Investigate the transcendent adapted of wavelet threshold algorithms for elbow movement by surface EMG signal

Prosthesis hand is an artificial device which replaces missing hand because of human´s hand may lost by trauma, disease or defect. From last decade, some researchers have been studying to invent and develop artificial hand. One of the most important step of bionic upper limb is developing surface electromyography (sEMG) signal analyzing methods. This pilot study is aimed to examine de-noising efficiency of four different wavelet threshold techniques such as Fixed Form Threshold (FFT), Heuristic Stein´s Unbiased Risk Estimate (HSURE), Minimax and Penalize Medium (PM) with hard and soft threshold rest on two different Stationary Wavelet Transform (SWT) methods i.e. Haar and Discrete Meyer. This research investigates the proposed wavelet method on bicep and triceps muscles during elbow extension and flexion. Both Haar and Discrete Meyer (dMey) methods are applied to the sEMG signal for de-noising the raw signals. Efficiency of the methods is examined by Mean Square Error (MSE). The results show that the minimum MSE is presented by PM with hard threshold by using MSE. However, PM with hard threshold has also minimum MSE value by using Haar wavelet method.