Application of machine learning in safety evaluation of athletes training based on physiological index monitoring

Abstract

Abstract In order to realize the monitoring and adaptive evaluation of athletes training safety, the physiological index monitoring and training safety evaluation method of athletes based on machine learning is put forward. Taking the physiological index parameters such as heart rate HR, maximum oxygen uptake VO2max, oxygen pulse O2P, respiratory entropy RQ, maximum ventilation (VEmax) as constraint indexes, the physiological index monitoring big data analysis model of athlete training safety evaluation is constructed. The multivariate index joint analysis modeling method is used to reconstruct the characteristics of athletes physiological index monitoring, the related characteristic quantities of athletes physiological index monitoring data are extracted, the correlation of athletes physiological indexes is analyzed by using association rule reconstruction method, and the adaptive training of athletes physiological index monitoring and safety evaluation is carried out combined with machine learning method. The statistical analysis and optimization control model of athletes training safety evaluation is constructed, and the physiological index monitoring and training safety evaluation of athletes are realized under machine learning. The simulation results show that the confidence level of this method is high and the convergence of the evaluation process is good, so it has a good application value in athletes training and physiological monitoring.