基于表面肌电信号及肌肉疲劳的上肢肌力预测  

Prediction of upper extremity muscle strength based on surface EMG signal and muscle fatigue

作　　者：隋修武[1,2] 高俊杰 梁天翼 蔡俊杰 王涛 Sui Xiuwu;Gao Junjie;Liang Tianyi;Cai Junjie;Wang Tao(School of Mechanical Engineering,Tian Gong University,Tianjin 300387,China;Tianjin Modern Electromechanical Equipment Technology Key Laboratory,Tianjin 300387,China)

机构地区：[1]天津工业大学机械工程学院,天津300387 [2]天津市现代机电装备技术重点实验室,天津300387

出　　处：《电子测量技术》2024年第5期181-187,共7页Electronic Measurement Technology

基　　金：中国航空科学基金(201729Q2001)项目资助。

摘　　要：为解决目前肌肉力测量时用肢体末端力表示实际肌肉力大小,以及未将肌肉疲劳程度考虑在内的问题,本文提出了一种基于表面肌电信号和肌肉疲劳的上肢肌肉力预测方法。利用AnyBody软件建立上肢肌肉骨骼模型,并将上肢末端力经过仿真得到单块肌肉的肌力大小;采用肌肉等长收缩的时间来表征肌肉疲劳程度。10名健康男性受试者进行上肢等长收缩实验,提取实验过程中肱二头肌肌电信号的积分肌电值、均方根、中值频率、平均功率频率、最大小波系数及其对应频率六个特征值;将肌肉力与特征值、肌肉疲劳程度进行分析后发现三者之间高度相关。采用麻雀搜索算法优化BP神经网络的权值和阈值,构造并训练上肢肌力预测模型。经测试集检验结果表明,该方法的误差小于12%,可以对肌力进行较为准确的预测。In order to solve the problem that the actual muscle force is represented by the extremity force and the degree of muscle fatigue is not taken into account in muscle force measurement,this paper studies an upper limb muscle force prediction method based on surface EMG and muscle fatigue.The musculoskeletal model of upper limb was established by AnyBody software,and the muscle force of a single muscle was obtained by simulation of the end force of upper limb.The time of isometric muscle contraction was used to characterize the degree of muscle fatigue.Ten healthy male subjects were subjected to the upper limb isometric contraction experiment,and six eigenvalues of integrated electromyography,root mean square,median frequency,average power frequency,wavelet coefficient and frequency were extracted during the experiment.After analyzing muscle force,eigenvalue and muscle fatigue degree,it is found that the three are highly correlated.The Sparrow search algorithm(SSA)was used to optimize the weights and thresholds of BP neural network,and the upper limb muscle strength prediction model was constructed and trained.The test results show that the error of this method is less than 12%,and it can predict the muscle strength accurately.

关 键 词：表面肌电信号 肌肉疲劳 SSA-BP回归预测模型 AnyBody 肌力预测 

分 类 号：TN911.7[电子电信—通信与信息系统] R741.044[电子电信—信息与通信工程]