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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The advent of new technology has opened opportunities to the computer users to consider alternate ways for humans to interface with machines. Research groups are looking to use computer interfaces that can allow users to communicate with devices in a subtle way and allow virtual reality environments to create an immersing experience. Electromyography based wearable input device can interface with mobile devices in a subtle way. Electromyography can also be used for interactive computer gaming as well as to control flights through virtual joysticks and controllers. This study provides a brief review on various ways for humans electromyography (EMG) signal to interface with machines. Therefore, the crux of this study is to overview the up to date developments and research related to the EMG interface with the wearable devices. This study further opens up a passage for researchers and end users to advocate an excellent understanding of EMG interfacing with mechanical devices.

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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