit testing, and make them feel safer[1][5].
Our Solution
In order to provide purified air to those in the most high-risk environments, we have developed a novel, inexpensive, and accessible PAPR device that is both lightweight and 3D-printable within 24 hours. Printed using readily-available filaments (e.g. PLA, ABS), paPURE is mounted to the user’s hip and assembled via on-hand motors and batteries. (See Appendix 2.5).
Through PAPR technology, HCPs are given access to filtered positive pressure air systems (in which airflow serves to seal any gaps in masks, as well as reduce respiratory fatigue in HCPs), drastically decreasing infection risk in areas such as ICUs and ERs.
Our device’s customizability allows for interoperability with existing masks, filters, and hosing (See Appendix 3.1), enabling hospitals, or possibly surrounding hobbyists/machinists (regulatory dependent), to produce PAPRs for their physicians and nurses. For images and procedures: See Appendix 1 and 2.
The system features a dual battery set-up that allows HCPs to utilize one or both batteries independently, as well as swap out batteries while the device is in use (such as during an extended patient procedure that a physician cannot leave from). Additionally the belt system, with the fan/chassis on you lumbar and 2 battery on ports on both hips gives a better weight distribution for improved comfort in extended usages (such as a surgeon leaning in an awkward position during the operation). The use of an inline filter means that air is pushed into a filter at the end of the device, as opposed to regular PAPRs that pull air through filters. This setup means that the risk of an imperfect seal compromising air quality is virtually nullified as no negative pressure system exists after air filtration in our device. Additionally, the aforementioned inline filters are better at filtering biological particles without disturbing airflow than standard P100s and are already used extensively in anesthesiology and respiratory care departments of hospitals across the country.
After printing the device’s chassis and shroud, integration with an inline bacterial/viral filter, housing, and masks will be followed by on-site fit and efficacy testing to ensure proper device assembly.[6] Then, an HCP would don their mask, clipping the paPURE chassis and two smart power tool batteries to a provided utility belt, and connecting to the mask via a hose. At most, we expect equipping paPURE to add 1-3 minutes to a medical professional’s routine and greatly improve safety and comfort.
An Improvement from Traditional PAPRs
Our technology eliminates the need for a middle-man manufacturer. Because the only required components are readily available to hospitals and clinics, hospitals can produce the device as per their need. We anticipate working with local 3D-printing facilities to produce and assemble the product, then to distribute the Filtrexa PAPR to hospitals. Physicians and NIOSH officials (most notably Richard Metzler, the first Director of the National Personal Protective Technology Laboratory at NIOSH), have already given us promising feedback regarding the need for this technology, and we are looking into potential partnerships with PPE developers and/or motor manufacturers. Some hospital purchasing experts have additionally communicated a need for affordable PAPRs. Our solution is over 10 times cheaper than current PAPR technologies ($155; see Appendix 2, Figure 2), increasing likelihood of adoption. To allow smaller hospitals to easily obtain our technology, we plan to raise awareness of our business through phone calls and emails to hospitals throughout the country.
Implementation Plan
paPURE’s solution is implementable almost immediately. The main barrier between our tested prototype and implementation is FDA/NIOSH approval (FDA EUA Sec II/IV Approve NIOSH Certified Respirators). We have also identified conditions that will allow us to expedite the regulation and roll-out of the production (such as the IDE and 501(k) pathways suggested to us by regulatory experts).[15] Because our device is based on existing PAPR technology, this predicate nature in combination with existing precedents for 3D-printed medical technology, can help expedite its deployment.[16]
Our technology minimizes the need for a middle-men. We are partnering with regional additive manufacturers to allow for quick, standardized, yet still decentralized production of the device. The only required components are readily available to hospitals and clinics, allowing HCPs to produce the device as per their need. Additionally, if regulatory approval permits, we may utilize local schools/universities/hospitals with on-site 3D printers in order to allow for fully decentralized manufacturing. After NIOSH Approval, our device (and depending on regulatory guidelines, possibly our CAD file) will be sent to those with 3D printers available, who could print and assemble the device (See Appendix 3.1).
Players involved in the production of this technology would be hospital assembly workers, but the design is easily assembled by anyone (the only limitation being that assembly be done under a fume hood to prevent contamination). Physicians we’ve already talked to have given us promising feedback regarding the need for this technology. We are currently looking into potential partnerships with PPE developers (See Appendix 3.2) and/or motor manufacturers. Our solution is over ten times cheaper than current PAPR technologies (See Appendix 3.3), increasing the likelihood of adoption.
Due especially to the length of this health crisis, hospitals are facing dire shortages of PPE. This has accelerated our timeline, but we are confident that it is feasible given the current state of emergency (See Appendix 3.4).
Since this product has yet to be implemented in hospitals, we are writing to you today to gauge your interest in paPURE. Additionally, any feedback you have relating to our product or interest in helping us with laboratory testing of paPURE would be greatly appreciated.
We anticipate our project to reach full fruition within 6-12 months. Our timeline is as follows. Our second iteration of prototyping for clinician testing will conclude in 2-3 weeks, followed by initial clinical testing, which will finish in around 1.5 months. As soon as clinical testing is finished and the product is validated, we will submit our product officially to NIOSH for regulatory approval. We anticipate receipt of regulatory approval within 1.5 months from submission. After approval is obtained, we will also apply for either a provisional patent or copyright, depending on legal advice. Within 1-2 months after regulatory approval, we plan to roll out our product to hospitals via centralized 3D-printing. During the next 1-2 months, we will continue to iterate and optimize the product. Official hospital rollout, with multiple 3D-printing partners and company partnerships, will occur around a month later. This will be around 6-7 months from now. As seen, our timeline is aggressive as we wish to equip healthcare providers with PPE as soon as possible. The prior goals mentioned in our timeline are our key goals and objectives for the project at this time.
Current Testing and Partnerships
Technical Testing is being carried out at Filrexa's primary residence and at Johns Hopkins University and includes analysis of airflow data, battery life, and filtration efficacy. For clinical testing, we already have established connections for clinical testing with both Johns Hopkins Medical Institute and Stanford University. In regards to business-focused assistance, we have also partnered with FastForwardU for advising regarding intellectual property protection, strategic marketing, and clinical networking.
Planned Partnerships
We plan to designate one 3D-printing company (current candidates include Xometry, Protolabs, Cowtown, and Health3D) as our manufacturer during our initial launch into the market, but will continue to partner with additional 3D-printing companies as our business grows. Due to our unique manufacturing approach, all hospitals, regardless of their size, will be able to order and quickly receive PAPRs, lowering the impact of the current shortage. In order to supply the auxiliary materials such as motors, batteries, and more, we plan to initiate company partnerships with large corporations such as 3M, Dyson, Black and Decker, GE, Cuisinart, Hitachi, Makita, Shop Vac, Hoover, Bissell, Shark, iRobot, and Bosch.
Additional Video
https://youtu.be/iFMtzt52BEQ
Appendix and Citations
Click here!
Website
paPURE Website
Built With
3dprinting
cad
cpap
p100 | paPURE | paPURE is a hospital accessible PAPR Technology utilizing 3D printing and readily available hardware to give healthcare's frontline the gold standard of personal protective equipment right now. | ['Sanjana Pesari', 'Hannah Yamagata', 'Sneha Batheja', 'Joshua Devier'] | ['2nd Place Overall Winners', '1st Place', 'The Wolfram Award', 'The Best Business Idea', '3rd Place Hack', 'Best COVID-19 Hack'] | ['3dprinting', 'cad', 'cpap', 'p100'] | 125 |
9,890 | https://devpost.com/software/covid19-predictor | Upload page
Image to upload
Prediction results
Real time location updates of covid19 cases
Specific location information on covid19
Graph curve on the spread of the virus
Latest news and information on the area selected
Covid-app 2020
Inspiration.
Since the virus in question attacks our respiratory system and hinders breathing ability, doctors have concluded that an efficient way to predict the prognosis is to examine the X-ray of patients.
We have employed Deep Learning in our application which uses the VGG16 which is a deep convolutional network for object recognition.
We have used the VGG16 as the base model and on top a custom made model to suit the requirements of the application.
DATASET USED -
Dataset of Covid patients
Accuracy obtained - 90%
Specificity - 80% i.e. People who dont have covid and are diagnosed as negative
Sensitivity - 100% i.e. People who have covid and are diagnosed as positive
which shows that there is no scope of error in the prognosis
What it does
Use of deep learning in the app to predict whether the user is prone to coronavirus.
Real time location updates of coronavirus infections all around the globe with current news on covid-19 of that location.
How I built it
The deep learning model uses VGG-16 architecture which is a deep convolutional network for object recognition developed and trained by
Oxford's renowned Visual Geometry Group (VGG)
, which achieved very good performance on the ImageNet dataset.
It is used as a base model while a top model is used to refine the model based on the requirements of this application. The weights used are pre-trained of the imagenet dataset and the architecture of the entire model is shown as below: -
picture
Challenges I ran into
The dataset is scarce due to the novelty of the virus.
Accomplishments that I'm proud of
Avoided queues needed to see an x-ray specialist in a hospital.
Avoided getting infected in the hospital which is a hot zone for getting the virus
Avoided putting load on already exhausted doctors by going to any local radiologist and uploading a scan image on our app by sitting at home
Spread awareness by showing a real time location update on the coronavirus such that you can secure your location
What I learned
Deep knowledge about covid19
Practice with deep learning networks
API interfacing (REST API)
What's next for COVID19 PREDICTOR
With time as the number of COVID19 cases increase, dataset shall increase and the accuracy of the deep learning network will improve naturally.
Deployment
The API accepting our image is deployed on
Python Anywhere
The link of our REST API is as such:
API
Libraries Used
Python 3.7.6
Tensorflow 2.1.0
Keras 2.3.1
Flask 1.1.1
Accuracy and loss plot
picture
Accuracy
picture
Application
The application is developed in flutter. It can be run simply by downloading app-release.apk and installing in your mobile phone. It accesses the API(link above) to use the trained model to predict COVID-19. A live tracker is also included in the application to keep track of status of COVID-19 cases around the world.
Flutter Dependencies
cupertino_icons: ^0.1.3
image_picker: ^0.6.3+4
webview_flutter: ^0.3.14+1
dio: ^2.1.16
http: ^0.12.0+4
flutter_spinkit: ^4.1.2
.
Built With
api
flask
flutter
python
tensorflow
Try it out
github.com
github.com | COVID19 PREDICTOR | Don't scram before you scan! Upload your scan on our application which, using a reliable deep learning network in the back-end diagnoses whether the patient is COVID positive or negative. | ['Rijul Nandy', 'Rishabh Goel'] | [] | ['api', 'flask', 'flutter', 'python', 'tensorflow'] | 126 |
9,890 | https://devpost.com/software/health_id | HEALTH_ID user interface
The HEALTH_ID system
The concept of HEALTH_ID
HEALTH_ID user interface
Inspiration
All over the world countries and people suffer from covid-19. Isolation is one answer to the crisis. But it only adresses one problem: The health problem (while economy and social life are collapsing!). We can only save our economy and our social life by getting back to normality. Even if some countries may flatten their curve drastically - Fact is that one infected person can start the whole problem all over again. That means that our rights will be ristricted for a long time from now! We cant get back to normal because we don´t know who is a threat for other people. Immune people might get back to normal life but those people simply cannot prove their immunity! That´s where HEALTH_ID enters the game...
What it does
HEALTH_ID proves the immunity of a user in everyday situations without intriguing their privacy. HEALTH_ID connects your normal personal ID-number with your covid-19-health-status. Nothing more, nothing less. Depending on your country the ID-number is a simple sequence of numbers that don´t carry any further personal information. Therefore the best tool to connect it with a sensitive personal information such as your corona-immunity-status. Only someone who sees your id-card can check your health-status. It´s like a credit card that in fact shows very sensitive data as well. If you don´t show it to anybody, nobody can harm you. Only people who know your id-number can check if you are a threat to them. Just enter a persons id number into the HEALTH_ID input field and get the state back. This certificate can be verified with a QR-Code.
With this super easy tool you can prove your immunity in everyday situations (with a QR-code) or online (by simply typing in your ID-number). With our built-in API third party platforms can check your health-state as well. Examples can be:
Digital:
Booking a flight online (The airline can check your covid-19 state with our API)
Requesting a visa online (The government can check your covid-19 state with our API)
Booking a festival/concert/theater/cinema ticket (The manager can check your covid-19 state with our API)
And plenty of other situations...
Everyday situations:
Admission and entrance controls of festivals/concerts/theaters/cinemas/shops/restaurants/hotels
Police controls
Border controls
And plenty of other situations...
Note: health and test data is provided by certified facilities such as laboratories/hospitals and doctors. The HEALTH_ID can be owned by a state or by a trustworthy non-profit-organisation.
HEALTH_ID is 100% free, 100% voluntary and 100% useful!
The only way to fully get back to our normal life is to differentiate between immune and non-immune people. Think about it! It´s so obvious!!!
How I built it
The best and secure system is a simple system. I`ve developed a front-end website (
https://www.global-health-id.com
) and a back-end database. As I´ve discribed it further on top it is simply a connection between a personal id with a state of health. The information exchange on the user side is done with a qr code which can also be scanned and verified within the HEALTH_ID platform/website. Therefore I´ve taken some board-libraries for qr-code-scanning and -generating and combined it with a database. HEALTH_ID works from EVERY smartphone that has a camera in the world via the internet. It´s made for a everyday mass-use. Even people in slums have smartphones!
Challenges I ran into
While developing the concept I put the highest emphasis on simplicity and privacy. While other countries come across a lot of apps that truly intrigue users privacy this one doesn´t. I figured out that people need 5-10 minutes to truly understand the concept behind it and then - without a doubt - realise that HEALHT_ID can be very useful.
Accomplishments that I'm proud of
Creating a dead simple concept that works.
What I learned
Creating a good concept is one thing. Convincing others is super hard.
What's next for HEALTH_ID
Obviously for HEALTH_ID to work health data is needed. Health_ID needs fast and reliable immune tests for millions of people and a facilities with special certificates. Nevertheless Health_ID can start small and verify immunity of few hundreds at first. This means that more and more people can go back to normal life. The economic and social problem is solved.
global-health-id.com
Questions or answers:
[email protected]
Thanks for your time!
Built With
javascirpt
php
Try it out
www.global-health-id.com | HEALTH_ID | HEALTH_ID is the first digital ID that proves your covid-19 immunity in everyday life withouth intriguing your privacy.... | ['Paul Metzger'] | [] | ['javascirpt', 'php'] | 127 |
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