Intelligent Algorithm Based Personalized Training Plan Generation and Implementation Strategy for Tennis Physical Education

Abstract

 In the era of mobile internet, data-driven precision training represents a significant trend in the evolution of technical training from traditional experience-based instruction to digital intelligent precision training. The application of intelligent algorithms in the design and implementation of personalized training programs for tennis constitutes the application of data-driven precision training enabled by intelligent technology in tennis instruction and training. The study employs intelligent wearable devices and computer vision algorithms during training to collect parameters such as athletes' physiological data and technical movement parameters. Data is analyzed and mined using swarm intelligence optimization algorithms and deep learning training models, enabling dynamic adjustments and the personalized formulation of scientific training plans. Research findings indicate that intelligent training algorithms can enhance training effectiveness, technical movement standardization, and prevent sports injuries. Athletes in the intelligent training group achieved significant improvements in metrics such as serve accuracy, forehand hitting power, and backhand return success rate compared to those in the traditional training group. Additionally, data on heart rate, blood oxygen recovery time, and the incidence of sports injuries showed a significant reduction. The intelligent video game-assisted training unit designed in this study demonstrates unique effectiveness in cultivating athletes' tactical awareness and the coordination of movements across different body parts. In the comparison of tennis training data, extensive training data confirms the effectiveness of intelligent algorithms in tennis teaching and training, providing new directions for the future development of intelligent algorithms in tennis teaching and training, as well as technical training methods.