| 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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| 作者姓名： | YAN Zhi-guo WANG Zhi-zhong REN Xiao-mei |
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| 作者单位： | Departmen of Biomedical Engineering，Shanghai Jiao Tong University，Shanghai 200030，China |
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| 基金项目： | Project （No. 2005CB724303） supported by the National Basic Re-search Program （973） of China |
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| 摘 要： | 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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| 关 键 词： | 电肌信号 经验模式分解 自动衰退模型 小波变换 最小二乘支持特向量机 神经网络 |
| 修稿时间： | 2006-11-082007-03-23 |
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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| 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,2007,8(8):1246-1255. |
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| Authors: | Yan Zhi-guo Wang Zhi-zhong Ren Xiao-mei |
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| Institution: | (1) Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, China |
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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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| Keywords: | 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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