计算机科学 ›› 2021, Vol. 48 ›› Issue (6A): 317-320.doi: 10.11896/jsjkx.200600021
郭福民, 张华, 胡瑢华, 宋岩
GUO Fu-min, ZHANG Hua, HU Rong-hua, SONG Yan
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。
中图分类号:
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