华东理工大学学报：自然科学版2011，Vol.37Issue(5)：644-649,6.
基于肌音信号的四种手部动作模式的识别方法
A Recognition Method for Four Hand-Motion Patterns Based on Mechanomyographic Signal
摘要
Abstract
Mechanomyography（MMG） refers to the ＂sound＂ of muscle contracting,with frequency band from 2 to 100 Hz.MMG signal as a physiological signal source has been gradually utilized and justified in the control of prosthetic hands recently.This paper developed a way of constructing a forearm hand-motion MMG feature space containing 18 time and frequency features,and principal component analysis（PCA） is adopted to reduce the feature dimensionality.Linear classifier algorithm is then applied to identify the four hand-motion patterns（hand close,hand open,wrist flexion and wrist extension）.Forearm hand-motion MMG signals are acquired from 32 volunteers.The analysis results show that the average accuracy rate is above 95%,the recognition with three-channel acquisition configuration has the best overall performance,and the placement distribution of acquisition points on four forearm muscles has few effects on the accuracy rate.关键词
肌音/手部动作/模式识别/主成分分析/线性分类器Key words
mechanomyography/hand-motion/pattern recognition/principal component analysis/linear classifier分类
临床医学引用本文复制引用
曹炜,夏春明,曾勇,曹恒..基于肌音信号的四种手部动作模式的识别方法[J].华东理工大学学报：自然科学版,2011,37(5):644-649,6.基金项目
国家自然科学基金资助项目 （）
