多模式人手动作分类识别方法  

Classification and Identification of Multi-pattern of Hand Actions

作　　者：都明宇[1] 王志恒[1] 荀一[1] 鲍官军[1] 高峰[1] 杨庆华[1] 张立彬[1] DU Mingyu;WANG Zhiheng;XUN Yi;BAO Guanjun;GAO Feng;YANG Qinghua;ZHANG Libin(College of Mechanical Engineering,Zhejiang University of Technology,Hangzhou,310014)

机构地区：[1]浙江工业大学机械工程学院

出　　处：《中国机械工程》2019年第12期1474-1479,共6页China Mechanical Engineering

基　　金：国家自然科学基金资助项目(51775499);浙江省自然科学基金资助项目(LQ15E050008);浙江省教育厅科研项目(Y201121563);北京市智能机器人与系统高精尖创新中心开放基金资助项目(2016IRS03)

摘　　要：为了满足手部运动功能康复器的主动康复训练对多种人手动作模式识别的需求,分析了表面肌电信号采样通道设置布局、训练样本制作、特征提取方式、模式分类器结构参数等因素对手部动作识别的影响,设计了针对前臂的表面肌电信号采集方案,分别基于时域统计量、自回归模型系数、小波包分解系数特征设计了BP神经网络分类器。实验结果表明:对6种单指动作、13种多指动作、20种手部动作的最佳平均识别率分别为98.5%、92.4%、90.9%,计算时间小于190 ms,验证了所提出方法的有效性和实用性。In order to meet the needs of many human hand action pattern recognitions in complex applications such as exercise rehabilitation training and human-machine interaction,influences of sEMG signals sampling channel layouts,training sample preparations,feature extraction modes,classifier structure parameters,and so on were analysed herein. Optimal signal sampling scheme was established,and 3 BP neural network classifiers were designed based on time domain statistic,self-regression model coefficient,and wavelet packet decomposition coefficient. Experimental results show that average recognition rate of classifiers for 6 kinds of single finger actions,13 kinds of multi finger actions,20 kinds of hand movements are 98.5%,92.4%,and 90.9% respectively. Output delay is less than 190 ms,which verifies effectiveness and practicability of the proposed method.

关 键 词：表面肌电 手部动作 神经网络 模式识别 

分 类 号：TP241.3[自动化与计算机技术—检测技术与自动化装置]