Research on injury prevention strategies of biomechanical analysis in physical education teaching

Physical education instruction has several issues on injuries as a result of interruptions to the learning, participation, or physical activity of the students. Existing strategies concern the risk prevention and warm-up activities without addressing personal characteristics and anatomical and kinematic prerequisites that underlie the occurrence of injury. The biomechanical evaluation of human motion can determine factors such as joint stresses, muscle loads, and motion patterns. This proceeding strives at eradicating the aspects of biomechanics in expanding the protective measures of injury prevention in physical education teaching. Beside the motion capture system, the force plate measurement and the electromyography (EMG) data movement patterns and joint load are measured biomechanically as accurately as possible. The accumulative data have to pass through cleaning and standardization steps to provide a certain level of reliability. In this case, the use of Fast Fourier Transform (FFT) extracts features of the movements relating to the frequency domain to undergo further analysis. Subsequently, an efficient Earthworm Optimized Graph Neural network (EEO-GNN) is employed to identify injury risk elements through modeling complex biomechanical relationships and patterns. The EEO-GNN model efficiently predicted ability injury hotspots by analyzing joint stresses, muscle activation, and motion irregularities. It is surpassing previous approaches in terms of F1-score (96.2%), recall (95.2%), accuracy (96%), and precision (95%). It underscores the ability to integrate superior biomechanical analysis and deep learning procedures to enhance injury prevention, enhance motion mechanics, and foster safer and greater effective physical education environments.