Research on the Data Algorithm of Physical Fitness Monitoring and Injury Prevention in Track and Field Sports Based on Deep Learning

This paper proposes a physical fitness monitoring algorithm based on deep learning. By real-time collection and analysis of athletes' sports data, the physical fitness status of athletes can be accurately evaluated, so as to effectively prevent sports injuries in track and field sports. Firstly, a CNN model is designed to collect athletes' physiological parameters, movement trajectory and other data through sensors, and the key features in the data are extracted using a deep learning algorithm. Then, combined with the individual differences of athletes, the LSTM network is used to model the dynamic changes during exercise, so as to accurately predict the physical fitness level of athletes. The simulation results of the model show that the algorithm can accurately predict the physical fitness status of athletes in various sports scenarios and effectively warn potential sports injuries. The simulation data analysis shows that the deep learning model has obvious advantages in injury prediction accuracy and real-time performance compared with traditional statistical methods. The error rate of the model in data prediction is controlled within 5% through experiments, which has high practical value and promotion potential.