摘要
运动员心肺功能检测是科学合理制定训练计划的关键。针对现有心肺功能检测方法误差较大的问题,提出了基于粒子群优化的多元线性回归心肺功能检测方法。该方法以氧气摄入量、二氧化碳排出量和心率作为评价运动员心肺功能指标,基于多元线性回归构建了运动心肺功能检测模型。针对多元线性回归方法收敛速度慢和容易陷入局部最优的问题,利用粒子群算法进行优化,提升心肺功能检测的速度和精度。实验结果表明,该方法能够有效运动员有氧训练前后的心肺功能,且建模精度较高。
Athletes’cardiopulmonary function test is the key to scientifically and reasonably formulate training plans.Aiming at the problem of large errors in existing methods of cardiopulmonary function test,a method of multiple linear regression cardiopulmonary function test based on particle swarm optimization is proposed.In this method,oxygen intake,carbon dioxide output and heart rate are used as indexes to evaluate athletes’cardiopulmonary function.Based on multiple linear regression,a test model of exercise cardiopulmonary function is constructed.Aiming at slower convergence speed and easy to fall into local optimum of multiple linear regression method,particle swarm optimization is used to improve the speed and accuracy of cardiopulmonary function test.The experimental results show that this method can effectively improve the cardiopulmonary function of athletes before and after aerobic training,and the modeling accuracy is relatively high.
作者
史岩峰
SHI Yan-feng(Shaanxi Industrial Vocational and Technical College,Xianyang 712099,Shaanxi Province,China)
出处
《信息技术》
2019年第8期130-133,共4页
Information Technology
关键词
有氧训练
心肺功能检测
多元线性回归
粒子群算法
aerobic training
cardiopulmonary function test
multiple linear regression
particle swarm optimization
