Biomechanical analysis and teaching strategies of complex movements in physical education teaching

Abstract

This study suggests a new method for evaluating students’ ordinariness of movement in professional physical education by developing an assessment algorithm based on the biomechanical analysis of complex motions. The study aims to provide purposeful and data-driven techniques for assessing and optimizing movement ability in intricate physical tasks by utilizing higher motion capture and deep learning (DL) approaches, especially the Updated African Buffalo Optimization Based Deep Convolutional Neural Network (UABO-DCNN) categorization. The method includes collecting data utilizing high-precision movement capture equipment to research certain multifaceted movements, preprocessing trajectory data to extract kinematic, temporal, and spatial information, and increasing categorization algorithms with UABO-DCNN. The consequences specify that the algorithm can differentiate between normal and abnormal association patterns with outstanding accuracy. The UABO-DCNN model measures physical education teaching complex movements with accuracy (99.43%), precision (98.12%), recall (98.50%), F1-score (98.56%), and specificity (98.40%). Furthermore, the result is reliable, with a broader tendency toward instructive skill and individualized learning, which requires the development of physical education instruction actions by creating a culture of physical literacy and well-being. The implication of this employment includes an enhanced approach to promote optimal association skill increase in students, particularly for confronting complicated biomechanical measures.