Inspiration

During Covid or in general working class specially IT professionals cant get time for exercise, sitting for long hours therefore they might get issues in body. So if we get them a virtual physiotherapist, so that sitting at home they can work out with proper guidance and do exercises will be beneficial.

What it does

Our application uses a tensorflow.js (browser-based) model to make predictions on the state of the current user's pose. It has been trained on a dataset of images created by us (~300 images per pose) to predict whether the position is correct, or incorrect - and what makes it so. I have used GCP Machine Learning Studio, a GCP Machine Learning tool, to train our models in the various physiotherapy poses. GCP Services Speech-to-Text API was also used to enable the application to be accessible by the visually impaired. The user can start their exercises via speech in various languages using GCPTranslator Speech API remotely and this is more convenient and easier to use for our target audience. The application utilizes GCP Services for text-to-speech. This is useful for the visually impaired as they can hear if they are in the right position as the application will tell them to adjust their posture if incorrect. We also use the webcam to track the user's movement which is fed as input to the posenet machine learning model and outputs posture image on the user's body.

How I built it

This is fully supported on Desktop/Android Google Chrome.

What's next for Physiotherapy Aid

Make it available for Gym enthusiasts

About the project :

AIDEN. Your physio assistant.

By Sanskar Jethi, Ankit Maity, Shivay Lamba

AIDEN is a web app utilising tensorflow.js, browser-based Machine Learning library, to enable accessible physiotherapy for the Visually Impaired and other people as well - talking through exercises by responding to users' postures in real-time.

AIDEN makes it easier for users to not only complete but to improve their techniques independently.

How to use AIDEN

  • Allow browser access to microphone and camera
  • Say “start exercises” or press “Start” or any particular language ( translation )
  • Try to do a “back bend stretch” approximately 8 foot away from the webcam with whole body in frame like in demo video. (only works in one orientation currently)

Technology

Machine Learning - tensorflow.js

AIDEN uses a tensorflow.js (browser-based) model to make predictions on the state of the current user's pose. It has been trained on a dataset of images created by us (~300 images per pose) to predict whether the position is correct, or incorrect - and what makes it so. We have used Azure Machine Learning Studio, an Azure Machine Learning tool, to train our models in the various physiotherapy poses. Azure Cognitive Services Speech-to-Text API was also used to enable the application to be accessible by the visually impaired. The user can start their exercises via speech in various languages using Azure Translator Speech API remotely and this is more convenient and easier to use for our target audience. The application utilizes Azure Cognitive Services for text-to-speech. This is useful for the visually impaired as they can hear if they are in the right position as the application will tell them to adjust their posture if incorrect. We also use the webcam to track the user's movement which is fed as input to the posenet machine learning model and outputs posture image on the user's body. Key Azure Services that have been used in our product:

  • Azure Storage Services - storing machine learning model ( TF)
  • Azure Cognitive Services ( Inference )
  • Text-to-Speech
  • Speech-to-Text
  • Custom Vision ( to classify between correct and incorrect images)
  • Translator
  • Azure CDN ( three js and other libraries )
  • Azure Web App with Continuous Deployment
  • Linux Virtual Machine ( for hosting the website )
  • Azure CLI ( for deployment)
  • Azure Cloud Shell (for web app continuous deployment integration)
  • Azure Pipelines (Continuous deployment feature)
  • Visual Studio Code ( for all our life <3)

Supportability

This is fully supported on Desktop/Android Google Chrome.

Client Folder

  • The web application is located in the clients folder. The web application consists of two files: index.html and index.js.

Index.html

  • The index.html contains all the HTML that forms the backbone of the website.
  • We have used the bootstrap open-source CSS framework for our front-end development.

Index.js

  • index.js contains the Javascript code for the web application. This works with HTML to add functionality to the site.
  • Loads the model and metadata and handles image data.

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