Feature extraction method of sEMG of human legs

doi:10.11835/j.issn.1000-582X.2017.11.010

Home > Archive>Volume 40, Issue 11, 2017 >83-90. DOI:10.11835/j.issn.1000-582X.2017.11.010

Feature extraction method of sEMG of human legs
DOI:
                        10.11835/j.issn.1000-582X.2017.11.010
                    
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                        WANG KunpengWANG Kunpeng
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, P. R. China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, Sichuan, P. R. China
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PANG JiePANG Jie
School of Information Engineering, Southwest University of Science and Technology, Mianyang 621010, Sichuan, P. R. China;Robot Technology Used for Special Environment Key Laboratory of Sichuan Province, Mianyang 621010, Sichuan, P. R. China
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SHI LeiSHI Lei
Product Development Department Outsourcing Technology of ING, Tokyo 257-0028, Japan
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QU JianfengQU Jianfeng
College of Automation, Chongqing University, Chongqing 400044, P. R. China
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Clc Number:TN911.6
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Abstract:

Surface electromyography(sEMG) is one of the main information sources of human motion detection and has been widely used in the field of well-being of robots. We present a feature extraction method based on wavelet transform power for identifying the movement of human legs. The average power of the active segment in wavelet subspace is used to make up the feature vector according to the frequency domain distribution of the sEMG signal. In order to verify the effectiveness of the proposed method, we design and implement a small portable multi-channel sEMG signal acquisition system, and construct a classifier with support vector machine (SVM) to identify the leg movements. The results of the study show that the method can distinguish four kinds of common actions of the leg, the recognition rate of the same individual can reach more than 95%, and the recognition rate of different individuals can reach 85%, which can be applied to daily rehabilitation training of patients with lower limb movement disorders.

Key words:sEMG signal;wavelet transform;average power;SVM;rehabilitation training

Reference

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王坤朋,庞杰,石磊,屈剑锋.人体腿部表面肌电信号特征提取方法[J].重庆大学学报,2017,40(11):83~90

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Received:July 11,2017
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Online: November 14,2017
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