Research on human movements push up counter based on media-pipe artificial intelligence

This paper introduces a project that integrates pose estimation and deep learning to evaluate exercise performance and provide real-time feedback on repetition counts during workouts. Using machine learning in the fitness sector, the study focuses on accurately tracking and counting exercise repetitions, which is beneficial for physical education and competitive fitness events. The paper presents a novel real-time push-up counting method based on 2D video analysis, utilizing Media-pipe for precise pose estimation and motion analysis. By following the push-up guidelines set by the National Academy of Sports Medicine, the method differentiates between correct and incorrect push-up forms. The experimental results show that the proposed method achieves an accuracy of 90.00%, precision of 91.67%, recall of 91.67%, and an F1-Score of 91.66%. The integration of pose estimation and deep learning in the fitness industry offers new opportunities for improving workout monitoring, ensuring proper form, and optimizing fitness outcomes for individuals of different fitness levels.