AI Human Fitness Tracker using Computer Vision with MediaPipe

In recent years, the integration of artificial intelligence (AI) into health and fitness domains has significantly enhanced personal training and physical wellness monitoring. This research introduces an AI-powered Fitness Tracker system that utilizes computer vision and pose estimation techniques to detect human body posture and accurately count repetitions or steps for various physical exercises. The system leverages MediaPipe for real-time human pose detection, computing joint angles to analyse movements and classify exercises such as push-ups, pull-ups, squats, sit-ups, and walking. It incorporates audio feedback for correct posture recognition and rep completion, enhancing user engagement and form correction. The architecture is modular, consisting of key components: pose detection, angle calculation, exercise classification, and feedback generation. This approach minimizes the need for external sensors or wearable devices, offering a non-intrusive, camera-based solution that is both accessible and scalable. The proposed system aims to assist users in performing workouts with improved accuracy and consistency, promoting a more effective and injury-free fitness routine.