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Received: 2006-11-08

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Journal of Zhejiang University SCIENCE A 2007 Vol.8 No.8 P.1246~1255

http://doi.org/10.1631/jzus.2007.A1246


Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification


Author(s):  YAN Zhi-guo, WANG Zhi-zhong, REN Xiao-mei

Affiliation(s):  Departmen of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China

Corresponding email(s):   hengdaoxiao@sjtu.org, zzwang@sjtu.edu.cn

Key Words:  Electromyografic signal, Empirical mode decomposition (EMD), Auto-regression model, Wavelet packet transform, Least squares support vector machines (LS-SVM), Neural network


YAN Zhi-guo, WANG Zhi-zhong, REN Xiao-mei. Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification[J]. Journal of Zhejiang University Science A, 2007, 8(8): 1246~1255.

@article{title="Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification",
author="YAN Zhi-guo, WANG Zhi-zhong, REN Xiao-mei",
journal="Journal of Zhejiang University Science A",
volume="8",
number="8",
pages="1246~1255",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1246"
}

%0 Journal Article
%T Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification
%A YAN Zhi-guo
%A WANG Zhi-zhong
%A REN Xiao-mei
%J Journal of Zhejiang University SCIENCE A
%V 8
%N 8
%P 1246~1255
%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1246

TY - JOUR
T1 - Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification
A1 - YAN Zhi-guo
A1 - WANG Zhi-zhong
A1 - REN Xiao-mei
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 8
SP - 1246
EP - 1255
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1246


Abstract: 
This paper presents an effective and efficient combination of feature extraction and multi-class classifier for motion classification by analyzing the surface electromyografic (sEMG) signals. In contrast to the existing methods, considering the non-stationary and nonlinear characteristics of EMG signals, to get the more separable feature set, we introduce the empirical mode decomposition (EMD) to decompose the original EMG signals into several intrinsic mode functions (IMFs) and then compute the coefficients of autoregressive models of each IMF to form the feature set. Based on the least squares support vector machines (LS-SVMs), the multi-class classifier is designed and constructed to classify various motions. The results of contrastive experiments showed that the accuracy of motion recognition is improved with the described classification scheme. Furthermore, compared with other classifiers using different features, the excellent performance indicated the potential of the SVM techniques embedding the EMD-AR kernel in motion classification.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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