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Motion-Mentor

Description: The exercise detection system combines OpenCV for video capture and processing, MediaPipe for accurate pose estimation, and a user-friendly interface built with CustomTkinter. It detects key body landmarks and calculates joint angles to assess exercise form, offering real-time feedback on performance. Users can select between push-ups and squats, with the system counting repetitions and providing guidance on maintaining proper posture. The project aims to enhance workout efficiency by making fitness training more interactive and accessible, ultimately promoting better exercise habits and reducing the risk of injury. Modules Required for use: OpenCV MediaPipe Custontkinker

Only one file to run the program: main.py

🎥 Watch the demo video on Google Drive

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This project implements an exercise detection system that uses computer vision to analyze push-ups and squats in real-time. By leveraging pose estimation technology, it provides users with immediate feedback on their form, helping them improve their technique and track repetitions effectively.

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