Fuzzy Wavelet Packet based Feature Extraction Method for Multifunction Myoelectric Control

Authors: Rami N. Khushaba, Adel Al-Jumaily

Abstract:

The myoelectric signal (MES) is one of the Biosignals utilized in helping humans to control equipments. Recent approaches in MES classification to control prosthetic devices employing pattern recognition techniques revealed two problems, first, the classification performance of the system starts degrading when the number of motion classes to be classified increases, second, in order to solve the first problem, additional complicated methods were utilized which increase the computational cost of a multifunction myoelectric control system. In an effort to solve these problems and to achieve a feasible design for real time implementation with high overall accuracy, this paper presents a new method for feature extraction in MES recognition systems. The method works by extracting features using Wavelet Packet Transform (WPT) applied on the MES from multiple channels, and then employs Fuzzy c-means (FCM) algorithm to generate a measure that judges on features suitability for classification. Finally, Principle Component Analysis (PCA) is utilized to reduce the size of the data before computing the classification accuracy with a multilayer perceptron neural network. The proposed system produces powerful classification results (99% accuracy) by using only a small portion of the original feature set.

Keywords: Biomedical Signal Processing, Data mining andInformation Extraction, Machine Learning, Rehabilitation.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1080410

References:

[1] K. Englehart, B. Hudgin, and P.A. Parker, "A wavelet-based continuous classification scheme for multifunction myoelectric control," IEEE Transactions on Biomedical Engineering, vol. 48, pp. 302 - 311, 2001.
[2] C. J. De Luca, "Physiology and mathematics of myoelectric signals," IEEE Transactions on Biomedical Engineering, vol. BME-26, pp. 313- 325, 1979.
[3] R. Merletti and P. Parker, "Electromyography physiology, Engineering, and noninvasive applications," IEEE Press Engineering in Medicine and Biology Society., 2004.
[4] J. Hunt, "A 3-degree-of-freedom myoelectric control suitable for easy implementation in hardware," in Electrical and Computer Engineering, vol. Master of Science. Fredericton, NB Canada: University of NewBrunswick, 1998.
[5] B. Hudgins, P. Parker, and R. N. Scott, "A new strategy for multifunction myoelectric control," IEEE Transactions on Biomedical Engineering, vol. 40, pp. 82-94, 1993.
[6] C. M. Lighty, P. H. Chappelly, B. Hudgins, and K. Englehart, "Intelligent multifunction myoelectric control of hand prostheses," Journal of Medical Engineering & Technology, vol. 26, pp. 139- 146, 2002.
[7] S. Leowinata, "A new strategy for multifunction myoelectric control using an array of surface electrodes," in Electrical and Computer Engineering, vol. Master: University of New Brunswick, 2000.
[8] M. Vuskovic and D. Sijiang, "Classification of prehensile EMG patterns with simplified fuzzy ARTMAP networks," Proceedings of the International Joint Conference on Neural Networks, IJCNN '02. , vol. 3, pp. 2539-2544, 2002.
[9] J. J. Im, D. H. Rho, Y. J. Jeon, N. B. Lee, and J. I. Chung, "Extraction of parameters from EMG signals for the biofeedback electrical stimulation," presented at Proceedings of the Second Joint EMBS/BMES Conference, 2002.
[10] S-P. Lee, S-H. Park, J-S. Kim, and I.-J. Kim, "EMG pattern recognition based on evidence accumulation for prosthesis control," 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, The Netherlands, pp. 1481-1483, 1996.
[11] Sang-Hui Park and S.-P. Lee, "EMG Pattern Recognition Based on Artificial Intelligence Techniques," IEEE Transactions on Rehabilitation Engineering, vol. 6, pp. 400-405, 1998.
[12] L. Seok-Pil, P. Sang-Hui, K. Jeong-Seop, and K. Ig-Jae, "EMG pattern recognition based on evidence accumulation for prosthesis control," 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, vol. 4, pp. 1481-1483 vol.4, 1996.
[13] Jun-Uk Chu, Inhyuk Moon, Shin-Ki Kim, and M.-S. Mun., "Control of multifunction myoelectric hand using a real-time EMG pattern recognition," IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2005). pp. 3511 - 3516, 2005.
[14] Y. H. Kim, I. J. Shim, and G. T. Park, "A method of controlling household electrical appliance by hand motion in LonWorks," Proceedings of the 41st SICE Annual Conference SICE. , vol. 5, pp. 2849-2854, 2002.
[15] B. Hannaford and S. Lehman, "Short time fourier analysis of the electromyogram: fast movements and constant contraction," IEEE Transactions on Biomedical Engineering, vol. BME-33, pp. 1173- 1181, 1986.
[16] S. Karlsson, Y. Jun, and M. Akay, "Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study," IEEE Transactions on Biomedical Engineering, vol. 47, pp. 228-238, 2000.
[17] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, "Control of multifunctional prosthetic hands by processing the electromyographic signal," Critical Review in Biomedical Engineering, vol. 30, pp. 459- 485, 2002.
[18] K. Englehart, B. Hudgins, P. A. Parker, and M. Stevenson, "Improving myoelectric signal classification using wavelet packets and principal components analysis," IEEE Engineering in Medicine and Biology Society, Atlanta, vol. 1, pp. 569, 1999.
[19] J.-U. Chu, I. Moon, and M.-S. Mun, "A real-time EMG pattern recognition based on linear-nonlinear feature projection for multifunction myoelectric hand," Proceedings of the IEEE 9th International Conference on Rehabilitation Robotics, pp. 295-298, 2005.
[20] J. U. Chu, I. Moon, and M. S. Mun, "A real-time EMG pattern recognition system based on linear-nonlinear feature projection for a multifunction myoelectric hand," IEEE Transactions on Biomedical Engineering, vol. 53, pp. 2232-2239, 2006.
[21] L. Hargrove, K. Englehart, and B. Hudgins, "A comparison of surface and intramuscluar myoelectric signal classification," IEEE Transactions on Biomedical Engineering, Accepted for future publication.
[22] R. R. Coifman, Y. Meyer, and M. V. Wickerhauser, "Wavelet analysis and signal processing," in Wavelets and Their Applications, M. B. Ruskai, Ed. Boston: Jones and Bartlett, 1992.
[23] K. Englehart and B. Hudgins, "A robust, real-time control scheme for multifunction myoelectric control," Biomedical Engineering, IEEE Transactions on, vol. 50, pp. 848-854, 2003.
[24] L. Deqiang, W. Pedrycz, and N. J. Pizzi, "Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification," IEEE Transactions on Biomedical Engineering, vol. 52, pp. 1132-1139, 2005.
[25] K. Englehart, "Signal representation for classification of the transient myoelectric signal " in Electrical and Computer Engineering Department., vol. PhD Dissertation: University of New Brunswick, 1998.
[26] I. Drummond and S. Sandri, "A clustering-based fuzzy classifier," in Artificial Intelligence Research and Development, B. Lopez, Ed.: IOS Press, 2005.
[27] Y. KO├çY─░─×─░T and M. KOR├£REK, "EMG signal classif─▒cation using wavelet transform and fuzzy clustering algorithms," presented at Proc. of ELECO'2003, Bursa, Turkey, 2003.
[28] B. Karlik, M. Osman Tokhi, and M. Alci, "A fuzzy clustering neural network architecture for multifunction upper-limb prosthesis," IEEE Transactions on Biomedical Engineering, vol. 50, pp. 1255-1261, 2003.
[29] A. D. Boca and D. C. Park, "Myoelectric signal recognition using fuzzy clustering and artificial neural networks in real time," IEEE International Conference on Neural Networks, vol. 5, pp. 3098-3103 1994.
[30] M. M. Trivedi and J. C. Bezdeck, "Low-level segmentation of aerial images with fuzzy clustering," IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-16, pp. 589- 598, 1986.
[31] R. Boostani and M. H. Moradi, "Evaluation of the forearm EMG signal features for the control of a prosthetic hand," Physiological Measurement, vol. 24, pp. 309-319, 2003.