HIIT PI

HIIT PI is a Dash app that uses machine learning (specifically pose estimation) on edge devices to help track your HIIT workout progress in real time (~30fps). The backend runs everything locally on a Raspberry Pi while you interact with the app wherever there is a web browser connecting to the same local network as the Pi does.

How it works

• Video demo
• Blog post

Hardwares

• Raspberry Pi (Pi 4 recommended)
• Raspberry Pi Camera Module v2
• Google's Coral USB Accelerator (Edge TPU)

Softwares

• Raspbian 10 Buster
• Docker & Docker Compose
• Python 3.7+

Usage

1. SSH into your Raspberry Pi and clone the repository.

2. Install Docker & Docker Compose.

3. Build our Docker images and spawn up the containers with

   $ docker-compose -d --build up 
   

4. (Optional) For maximum performance, swap the standard Edge TPU runtime library libedgetpu1-legacy-std with libedgetpu1-legacy-max

   1. get into the shell of container web-hiitpi by

      $ docker exec -it web-hiitpi bash
      

   2. run the following inside the container

      $ DEBIAN_FRONTEND=dialog apt-get install -y libedgetpu1-legacy-max
      

      Note: select yes and hit ENTER in the interactive installation process.
   3. restart the web service after the above install finishes

      $ docker-compose restart web 
      

5. Go to <your_pis_ip_address>:8050 on a device in the same LAN as the Pi's, and then enter a player name in the welcome page to get started.

6. The live-updating line graphs show the model inferencing time (~50fps) and pose score frame by frame, which indicates how likely the camera senses a person in front.

7. Selecting a workout from the dropdown menu starts a training session, where your training session stats (reps & pace) are updating in the widgets below as the workout progresses. Tap the DONE! button to complete the session, or EXIT? to switch a player. Click LEADERBOARD to view total reps accomplished by top players.

Notes

• This project currently has implemented a couple of workouts to play with, and we're planning to expand our workout repertoire as it evolves over time.