Comprehensive evaluation of physical education based on personalized training plan generation algorithm and biomechanics

This work builds on advancements in biomechanics and artificial intelligence to develop personalized training plans, enhancing physical education by optimizing movement performance and reducing injury risks. However, limitations include reliance on accurate biomechanical data, potential algorithmic bias in training plan personalization, and challenges in integrating real-time feedback from wearable devices. The aim is to establish a comprehensive evaluation framework for physical education, leveraging personalized training algorithms and biomechanics to enhance performance and create tailored data-driven exercise plans. We propose the Versatile Hunter-Prey Optimizer-tuned Intelligent CNN (VHO-ICNN) to optimize ICNN parameters through VHO algorithms, thereby improving performance analysis, movement optimization, and injury prevention in athletes. The BFP and BMI datasets contain data for various human features and are utilized for biomechanical analysis and optimizing physical activities in sports and education. To preprocess the data, we employ z-score normalization to standardize joint position data, ensuring uniformity across features. Additionally, the Fourier Transform is applied for feature extraction, allowing us to analyze the frequency components of movements and enhance the model's performance. After evaluation, the results demonstrate an F1-score of 92.37%, accuracy of 93.41%, recall of 96.22%, and precision of 92.95%. The results indicate that the VHO-ICNN significantly improves classification accuracy and reduces injury risk, demonstrating its potential as a powerful tool in physical education. At the cell molecular biomechanics level, cells in tissues like muscles and ligaments are affected by mechanical forces during exercise. These forces can change how molecules in cells work. When we design personalized training, understanding these cell changes can help. If we know how cells react to different forces, we can make better training plans. This can make muscles stronger and less likely to get injured. It also ties in with the data we get from biomechanical analysis and the algorithms we use. So, adding cell molecular biomechanics knowledge makes our approach to physical education and athlete training even better.