NDLI: An optimal wavelet function based on wavelet denoising for multifunction myoelectric control

Content Provider	IEEE Xplore Digital Library
Author	Phinyomark, A. Limsakul, C. Phukpattaranont, P.
Copyright Year	2009
Description	Author affiliation: Department of Electrical Engineering, Prince of Songkla University, Hat Yai, 90110 Thailand (Phinyomark, A.; Limsakul, C.; Phukpattaranont, P.)
Abstract	The aim of this study was to investigate and select the wavelet function that is optimum to denoise the surface electromyography (sEMG) signal for multifunction myoelectric control. Wavelet denoising algorithm has been used to find the optimal wavelet function for removing white Gaussian noise (WGN) at various signal-to-noise ratios (SNRs) from sEMG signals. A total of 53 wavelet functions were used in evaluation of the denoised performance. The wavelets are Daubechies, Symlets, Coiflet, BiorSplines, ReverseBior, and Discrete Meyer. Universal thresholding method has been used to estimate threshold value. Soft, hard, hyperbolic, and garrote thresholding are applied. Evaluations of the performance of these algorithms are mean squared error (MSE). The results show that the best wavelet functions for denoising are the first order of Daubechies, BioSplines, and ReverseBior wavelets (db1, bior1.1, rbio1.1). Various families can be used except the third order of decomposition of BiorSplines (bior3.1, bior3.3, bior3.5, bior3.7, bior3.9) and Discrete Meyer (dmey) are not recommended to use in wavelet denoising of sEMG signal. In addition, performance of soft thresholding is better than the others modified thresholding.
Starting Page	1098
Ending Page	1101
File Size	351902
Page Count	4
File Format	PDF
ISBN	9781424433872
DOI	10.1109/ECTICON.2009.5137236
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-05-06
Publisher Place	Thailand
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wrist Electrodes Gaussian noise Noise reduction Optimal control Signal processing algorithms Signal processing Electromyography Discrete wavelet transforms Signal analysis
Content Type	Text
Resource Type	Article

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