依据反向传播神经网络建模预测骨骼肌的最佳功率负荷

Optimal power load forecasting of the skeletal muscle based on back propagation neural network

背景:研究表明最佳功率负荷力量训练可以有效增大骨骼肌输出功率、促进健康、提高运动表现,但如何快速确定最佳功率负荷是力量训练实践中经常遇到的难题,也是国内外学者研究的热点问题。目的:利用反向传播神经网络建模研究最大力量、身高、体质量与最佳功率负荷之间的数学关系,以构建预测骨骼肌最佳功率负荷的模型。方法:招募52名男性大学生受试者(测试对象46人,预测对象6人),对受试者进行最大力量测试和最大输出功率测试,构建基于误差反向传播校正训练算法的最佳功率负荷预测模型,采用训练好的反向传播神经网络模型预测新样本骨骼肌的最佳功率负荷,并探讨反向传播神经网络模型预测效果。研究方案的实施符合北京体育大学的相关伦理要求,参与者均知情同意。结果与结论:①运用反向传播神经网络强大的自学习及推理能力,构建了包含3个输入层、10个隐含层和1个输出层的骨骼肌最佳功率负荷预测模型;②不同力量训练手段预测精度方面,卧推抛和半蹲起相对误差均值均为9%,绝对误差均值分别为3.79 kg和6.91 kg;③结果提示,反向传播神经网络预测法可有效预测骨骼肌最佳功率负荷,使得骨骼肌最佳功率负荷的确定方式更具多元化、智能化。

BACKGROUND: Optimal power load strength training can effectively increase the output power of skeletal muscle, promote health and improve sportsperformance. However, how to quickly determine the optimal power load is often a difficult problem in the practice of strength training, and is also a hot topicin the research of scholars at home and abroad.OBJECTIVE: To study the mathematical relationship between maximum strength, height, weight and optimal power load by using back propagation neuralnetwork modeling, so as to build a model to predict the optimal power load.METHODS: Fifty-two subjects (46 subjects for test, 6 subjects for forecast) were recruited. The maximum strength test and maximum power output test werecarried out on the subjects to construct the optimal power load forecasting model based on error back propagation correction training algorithm, and thetrained back propagation neural network model was used to predict the optimal power load in the new sample to explore the prediction effect of the model.RESULTS AND CONCLUSION: Using the strong self-learning and reasoning ability of back propagation neural network, the optimal power load forecasting model wasconstructed with 3 input layers, 10 hidden layers and 1 output layer. In terms of the prediction accuracy of different strength training methods, the mean relative errorof bench press throw and half squat is 9%, and the mean absolute error is 3.79 kg and 6.91 kg respectively. Back propagation neural network prediction method caneffectively predict the optimal power load, which makes the determination method of optimal power load more diversified and intelligent.