文献类型：期刊文献

中文题名：基于肌音信号的握力运动时桡侧腕屈肌动态疲劳分析

英文题名：Dynamic Fatigue Analysis of Radial Wrist Flexion Based on MMG Signal during Griping Action

作者：钟豪[1];吴清[1];夏春明[1];章悦[1];顾晓琳[1];张胜利[1]

机构：[1]华东理工大学机械与动力工程学院,上海200237

年份：2018

卷号：44

期号：5

起止页码：783

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2017_2018】；

语种：中文

中文关键词：肌音信号;肌肉动态疲劳;小波包分解;平均功率频率;指数拟合

外文关键词：MMG signal;dynamic fatigue of muscles;wavelet packet decomposition;mean power frequency;exponential fitting

摘要：采集了10名受试者在做手部握力动作时桡侧腕屈肌的肌音信号,通过对信号进行滤波、动作分割和特征提取来分析肌肉动态疲劳程度与肌音信号特征值的关系。在信号滤波中,采用了小波包(WP)分解重构和经验模态分解(EMD)两种方法。在动作信号的分割中,提出了基于移动窗内信号方差阈值的自适应不等长分割算法。在特征提取时,提出了利用包含多个动作信号的移动窗对分割好的信号进行再重构,并选用平均功率频率(MPF)和中值频率(MDF)作为窗内信号提取的特征,再分别利用指数函数、二次函数和线性函数对特征值进行拟合。结果表明:去噪方法选用小波包分解重构、特征值选用MPF值、拟合方式选用指数函数进行逼近的分析方法,可以更好地反映肌肉疲劳的变化趋势。
The radial wrist flexor MMG signals of 10 subjects were acquired when they completed each griping and holding action.The relationship between the degree of dynamic fatigue of muscles and the characteristics of the MMG signals was analyzed by filtering,motion segmentation and feature extraction of MMG signals.For signal filtering,two methods,the wavelet packet decomposition(WP)and empirical mode decomposition(EMD)were adopted.In WP method,the original signal was decomposed into 7 layers with db4 wavelet base,and then 2-100 Hz frequency band signals were superimposed to reconstruct a filtered signal.In EMD method,the original signal was decomposed into intrinsic mode functions(IMFs),then,IMFs in layers 3-6 were superimposed to reconstruct a filtered signal.For motion segmentation,an adaptive unequal segmentation algorithm was proposed based on the variance threshold of the moving windowed signal.The starting point and ending point of each action were determined by calculating the variance value in each moving window,and thus individual action signal was segmented.For feature extraction,firstly,a moving window containing multiple motion signals was used to reconstruct the segmented signal;then mean power frequency(MPF)and median frequency(MDF)were selected as the characteristics of the windowed signal;finally,the characteristics were fitted by the exponential,the quadratic and the linear function,respectively.The results show that when selecting WP for de-noising,MPF as characteristics and exponential function as fitting method,it can reflect the trend of muscle fatigue better than that of the other combinations.