Yoga pose recognition and motion analysis for a home-based fitness monitoring and health management system
摘要
This study explores AI applications in motion recognition, focusing on the development of a home-based fitness monitoring and health management system that eliminates the need for a gym. By integrating dynamic exercises (e.g., Knee Lifts, Bicep Curls) with static yoga poses (e.g., Tree Pose, T-pose, Warrior Pose), the system provides real-time feedback to enhance workout effectiveness and overall health outcomes. OpenPose-based keypoint detection enables precise pose analysis, supporting personalized fitness plans tailored to individual users. A scoring system quantifies pose accuracy, ensuring proper form and minimizing injury risk. The implementation of OpenPose’s predictive model has yielded promising results, achieving a prediction accuracy of 99.9%. This system serves as a foundational step toward automated image and video analysis for both dynamic exercises and static yoga poses, making personalized fitness training more accessible and effective in home environments.