EMG denoising estimation based on adaptive wavelet thresholding for multifunction myoelectric control

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. This study proposes an improved threshold estimation method. Six modified threshold estimation methods associated with the selected thresholding rescaling are evaluated. SEMG signal from six hand motions with additive WGN at various signal-to-noise ratios (SNRs) were applied to evaluate the efficient of method. Features of the estimated signal are sent to classification task. Evaluations of the performance of these algorithms are mean squared error (MSE) and classification rate. The results show that Global Scale Modified Universal (GSMU) method provides better performance than traditional Donoho's method. It produces sEMG signals that remain important information of the original sEMG signal and can eliminate lots of noises. The average MSE are 0.0024 at 20 dB SNR, low noise, and 0.074 at 0 dB, high noise. The accuracy of hand movement recognition of sEMG signal that estimates from GSMU is improved. It improves 1 to 4% of the classification accuracy depend on level of noise. In addition, performance of level dependent method is better than the others rescaling method. In the experiment, GSMU threshold estimation method is an efficient method for producing useful sEMG signal without noise and improving the application of hand movement recognition.