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

Journal of Zhejiang University: Science A

Volumn 8, Issue 8, 2007, Pages 1246-1255

  Yan, Zhi Guo ^a   Wang, Zhi Zhong ^a   Ren, Xiao Mei ^a  

^a SHANGHAI JIAO TONG UNIVERSITY   (China)

Author keywords

Auto regression model; Electromyografic signal; Empirical mode decomposition (EMD); Least squares support vector machines (LS SVM); Neural network; Wavelet packet transform

Indexed keywords

ELECTROMYOGRAPHY; IMAGE ANALYSIS; LEAST SQUARES APPROXIMATIONS; MEDICAL IMAGING; NEURAL NETWORKS; SUPPORT VECTOR MACHINES; WAVELET TRANSFORMS;

AUTO-REGRESSION MODEL; ELECTROMYOGRAFIC SIGNALS; EMPIRICAL MODE DECOMPOSITION (EMD); WAVELET PACKET TRANSFORM;

FEATURE EXTRACTION;

EID: 34547178745     PISSN: 1673565X     EISSN: 18621775     Source Type: Journal    
DOI: 10.1631/jzus.2007.A1246     Document Type: Article

References (38)

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. [doi:10.1109/TNSRE.2005.847357]
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. [doi:10.1109/10.740879]
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. [doi:10.1016/S1350-4533(99) 00066-1]
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. [doi:10.1109/10.914793]
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. [doi:10.1109/TBME.2003.813539]
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. [doi:10.1016/j.clinph.2003.12.030]
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. [doi:10.1073/pnas.95.9.4816]
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. [doi:10.1146/annurev.fluid.31.1.417]
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. [doi:10.1109/10.204774]
13. Hyvärinen, A., 1999. Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans. on Neural Networks, 10(3):626-634. [doi:10.1109/72.761722]
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. [doi:10.1109/10.398638]
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. [doi:10.1016/j.ergon.2004.08.011]
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. [doi:10.1016/S1350-4533(99)00055-7]
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 Interpretation, 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):1941-1954. [doi:10.1016/j.ymssp. 2005.07.002]
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. [doi:10.1016/j.jelekin.2004.08.008]
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. [doi:10.1016/j.medengphy.2005.10.016]
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. [doi:10.1016/j.medengphy.2004.09.008]
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. [doi:10.1023/A:1024758415877]
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. [doi:10.1109/TIM.2006.887669]
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. [doi:10.1016/j.neunet.2005.01.006]
29. Suykens, J.A.K., Vandewalle, J., 1999a. Least squares support vector machine classifiers. Neural Processing Lett., 9(3):293-300. [doi:10.1023/A: 1018628609742]
30. Suykens, J.A.K., Vandewalle, J., 1999b. Multiclass Least Squares Support Vector Machines. Proc. Int. Joint Conf. on Neural Networks, p.900-903. [doi:10.1109/IJCNN.1999.831072]
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. [doi:10.1007/s11517-006- 0100-y]
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. [doi:10.1016/j.cmpb.2006.06.007]
37. Zecca, M., Micera, S., Carrozza, M.C., 2002. Control of multifunctional prosthetic hands by processing the electromyographic signal. Crit. Rev. Biomed. Eng., 30:459-485. [doi:10.1615/CritRevBiomedEng.v30.i456.80]
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.