Application of Biomechanics-Based Data Mining Technology in Personalized Training of Physical Education

Abstract

In recent years, the rapid development of data mining technology, machine learning (ML) algorithms, and advancements in biomechanics research have provided a new opportunity to realize personalized physical training. This study explores how to apply data mining technology to optimize physical education. By comprehensively considering biomechanical principles, it designs a more scientific and personalized training program that improves the training effect, optimizes the allocation of resources, and effectively prevents sports injuries; this program also enhances the overall athletic performance from a biomechanical perspective. Therefore, this study collects multi-dimensional data covering athletes' physical condition, sports performance, and training feedback. It also analyzes them using various ML algorithms, including but not limited to support vector regression (SVR), decision tree, and support vector machine (SVM) to tailor the optimal training plan for each athlete. Studies show that data-driven personalized training significantly improves athletes' performance, reduces injury frequency, and enhances psychological state scores. At the same time, applying data mining technology has greatly boosted the decision support system's training efficiency and effectiveness. Coaches can now make more informed decisions based on a comprehensive understanding of athletes' physical and biomechanical characteristics. The results of this study provide strong support for future sports training and competitions, highlighting the importance of combining modern data-analysis techniques with biomechanical insights in sports science.