Electromyography (EMG) signals are becoming increasingly important in many applications, including clinical/biomedical, prosthesis or rehabilitation devices, human machine interactions, and more. However, noisy EMG signals are the major hurdles to be overcome in order to achieve improved performance in the above applications. Detection, processing and classification analysis in electromyography (EMG) is very desirable because it allows a more standardized and precise evaluation of the neurophysiological, rehabitational and assistive technological findings. This paper reviews two prominent areas; first: the pre-processing method for eliminating possible artifacts via appropriate preparation at the time of recording EMG signals, and second: a brief explanation of the different methods for processing and classifying EMG signals. This study then compares the numerous methods of analyzing EMG signals, in terms of their performance. The crux of this paper is to review the most recent developments and research studies related to the issues mentioned above.

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Electromyography (EMG) is to measure the muscle response to nervous stimulation. The power spectrum of the EMG shifts towards lower frequencies during a continued muscle contraction due to muscular fatigue. Muscle fatigue is the decline in ability of a muscle to create force. This research presents the effectiveness of the wavelet transform applied to the surface EMG (SEMG) signal as a means of understanding muscle fatigue during walk. Power spectrum and bispectrum analysis on the EMG signal getting from right rectus femoris muscle is executed utilizing various wavelet functions (WFs). It is possible to recognize muscle fatigue appreciably with the proper choice of the WF. The outcome proves that, the most momentous changes in the EMG power spectrum symbolized by WF Daubechies4. Moreover, bispectrum properties compared to the other WFs. To determine muscle fatigue during gait, Daubechies45 is used in this research to analyze SEMG signal.

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Muscle fatigue is a long lasting reduction of the ability to contract and it is the condition when produced force is reduced. Walking fast can cause muscle fatigue, which is unhealthy and it is incurable when the level of fatigue is high. Muscle fatigue during walk can be determined using several spectral variables. The amplitude and frequency of the surface EMG signal provide a more accurate reflection of motor unit pattern among these spectral variables. This research reports on the effectiveness of Empirical mode decomposition (EMD) and wavelet transform based filtering method applied to the surface EMG (sEMG) signal as a means of achieving reliable discrimination of the muscle fatigue during human walking exercise. In this research, IAV, RMS and AIF values were used as spectral variable. These spectral variables extensively identifies the difference between fatigue and normal muscle when using EMD method compared with other different wavelet functions (WFs). The result shows that the sEMG amplitude and frequency momentously changes from rest position to maximum contraction position.