【摘要】 基于表面肌电信号(Surface ElectroMyoGraphy, sEMG)的人机交互力控制需要检测肌力的大小,而直接、精确地测量肌力十分困难,因此常使用肌力估计的方法估计肌力,为了实现基于sEMG信号的腕部肌力估计,文中提出了一种方法。该方法首先制作一个肌力采集平台,然后采集腕部一系列不同肌力水平的肌力信号和sEMG信号,将两种信号滤波后同步匹配,取sEMG信号的均方根、平均绝对值(MAV)、均值频率、谱矩比(Spectral Moments Ratio, SMR)作为4个特征值,最后使用支持向量机(Support Vector Machine, SVM)建模实现肌力估计,并与BP神经网络建模结果比较。两名实验者肌力估计均方根误差分别达到9.1%MVC(最大等长收缩力)和8.7%MVC,结果表明所提方法是一种有效的、简便的腕部肌力估计方法。更多还原

【Abstract】 Human-machine interaction force control based on surface electromyography(sEMG) needs to detect the force of muscle, and it is very difficult to measure muscle force directly and accurately.Therefore, muscle force estimation method is often used to estimate muscle force.A method for estimating wrist muscle force with sEMG signals is proposed.This method first makes a muscle force acquisition platform, then collects a series of muscle force signals and sEMG signals of wristat different muscle force levels, filters and matches the two signals synchronously, and takes the root mean square, mean absolute value(MAV).The mean frequency and spectral moments ratio(SMR) of the sEMG signal are taken as the fourfeatures.Finally, Support vector machine(SVM) modeling is used to achieve muscle force estimation and compared with the BP neural network modeling results.The root mean square error of the muscle force estimation of two experimenters reaches 9.1% MVC(maximum isometric contraction force) and 8.7% MVC,respectively.The results show that the method in this paper is an effective and simple method for estimating wrist muscle force.更多还原