Real-Time Exercise Tracking System

Abstract The increase in popularity of fitness and at-home fitness has illuminated a major deficiency with traditional fitness tracking; the absence of immediate motion analysis and corrective feedback. Many conventional methods of instruction, as well as wearables, lack the system feedback of prompting users for proper movements, which can lead to many real workout injuries. The proposed system is the "Real-Time Exercise Tracking System". The proposed project is an intelligent method of intervention that uses computer vision and deep learning to provide a solution to the problem of lacking immediate feedback. The objective of the system is to accurately assess the form a user is exercising in, and properly provide the user with effective feedback on their form in real-time. The system utilizes a combination of MediaPipe for estimating accurate user pose, and a machine learning model to assesses the required movements of the user. The application provides the user with real-time voice and visual feedback on motion awareness. The application will assess the user's exercise awareness in terms of AI-based form correction, real-time performance analytics, propose new workouts based on previous fitness activities, and mitigate risk of injuries with alerts. The project will contribute to filling the gap between unmonitored home activity and professional fitness education, thus creating a safer, more effective, and happy fitness experience for users. Keywords – Real-Time Tracking, Pose Estimation, Computer Vision, MediaPipe, Fitness Technology, Deep Learning, Form Correction