Effectiveness of Empirical Mode Decomposition Technique on Semg Signals in Fatigue Assessment during a Walk

Muscle fatigue is often caused by unhealthy and irregular work practice. It is defined as a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Faster walking can cause muscle fatigue, which is unhealthy when the level of fatigue is high. There are many mathematical parameters that are suitable to assess the muscle fatigue during gait. Out of these parameters, the amplitude and frequency of the surface EMG signal (sEMG) reflects the more accurate physiological activity in the motor unit during contraction and at rest. In this research, Empirical mode decomposition (EMD) based filtering process is applied on sEMG signal for realizing the fatiguing contraction during subject walking exercise. The purpose of this research is to evaluate the surface electromyographic parameters (RMS, IAV and AIF) for addressing the effectiveness of the EMD method. In this study, RMS, IAV and AIF values were used as spectral variable, which extensively categorizes the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). Furthermore, the outcome also demonstrates that, amplitude and frequency of EMG signal significantly changes from rest position to maximum contraction position. In this study, we will try to show that by using the EMD method an identification of reliable discrimination between fatigue and non-fatigue muscle is possible.