Optimal Wavelet Functions in Wavelet Denoising for Multifunction Myoelectric Control
Article Sidebar
Main Article Content
Abstract
Wavelet analysis is one of the most important methods for analyzing the surface Electromyography (sEMG) signal. The aim of this study was to investigate the wavelet function that is optimum to identify and denoise the sEMG signal for multifunction myoelectric control. This study is motivated by the fact that there is no universal mother wavelet that is suitable for all types of signal. The right wavelet function becomes to achieve the optimal performance. In this study, the optimal wavelets are evaluated in term of mean square error of two criterions, namely denoising and reconstruction. Fifty-three wavelet functions are used to perform an iterative denoising and reconstruction on different noise levels that are added in sEMG signals. In addition, various possible decomposition levels and types of wavelets in the denoising procedure are tested. The results show that the best mother wavelets for tolerance of noise in denoising are the first order of Daubechies, BioSplines, and ReverseBior but the classification results are not recommended. The fifth order of Coiflet is the best wavelet in perfect reconstruction point of view. Various families can be used except the third order of BiorSplines and Discrete Meyer are not recommended to use. Suitable number of decomposition levels is four and optimal wavelets are independent of wavelet denoising algorithms.
Article Details
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
[2] I. Moon, "Wearable human interfaces for myoelectric hand prosthesis and intelligent wheelchair," in22nd Japanese Conference on the Advancement of Assistive and Rehabilitation Technology, 2007, pp. 2 C 35.
[3] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario, "Control of multifunctional prosthetic hands by processing the electromyographic signal," Critical ReviewsTM in Biomedical Engineering, vol. 30, no. 4-6, pp. 459485, 2002.
[4] 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. 309319, 2003.
[5] S. Karlsson, J. Yu, and M. Akay, "Timefrequency analysis of myoelectric signals during dynamic contractions: a comparative study," IEEE Transactions on Biomedical Engineering, vol. 47, no. 2, pp. 228238, 2000.
[6] K. Englehart, B. Hudgins, and P.A. Parker, "A wavelet-based continuous classification scheme for multifunction myoelectric control," IEEE Transactions on Biomedical Engineering, vol. 48, no. 3, pp. 302310, 2001.
[7] D. K. Kumar, N. D. Pah, and A. Bradley, "Wavelet analysis of surface electromyography to determine muscle fatigue," IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 11, no. 4, pp. 400406, 2003.
[8] K. Englehart, Representation and Classification of the Transient Myoelectric Signal, Ph.D. thesis, University of New Brunswick, 1998.
[9] M. B. I. Reaz, M. S. Hussain, and F. Mohd Yasin, "Techniques of emg signal analysis: detection, processing, classi¯cation and applications," Biological Procedures Online, vol. 8, no. 1, pp. 1135, 2006.
[10] M. C. E. Rosas-Orea, M. Hernandez-Diaz, V. Alarcon-Aquino, and L. G. Guerrero-Ojeda, "A comparative simulation study of wavelet based denoising algorithms," in 15th IEEE International Conference on Electronics, Communications and Computers, 2005, pp. 125130.
[11] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, "A comparative study of wavelet denoising for multifunction myoelectric control," in International Conference on Computer and Automation Engineering, 2009, pp. 2125.
[12] C. F. Jiang and S. L. Kuo, "A comparative study of wavelet denoising of surface electromyographic signals," in 29th Annual International Conference of the IEEE Engineering in Medicine and
Biology Society, 2007, pp. 18681871.
[13] P. Wellig and G. S. Moschytz, "Analysis of wavelet features for myoelectric signal classification," in International Conference on Electronics, Circuits and Systems, 1998, vol. 3, pp. 109112.
[14] X. Guo, P. Yang, Y. Li, and W-L. Yan, "The semg analysis for the lower limb prosthesis using wavelet transformation," in 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2004, vol. 1, pp. 341344.
[15] X. Guo, P. Yang, Y. Li, and W-L. Yan, "Study and analysis of surface emg for the lower limb prosthesis," in International Conference on Machine Learning and Cybernetics, 2004, vol. 6, pp.
37363740.
[16] Z. Qingju and L. Zhizeng, "Wavelet denoising of electromyography," in IEEE International Conference on Mechatronics and Automation, 2006, pp. 15531558.
[17] Y. Guangying and L. Zhizeng, "Surface electromyography disposal based on the method of wavelet de-noising and power spectrum," in International Conference on Intelligent Mechatronics and Automation, 2004, pp. 896900.
[18] M. Amirmazlaghani and H. Amindavar, "Emg signal denoising via bayesian wavelet shrinkage based on garch modeling," in IEEE International Conference on Acoustics, Speech, and Signal Processing, 2009, pp. 469472.
[19] I. Kremenic, "Removal of magnetic stimulation artifact from emg using a wavelet shrinkagebased method," in Annual American Society of Biomechanics Meeting, 2003.
[20] D. Moshou, I. Hostens, G. Papaioannou, and H. Ramon, "Wavelets and self-organising maps in electromyogram (emg) analysis," in European Symposium on Intelligent Techniques, 2000, pp. 186191.
[21] R. Prinz, P. M. Zeman, S. Neville, and N. J. Livingston, "Feature extraction through wavelet denoising of surface emg signals for the purpose of mouse click emulation," in Canadian Conference
on Electrical and Computer Engineering, 2006, pp. 14541457.
[22] H. G. R. Tan, A. C. Tan, P. Y. Khong, and V. H. Mok, "Best wavelet function identification system for ecg signal denoise applications," in International Conference on Intelligent and Advanced
Systems, 2007, pp. 631634.
[23] M. Kania, M. Fereniec, and R. Maniewski, "Wavelet denoising for multi-lead high resolution ecg signals," Measurement Science Review, vol. 7, no. 4, pp. 3033, 2007.
[24] S. Neville and N. Dimopoulos, "Wavelet denoising of coarsely quantized signals," IEEE Transactions on Instrumentation and Measurement, vol. 55, no. 3, pp. 892901, 2006.
[25] D. Donoho and I.M. Johnstone, "Ideal spatial adaptation by wavelet shrinkage," Tech. Rep., Department of Statistics, Stanford University, 1993.
[26] C. Stein, "Estimation of the mean of a multi-variate normal distribution," Annals of Statistics, vol. 9, no. 6, pp. 11351151, 1981.
[27] D. Donoho and I. Johnstone, "Adapting to unknown smoothness via wavelet shrinkage," Tech. Rep., Department of Statistics, Stanford University, 1992.
[28] B. Vidakovic, Statistical Modeling by Wavelets, John Wiley Sons, Inc., Toronto, ON, Canada, 1999.
[29] H-Y. Gao, "Wavelet shrinkage denoising using the non-negative garrote," Journal of Computational and Graphical Statistics, vol. 7, no. 4, pp. 469488, 1998.
[30] A. Phinyomark, C. Limsakul, and P. Phukpattaranont, "Evaluation of wavelet function based on robust emg feature extraction," in 7th PSU Engineering Conference, 2009, pp. 277281.
[31] M. S. Hussain, M. B. I. Reaz, F. Mohd-Yasin, and M. I. Ibrahimy, "Electromyography signal analysis using wavelet transform and higher order statistics to determine muscle contraction," Expert Systems, vol. 26, no. 1, pp. 3548, 2009.