ashayp22 / HealthDESK

A website that uses deep learning and webcam data to detect bad desk habits || Won Most Scalable in Hack the Lib 2020

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HealthDESK - Using AI to ensure you are staying healthy at your desk

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Project Details

In order to help prevent the adverse effects of office work to people’s health, we created HealthDESK, a website that uses AI technologies and webcam data to determine and notify you when you have poor posture, and also, remind you to maintain healthy habits while sitting at your desk.

Hackathon

This project was submitted to HackTheLib in July 2020 and won "Most Scalable" out of 205 participants.

Links

Technologies

  • HTML/CSS
  • JavaScript
  • Tensorflow
  • OpenCV
  • Node.js
  • Express
  • Heroku

How We Built It

Using TensorFlow and OpenCV, we created an AI algorithm that can detect unhealthy habits that occur while sitting at your desk. The AI implements a variety of Computer Vision algorithms, such as Convolutional Neural Networks called ResNet-50 and MobileNet V2, along with a Cascade Classifier. After receiving bounding boxes and landmarks from these Computer Vision algorithms, we used mathematical computations to determine if a user is drinking water, stood up, touched their face, leaned in, and has bad posture.

In order to create the front end, we used JavaScript, HTML5, and CSS. We used responsive CSS and custom JavaScript to create a simple yet powerful UI/UX design. We also implemented rapid push notifications that notifies users whenever they are being unhealthy, allowing users to keep the website running in the background.

Authors

Contributing

We encourage people to contribute to our website and suggest changes. Please create a pull request and email us with your suggestion.

For major changes, please open an issue first to discuss what you would like to change.

License

GNU General Public License v3.0

About

A website that uses deep learning and webcam data to detect bad desk habits || Won Most Scalable in Hack the Lib 2020

License:GNU General Public License v3.0


Languages

Language:JavaScript 51.6%Language:CSS 32.8%Language:HTML 15.6%