Abstract—Electromyography (EMG) signals are muscles signals that enable the identification of human movements without the need of complex human kinematics calculations. Researchers prefer EMG signals as input signals to control prosthetic arms and exoskeleton robots. However, the proper algorithm to classify human movements from raw EMG signals has been an interesting and challenging topic to researchers. Various studies have been carried out to produce EMG-based human movement classification that gives high accuracy and high reliability. In this paper, the methods used in EMG signal acquisition and processing are reviewed. The different types of feature extraction techniques preferred by researchers are also discussed, including some combination and comparison of feature extraction techniques. This paper also reviews the different types of classifiers favored by researchers to recognize human movements based on EMG signals. The current applications of EMG signals are also reviewed. 

Index Terms—classification, electromyography, feature extraction, human movement

Cite: Evon Lim Wan Ting, Almon Chai, and Lim Phei Chin, "A Review on EMG Signal Classification and Applications," International Journal of Signal Processing Systems, Vol. 10, No. 1, pp. 1-6, March 2022. doi: 10.18178/ijsps.10.1.1-6