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

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Abstract

This paper presents an effective and efficient combination of feature extraction and multi-class classifier formotion 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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Authors and Affiliations

  1. Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China

    Yan Zhi-guo, Wang Zhi-zhong & Ren Xiao-mei

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  1. Yan Zhi-guo
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  2. Wang Zhi-zhong
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  3. Ren Xiao-mei
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Correspondence to Wang Zhi-zhong.

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Project (No. 2005CB724303) supported by the National Basic Research Program (973) of China

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Yan, Zg., Wang, Zz. & Ren, Xm. Joint application of feature extraction based on EMD-AR strategy and multi-class classifier based on LS-SVM in EMG motion classification. J. Zhejiang Univ. - Sci. A 8, 1246–1255 (2007). https://doi.org/10.1631/jzus.2007.A1246

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  • DOI: https://doi.org/10.1631/jzus.2007.A1246

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