Lower limb semg denoising using daubechies wavelets

This paper represents a different way of denoising lower limb Surface electromyography sEMG signals using Daubechies wavelets Much noise will be needed to remove as we can from this signal for it to function properly. The previous works couldn’t accurately determine the most suitable method to be used for lower limbs. This paper uses different thresholding approaches to calculate the highest value of SNR to identify the best denoising method. And a complete detailed survey of denoising techniques for reducing noise from surface electromyography signals is provided. This research has important implications for the practical application of lower limb EMG. This paper aimed to ascertain what are the most optimal parameters to be applied while using wavelet transform (Daubechies wavelets) to achieve the highest possible SNR in sEMG of the lower limb. The sample that was used came from 11 healthy subjects doing 3 different movements, using 4 electrodes to extract the signal. To identify the best denoising is calculated using different thresholding types, Daubechies level, and noise structure. The result from this experiment indicates that the hard-rigorous SURE threshold and scaled white noise provide the highest SNR in every signal tested but the Daubechies level differs from one signal to another.