Real-Time Indoor Workout Analysis Using Computer Vision and MediaPipe

To maximise results and avoid injuries, proper form and technique must be used during workouts. One promising way to tackle this issue is to include technology into fitness tracking. In this paper, we offer a real-time indoor workout analysis system using the Media Pipe framework and computer vision algorithms. Without the need for specialised tracking hardware, our solution uses computer vision and machine learning to deliver users precise and instant feedback on how well they worked out. We provide a thorough explanation of our system architecture, which consists of important parts including workout identification, pose estimation and performance assessment. Your work advances computer vision-based fitness solutions and presents a viable method for improving the efficiency and accessibility of indoor exercise programmes. In order to enhance the system's functionality and encourage good exercise habits, we go over possible uses and future possibilities, such as interactive coaching tools and tailored workout recommendations.