Determination of muscle fatigue in SEMG signal using empirical mode decomposition

Muscle fatigue is defined as the long lasting deterioration of a performance of the human operator to create force. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue is a well-known research area. In order to completely comprehend the idea many research have been done on different type of muscle fatigue. There are many spectral variables that can be used to determine muscle fatigue during gait. Out of these variables, the amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern. In this research, Empirical mode decomposition (EMD) and wavelet Transform applied to the surface EMG (SEMG) signal for realizing the fatiguing contraction during human walking exercise. In this study, RMS, IAV and AIF values were used as spectral variable, which extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). Furthermore, the outcome also proves that, the SEMG amplitude and frequency momentously changes from rest position to maximum contraction position. This research reports on the effectiveness of EMD-based filtering method applied to the surface EMG (SEMG) signal as a means of achieving reliable discrimination of the muscle fatigue.