Athletes’ Physical Fitness Evaluation Model based on Data Mining

Abstract

This study firstly introduces the design and implementation of a physical health monitoring bracelet and describes in detail how the bracelet collects multi-dimensional physiological data such as heart rate, step number and sleep quality of athletes. This paper then discusses the application of data mining algorithms in processing these data, including cluster analysis, classification and prediction models, to identify the key influencing factors of athletes’ physical fitness. This paper uses the RFID anti-collision algorithm to improve the accuracy and efficiency of data acquisition, avoiding the error and delay that may occur in traditional methods. The practicability and validity of the model are verified by system simulation. The simulation results show that the model can accurately evaluate the athletes’ physical fitness and provide real-time feedback and personalized training suggestions for the coaching team. This not only helps athletes improve their competitive performance but also prevents sports injuries during daily training.