多模式人手动作分类识别方法
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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.
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.
引文
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[2] ROSATI G,GALLINA P,MASIERO S.Design,Implementation and Clinical Tests of a Wire-based Robot for Neurorehabilitation[J].IEEE Transactions on Neural Systems and Rehabilitation Engineering,2007,15(4):560-569.
[3] KHUSHABA R N,KODAGODA S,TAKRURI M,et al.Toward Improved Control of Prosthetic Fingers Using Surface Electromyogram (EMG) Signals[J].Expert Systems with Applications,2012,39(12):10731-10738.
[4] 黄鹏程,杨庆华,鲍官军,等.基于FPA的气动柔性手指康复器及其控制系统[J].中国机械工程,2014,25(13):1724-1743.HUANG Pengcheng,YANG Qinghua,BAO Guanjun,et al.Flexible Pneumatic Finger Rehabilitation Apparatus Based on FPA and Its Control System[J].China Mechanical Engineering,2014,25(13):1724-1743.
[5] 张辉.六自由度外骨骼式上肢康复机器人设计[D].上海:东华大学,2014.ZHANG Hui.The Design of Exoskeleton Upper Limb Rehabilitation Robot with 6-Dof[D].Shanghai:Donghua University,2014.
[6] De LUCA C J,ADAM A,WOTIZ R,et al.Decomposition of Surface EMG Signals[J].Journal of Neurophysiology,2006,96(3):1646-1657.
[7] 丁其川,熊安斌,赵新刚,等.基于表面肌电的运动意图识别方法研究及应用综述[J].自动化学报,2016,42(1):13-25.DING Qichuan,XIONG Anbin,ZHAO Xingang,et al.A Review on Researches and Applications of sEMG-based Motion Intent Recognition Methods[J].Acta Automatica Sinica,2016,42(1):13-25.
[8] RYAIT H S,ARORA A S,AGARWAL R.Interpretations of Wrist/Grip Operations from sEMG Signals at Different Locations on Arm[J].IEEE Transactions on Biomedical Circuits and Systems,2010,4(2):101-111.
[9] 刘建,邹任玲,张东衡,等.表面肌电信号特征提取方法研究发展趋势[J].生物医学工程学进展,2015(3):164-168.LIU Jian,ZOU Renling,ZHANG Dongheng,et al.Research and Development Trend of Feature Extraction Methods of Surface Electromyographic Signals[J].Progress in Biomedical Engineering,2015(3):164-168.
[10] 张峰,王东,石现峰.振动信号Burg谱估计算法的性能优化研究[J].计算机仿真,2017,34(4):262-266.ZHANG Feng,WANG Dong,SHI Xianfeng.Study on Performance Optimization of Vibration Signal Burg Spectral Estimation Algorithm[J].Computer Simulation,2017,34(4):262-266.
[11] 黄鹏程,杨庆华,鲍官军,等.基于幅值立方和BP神经网络的表面肌电信号特征提取算法[J].中国机械工程,2012,23(11):1332-1336.HUANG Pengcheng,YANG Qinghua,BAO Guanjun,et al.Feature Extraction Method Based on Amplitude Cubic and BP Neural Network of Surface Electromyography (sEMG) Signals[J].China Mechanical Engineering,2012,23(11):1332-1336.