Athletes' State Monitoring under Data Mining and Random Forest

Abstract

The study aims to train athletes to be in top form and at their best in the competition. Based on the relevant theoretical research, archers are taken as the research subjects, the characteristics of archery are analyzed, and the electroencephalogram (EEG) features of the athletes in different stages of precompetition training are monitored. And the athletes' competitive state monitoring model based on random forest (RF) is implemented and tested. The experimental results show that the athletes' dominant frequency of brain band α, EEG entropy, central fatigue index, excitation inhibition index, and cerebral state index in precompetition training is significantly different from those in training (P<0.05).The monitoring model implemented classifies athletes' competitive states. Compared with the support vector machine (SVM) classification model, its classification accuracy is higher than 90%. The overall classification accuracy is 89.74%, more significant than SVM. The research provides a reference for monitoring athletes' competitive states and helps them regulate their states in real time.