Application of machine learning in motion capture and technical movement diagnosis of football players

This study combines motion capture systems and machine learning methods to explore the construction of high-precision capture and diagnosis models and their applications in football. Motion capture technology relies on optical sensors and inertial sensors to obtain multi-dimensional motion data of athletes. Through data preprocessing and feature extraction, machine learning algorithms classify and analyze movements. This paper designs a technical motion diagnosis model based on deep learning to identify detail deviations in football shooting movements and optimize the training process. Experimental verification shows that the model is superior to traditional methods in terms of data capture accuracy and diagnostic accuracy, and can effectively improve the standardization and expressiveness of football players' movements. The study summarizes the advantages of machine learning in technical motion capture and diagnosis, proposes future improvement directions, and provides intelligent solutions for football training and technical analysis.