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Electromyography (EMG) signal is interfered with different kinds of noise and wavelet denoising algorithm is a powerful method to reduce noises in EMG signal. Hard and soft shrinkage, traditional wavelet transformation, are applied to wavelet coefficients with threshold value. From the limitation of hard and soft shrinkage, this study proposes nine improved wavelet shrinkage methods that achieve a...
Wavelet denoising algorithms have been received considerable attention in the removal of noises of surface electromyography (sEMG) signal. Wavelet denoising algorithms proposed by Donoho's method is more often used in sEMG signal. However, Donoho's method is limited especially for multifunction myoelectric control. It does not only remove noises but it also removes some important part of sEMG signals...
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...
The aim of this study was to investigate the application of wavelet denoising in noise reduction for multifunction myoelectric control system. Six upper limb motions including hand open, hand close, wrist extension, wrist flexion, pronation, and supination. For each motion, two channels of electrodes were applied. A comparative study of four classical denoising algorithms including universal thresholding,...
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