An intelligent personal training system based on AI that uses computer vision to analyze posture, count exercises, and provide real-time feedback.

• Posture Detection: Analysis of 33 body points using MediaPipe
• Automatic Counting: Counts push-ups and other exercises automatically
• Angle Analysis: Calculates angles between joints to verify correct form
• Web Interface: Interactive dashboard with Streamlit for real-time visualization
• Video Processing: Support for video files (.MOV, .MP4)
• Real-time Metrics: Movement graphs and exercise statistics

• Python 3.11+
• MediaPipe - AI framework for computer vision
• OpenCV - Video and image processing
• Streamlit - Interactive web interface
• Pandas & NumPy - Data manipulation and analysis
• Matplotlib - Data visualization

• Python 3.11 or higher
• Webcam or video file for analysis
• Internet connection for MediaPipe model download

1. Clone the repository

git clone https://github.com/seu-usuario/ai-personal-trainer.git
cd ai-personal-trainer

2. Install dependencies

pip install -r requirements.txt

3. MediaPipe model download The pose_landmarker_full.task model is already included in the repository.

python pose_estimator.py

python personal_ai.py

streamlit run personalai_dash.py

AI Personal Trainer/
├── personal_ai.py # Core AI system
├── personalai_dash.py # Web interface with Streamlit
├── pose_estimator.py # Basic detection module
├── pose_landmarker_full.task # MediaPipe model
├── requirements.txt # Python dependencies
├── IMG_2149.mov # Example video
└── README.md # This file

• Supported formats: .MOV, .MP4, .AVI
• Recommended resolution: 720p or higher
• Recommended FPS: 24-30 fps

• Shoulders: Points 11 (right) and 12 (left)
• Hips: Points 23 (right) and 24 (left)
• Elbows: Points 13 (right) and 14 (left)
• Total: 33 body points

• Push-ups: Automatic counting based on elbow angles
• States: "relaxed", "ready", "up"/"down" direction
• Metrics: Real-time angles, repetition counter

• Body landmarks drawn on video
• Real-time angle display
• Movement graphs of body points
• Dashboard with statistics

• Display Charts: Enable/disable movement graphs
• Reset: Restart counters and metrics
• Status: Show current exercise state
• Count: Display repetition count

from personal_ai import PersonalAI
# Initialize system
trainer = PersonalAI("your_video.mov")
# Process video
trainer.process_video(display=True)
# Run in background
trainer.run()

# Start dashboard
streamlit run personalai_dash.py
# Access in browser
# http://localhost:8501

• Asynchronous Processing: Queue system for better performance
• Multiple Exercises: Support for different types of movements
• Quality Analysis: Verification of correct form and posture
• Data Export: Metrics saved for later analysis

pip install mediapipe --upgrade

• Check if video file exists
• Confirm correct path in code
• Test with different video formats

• Reduce video resolution
• Use GPU if available
• Adjust FPS for processing

1. Fork the project
2. Create a feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

This project is under the MIT license. See the LICENSE file for more details.

• MediaPipe for posture detection technology
• Google for the AI framework
• Streamlit for the web interface
• Python Community for support

• Author: [Guilherme Gomes]
• Email: [guilherme@mymesh.com.br]
• GitHub: [@devhttps]
• LinkedIn: [https://www.linkedin.com/in/erro404/]

• Support for more exercises
• Advanced biomechanics analysis
• Wearable integration
• Mobile app
• REST API
• Custom Machine Learning

⭐ If this project helped you, consider giving it a star on GitHub!