AI-Powered Workout Analysis Application for Posture Feedback and Repitition Grading

The AI-powered exercise tracking system presented in this project represents a new approach to revolutionize fitness routines using cutting-edge technologies. Leveraging advanced computer vision techniques, particularly pose estimation, the system offers real-time tracking and analysis of exercise repetitions during gym workouts. Focused initially on four fundamental exercises like Bicep Curls, Push-Ups, Squats, and Shoulder Press. The proposed system employs Machine Learning (ML) models to detect and classify correct and incorrect exercise forms. Integrating the power of Mediapipe's pose detection framework and speech recognition capabilities, the proposed system provides accurate feedback and guidance to users through a smart web application interface. Through the selection of a tech stack including Python, OpenCV, Streamlit, and Scikit-Learn, the system ensures seamless data processing, model training, and user interaction. By analyzing workout videos uploaded by users, the proposed system identifies and highlights areas of improvement in exercise technique, enhancing the effectiveness and safety of fitness routines. Moreover, the integration of speech recognition enables hands-free control, adding convenience and accessibility for users during their workouts. This research study not only aims to optimize individual fitness journeys but also contributes to advancing the field of fitness tracking through the application of Artificial Intelligence (AI) and computer vision technologies. By empowering users with personalized feedback and guidance, the proposed system promotes healthier habits, improved performance, and overall well-being.