摘要
针对传统运动性疲劳检测存在检测生理参数单一、实时性差等问题,文中进行了多生理参数和动态条件下的疲劳检测研究。实验测量多个实验对象在其运动过程中的心率,血氧饱和度值,运动能耗等多项生理参数。同时,通过问卷调查和分析生理现象来得到相应的疲劳值。利用以上数据训练BP神经网络,训练后的神经网络能够根据多项生理参数较好地预测出人体运动时的疲劳值,其误差小于20%,达到运动性疲劳检测的要求。这种方法对运动型疲劳的检测研究具有重要参考意义。
Traditional kinematic fatigue testing has many problems, for instance the limitation of detecting multiple physiological parameters at the same time and the under-performance in real-time.This paper presents to test the fatigue,when people do exercise,based on multi-physiological parameters. Heart rate, hemoglobin oxygen saturation, exercise energy consumption etc.,these physiological parameters were measured in this experiment,The corresponding fatigue data obtained by questionnaire investigation and physiological analysis were also taken into consideration. The above data were used to train BP Neural Networks. The trained neural network can predict the fatigue value of human body according to a number of physiological parameters,and the error ratio is less than 20%,which meets the requirement of exercise fatigue testing. This method has important reference significance for the study on sports fatigue.
出处
《信息技术》
2017年第11期121-124,128,共5页
Information Technology
关键词
运动性疲劳
多生理参数
BP神经网络
sports fatigue
multiple physiological parameters
BP neural networks
