Real-Time Posture Monitoring for Effective Exercise Using MediaPipe Python
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Abstract
Maintaining proper posture during exercise is crucial for preventing injuries and maximizing workout efficiency. This project aims to develop a real-time posture monitoring system using MediaPipe and OpenCV to provide instant feedback on the exercise form. The system captures video input through a webcam, processes it using OpenCV, and utilizes MediaPipe’s pose-estimation model to detect key body landmarks. By analysing the joint angles and comparing them to predefined optimal postures, the system evaluates the user’s form and provides corrective feedback in real time. This approach eliminates the need for expensive wearable sensors, making posture monitoring more accessible and user friendly. The literature review highlights the effectiveness of computer vision-based solutions in fitness applications and identifies key challenges, such as occlusions, varying lighting conditions, and real-time processing constraints. The proposed system addresses these issues by optimizing the pose-estimation accuracy and feedback mechanisms. Testing and user surveys confirmed the system’s effectiveness, achieving 90% accuracy for squat posture detection and 86% accuracy for lunges under typical home-workout conditions. The expected outcome of this project is a functional real-time exercise posture monitoring system that enhances the user training experience by ensuring a proper form. Future improvements may involve integrating machine learning techniques to personalize feedback and expand the system to multi-user environments. This project contributes to the advancement of computer vision applications in fitness and rehabilitation domains.
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