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Sports_android

🏋️ AI Fitness Monitor - Real-time pose detection for exercise analysis. Android app streams camera to Flask server using MediaPipe for instant squat counting & jump height measurement with skeleton overlay feedback. Built with Python, Kotlin, CameraX & OpenCV.

#ai#android#body-tracking#camerax#computer-vision#exercise-tracking

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README preview

AI Fitness Monitor

Real-time AI-powered fitness monitoring system using pose estimation

Features • Installation • Usage • API • Architecture • Contributing

🎯 Overview

AI Fitness Monitor is a comprehensive fitness tracking system that uses computer vision and machine learning to analyze exercise form in real-time. The system consists of a Python server with pose detection capabilities and an Android mobile application for user interaction.

Key Features

Real-time Squat Analysis: Tracks squat depth, knee angles, and form with instant feedback
Vertical Jump Measurement: Measures jump height using pose estimation
Voice Feedback: Audio cues to guide proper exercise form
Mobile Integration: Android app for convenient mobile workout tracking
WebSocket Streaming: Low-latency video processing for smooth experience

📁 Project Structure

SIH_SPORTS_proj/
├── server/                     # Python backend server
│   ├── src/
│   │   ├── analyzers/         # Exercise analysis modules
│   │   │   ├── squat_analyzer.py
│   │   │   └── jump_analyzer.py
│   │   ├── api/               # Flask API routes
│   │   │   └── routes.py
│   │   └── utils/             # Utility modules
│   │       ├── camera_manager.py
│   │       └── frame_processor.py
│   ├── config/                # Configuration files
│   │   └── settings.py
│   ├── templates/             # HTML templates
│   ├── tests/                 # Unit & integration tests
│   ├── requirements.txt       # Python dependencies
│   └── run.py                 # Main entry point
│
├── android_app/               # Android mobile application
│   ├── app/src/main/
│   │   ├── java/             # Kotlin source code
│   │   └── res/              # Android resources
│   ├── build.gradle.kts      # Gradle build config
│   └── USER_MANUAL.md        # User documentation
│
├── builds/                    # Pre-built APK files
│   └── FitnessMonitor.apk
│
├── docs/                      # Additional documentation
│   ├── API.md                # API reference
│   └── ARCHITECTURE.md       # System architecture
│
└── README.md                  # This file

Clone the repository

git clone https://github.com/your-repo/ai-fitness-monitor.git
cd ai-fitness-monitor

Create virtual environment

python -m venv venv

# Windows
venv\Scripts\activate

# Linux/macOS
source venv/bin/activate

Install dependencies

cd server
pip install -r requirements.txt

Run the server
```
python run.py
```
The server will start at http://0.0.0.0:5000

Install pre-built APK
- Transfer builds/FitnessMonitor.apk to your Android device
- Enable "Install from unknown sources"
- Install the APK
Or build from source
```
cd android_app
./gradlew assembleDebug
```

Method	Endpoint	Description
GET	`/`	Web interface
GET	`/squat_feed`	MJPEG squat analysis stream
GET	`/jump_feed`	MJPEG jump analysis stream
POST	`/process_frame`	Process mobile camera frame
POST	`/reset_analyzer`	Reset analyzer state

{
  "image": "base64_encoded_jpeg",
  "type": "squat" | "jump"
}

{
  "image": "base64_encoded_result",
  "count": 5,
  "stage": "UP",
  "feedback": "Good form!"
}

┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Android   │────▶│   Flask     │────▶│  MediaPipe  │
│     App     │◀────│   Server    │◀────│    Pose     │
└─────────────┘     └─────────────┘     └─────────────┘
      │                    │                    │
      │   HTTP/WebSocket   │    Pose Detection  │
      │                    │                    │
      ▼                    ▼                    ▼
┌─────────────┐     ┌─────────────┐     ┌─────────────┐
│   Camera    │     │   OpenCV    │     │   Numpy     │
│   Frame     │     │  Processing │     │   Arrays    │
└─────────────┘     └─────────────┘     └─────────────┘

cd server

# Run all tests
pytest

# Run with coverage
pytest --cov=src tests/

# Run specific test file
pytest tests/test_analyzers.py -v

Sports_android

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README preview

AI Fitness Monitor

🎯 Overview

Key Features

📁 Project Structure

🚀 Installation

Prerequisites

Server Setup

Android App Setup

📱 Usage

Quick Start

Server Modes

Exercise Guidelines

Squats

Vertical Jumps

🔌 API Reference

Endpoints

POST /process_frame

🏗️ Architecture

Key Components

🧪 Testing

🤝 Contributing

📄 License

🙏 Acknowledgments