PoseCoach - The Modern Exercise Form Checker

This application is designed to provide real-time feedback on exercise form using computer vision and machine learning techniques. It consists of a backend server that processes video input and a frontend interface that allows users to interact with the system.

Repository Structure

├── README.md
├── .gitignore
├── src
│   ├── feedback_ai.py
│   ├── form_checkers
│   │   ├── __init__.py
│   │   ├── _utilityFunctions.py
│   │   ├── benchpress_formChecker.py
│   │   ├── bentOver_FormChecker.py
│   │   └── squat_formChecker.py
│   ├── frontend
│   │   ├── eslint.config.mjs
│   │   ├── next.config.ts
│   │   ├── package-lock.json
│   │   ├── package.json
│   │   ├── postcss.config.mjs
│   │   ├── public
│   │   ├── src
│   │   │   └── app
│   │   │       ├── components
│   │   │       │   ├── countdown.tsx
│   │   │       │   ├── exercise-video.tsx
│   │   │       │   ├── formChecker_button.tsx
│   │   │       │   ├── navbar.tsx
│   │   │       │   └── webcam-feed.tsx
│   │   │       ├── exercises
│   │   │       │   ├── bench-press
│   │   │       │   │   ├── form_checker
│   │   │       │   │   │   └── page.tsx
│   │   │       │   │   └── page.tsx
│   │   │       │   ├── bent-over-barbell-row
│   │   │       │   │   ├── form_checker
│   │   │       │   │   │   └── page.tsx
│   │   │       │   │   └── page.tsx
│   │   │       │   └── squat
│   │   │       │       ├── form_checker
│   │   │       │       │   └── page.tsx
│   │   │       │       └── page.tsx
│   │   │       ├── globals.css
│   │   │       ├── layout.tsx
│   │   │       └── page.tsx
│   │   └── tsconfig.json
│   ├── init_functions.py
│   ├── pose_estimator.py
│   ├── pose_estimator_supine.py
│   ├── requirements.txt
│   └── server.py
├── trained_models
│   └── yolo26s-pose.pt

How to use it

Create a venv and download the required libraries:

python3 -m venv .venv
source .venv/bin/activate
cd src
pip install -r requirements.txt

Prerequisites

To test the application, it is required that the backend as well as the frontend uses a secure tunnel to be able to request camera access on a webbrowser. Follow the next steps to ensure that the whole application is secure:

First Terminal:

cd src
uvicorn server:app --reload --port 8000

Second Terminal:

macOs:

brew install cloudflared
cloudflared tunnel --url http://localhost:8000

Windows

winget install --id Cloudflare.cloudflared
cloudflared tunnel --url http://localhost:8000

Third Terminal:

Copy the link from your cloudflare Tunnel and reuse it.

cd src/frontend
NEXT_PUBLIC_BACKEND_WS_URL=YOUR_CLOUDFLARE_LINK  npm run dev

Fourth Terminal

You can use ngrok. For more information look up the ngrok Website or if you have already installed ngrok do following:

ngrok http 3000