Full Text:   <3603>

CLC number: TN911.7; R318.04

On-line Access: 

Received: 2006-11-08

Revision Accepted: 2007-03-23

Crosschecked: 0000-00-00

Cited: 6

Clicked: 6778

Citations:  Bibtex RefMan EndNote GB/T7714

-   Go to

Article info.
1. Reference List
Open peer comments

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


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",
publisher="Zhejiang University Press & Springer",

%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

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

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


[1] Ajiboye, A.B., Weir, R.F., 2005. A heuristic fuzzy logic approach to EMG pattern recognition for multifunctional prosthesis control. IEEE Trans. on Neural Syst. Rehabil. Eng., 13(3):280-291.

[2] Christodoulou, C.I., Pattichis, C.S., 1999. Unsupervised pattern recognition for the classification of EMG signals. IEEE Trans. on Biomed. Eng., 46:169-178.

[3] Englehart, K., Hudgins, B., Parker, P.A., Stevenson, M., 1999. Classification of the myoelectric signal using time-frequency based representations. Med. Eng. Phys., 21(6-7):431-438.

[4] Englehart, K., Hudgin, B., Parker, P.A., 2001. A wavelet-based continuous classification scheme for multifunction myoelectric control. IEEE Trans. on Biomed. Eng., 48(3):302-311.

[5] Englehart, K., Hudgins, B., 2003. A robust, real-time control scheme for multifunction myoelectric control. IEEE Trans. on Biomed. Eng., 50(7):848-854.

[6] Grzymala-Busse, J.W., 2003. A comparison of three strategies to rule induction from data with numerical attributes. Electr. Notes in Theor. Computer Sci., 82(4):1-9.

[7] Han, J.S., Bien, Z., Bang, W.C., 2000. New EMG Pattern Recognition Based on Soft Computing. Techniques and its Application to Control of a Rehabilitation Robotic Arm. Proc. 6th IIZUKA 2000. Lizuka, Japan.

[8] Hu, X., Nenov, V., 2004. Multivariate AR modeling of electromyography for the classification of upper arm movements. Clin. Neurophysiol., 115(6):1276-1287.

[9] Huang, W., Shen, Z., Huang, N.E., Fung, Y.C., 1998a. Engineering analysis of biological variables: an example of blood pressure over 1 day. PNAS, 95(9):4816-4821.

[10] Huang, N.E., Shen, Z., Long, S.R., Wu, M.J.C., Shih, H.H., Zheng, Q.N., Yen, N.C., Tung, C.C., Liu, H.H., 1998b. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Royal Soc. London A, 454:903-995.

[11] Huang, N.E., Shen, Z., Long, S.R., 1999. A new view of nonlinear water waves: the Hilbert spectrum. Ann. Rev. Fluid Mech., 31(1):417-457.

[12] Hudgins, B., Parker, P., Scott, R.N., 1993. A new strategy for multifunction myoelectric control. IEEE Trans. on Biomed. Eng., 40(1):82-94.

[13] Hyvärinen, A., 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. on Neural Networks, 10(3):626-634.

[14] Kang, W.J., Shiu, J.R., Cheng, C.K., Lai, J.S., Tsao, H.W., Kuo, T.S., 1995. The application of cepstral coefficients and maximum likelihood method in EMG pattern recognition. IEEE Trans. on Biomed. Eng., 42(8):777-785.

[15] Karlik, B., 1999. Differentiating type of muscle movement via AR modeling and neural network classification. Turk. J. Electr. Eng. & Computer Sci., 7(1-3):45-52.

[16] Kim, J.Y., Jung, M.C., Haight, J.M., 2005. The sensitivity of autoregressive model coefficient in quantification of trunk muscle fatigue during a sustained isometric contraction. Int. J. Ind. Ergon., 35(4):321-330.

[17] Kwon, J., Lee, S., Shin, C., Jang, Y., Hong, S., 1998. Signal Hybrid HMM-GA-MLP Classifier for Continuous EMG Classification Purpose. Proc. 20th Annual Int. Conf. of the IEEE, 3:1404-1407.

[18] Micera, S., Sabatini, A.M., Dario, P., 1999. A hybrid approach to EMG pattern analysis for classification of arm movements using statistical and fuzzy techniques. Med. Eng. Phys., 21(5):303-311.

[19] Neto, E.P.S., Custand, M.A., Cejka, J.C., Abry, P., Frutoso, J., Gharib, C., Flandrin, P., 2002. Assessment of Cardiovascular Autonomic Control by the Empirical Mode Decomposition. 4th International Workshop on Biosignal Interpreatation, 43:123-126.

[20] Nishikawa, D., Yu, W., Yokoi, H., 1999. EMG Prosthetic Hand Controller Discriminating Ten Motions Using Real-Time Learning Method. Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems.

[21] Qin, S.R., Zhong, Y.M., 2006. A new envelope algorithm of Hilbert-Huang transform. Mech. Syst. Signal Processing, 20(8):1941-1952.

[22] Ravier, P., Buttelli, O., Jennane, R., Couratier, P., 2005. An EMG fractal indicator having different sensitivities to changes in force and muscle fatigue during voluntary static muscle contractions. J. Electromyogr. Kinesiol., 15(2):210-221.

[23] Reddy, N.P., Gupta, V., 2007. Toward direct biocontrol using surface EMG signals: control of finger and wrist joint models. Med. Eng. Phys., 29(3):398-403.

[24] Schwenker, F., 2000. Hierarchical Support Vector Machines for Multi-class Pattern Recognition. 4th Int. Conf. on Knowledge-Based Intelligent Engineering Systems & Allied Technologies. Brighton, UK, p.561-565.

[25] Sebelius, F., Eriksson, L., Holmberg, H., Levinsson, A., 2005. Classification of motor commands using a modified self-organising feature map. Med. Eng. Phys., 27(5):403-413.

[26] Soares, A., Andrade, A., Lamounier, E., Carrijo, R., 2003. The development of a virtual myoelectric prosthesis controlled by an EMG pattern recognition system based on neural networks. J. Intell. Inf. Syst., 21(2):127-141.

[27] Su, Y., Fisher, M.H., Wolczowski, A., Bell, G.D., 2007. Towards an EMG-controlled prosthetic hand using a 3-D electromagnetic positioning system. IEEE Trans. on Instrum. Meas., 56(1):178-186.

[28] Subasi, A., Alkan, A., Koklukaya, E., Kiymik, M.K., 2005. Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing. Neural Networks, 18(7):985-997.

[29] Suykens, J.A.K., Vandewalle, J., 1999a. Least squares support vector machine classifiers. Neural Processing Lett., 9(3):293-300.

[30] Suykens, J.A.K., Vandewalle, J., 1999b. Multiclass Least Squares Support Vector Machines. Proc. Int. Joint Conf. on Neural Networks, p.900-903.

[31] Suykens, J.A.K., Gestel, T.V., Brabanter, J.D., Moor, B.D., Moor, B.D., Vandewalle, J., 2002. Least Support Vector Machines. World Scientific, Singapore. Http://www.worldscibooks.com/compsci/5089.html

[32] Theodoridis, S., Koutroumbas., K., 2003. Pattern Recognition (2nd Ed.). Elsevier Science, p.77-82.

[33] Valyon, J., Horváth, G., 2003. A weighted generalized LS-SVM. Period. Polytechn. Electr. Eng., 47(3-4):229-254.

[34] Vapnik, V., 1998. The Support Vector Method for Function Estimation. Int. Workshop on Advanced Black-box Techniques for Nonlinear Modeling: Theory and Applications with Time-Series Prediction Competition, p.55-85.

[35] Wang, G., Wang, Z.Z., Chen, W.T., Zhuang, J., 2006. Classification of surface EMG signals using optimal wavelet packet method based on Davies-Bouldin criterion. Med. Biol. Eng. Comput., 44(10):865-872.

[36] Wang, X.Y., Yang, J., Jensen, R., Liu, X.J., 2006. Rough set feature selection and rule induction for prediction of malignancy degree in brain glioma. Computer Methods and Programs in Biomed., 83(2):147-156.

[37] Zecca, M., Micera, S., Carrozza, M.C., 2002. Control of multifunctional prosthetic hands by processing the electro-myographic signal. Crit. Rev. Biomed. Eng., 30:459-485.

[38] Zhong, Y.M., Qin, S.R., Tang, B.P., 2004. Research on theoretic evidence and realization of directly-mean EMD method. Chin. J. Mech. Eng., 17(3):399-404.

Open peer comments: Debate/Discuss/Question/Opinion


Please provide your name, email address and a comment

Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - 2024 Journal of Zhejiang University-SCIENCE