JZUS - Journal of Zhejiang University SCIENCE

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

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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.

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