Electromyography Feature Extraction, Selection and Classification: A REVIEW - Pooja Kataria (International Journal of Advanced Science and Technology, 2020)
Inpast decades remarkable progress has done in processing, extraction of feature and classification of motion of EMG signals.EMG Signals are termed as recording in terms of electric measurement that can be measured from the surface of skin which carry important and useful information regarding activity of muscle. How much muscle have been contracted i.e. in terms of strength can be monitored and captured by activity of muscle through skin surface extraction. EMG signal analysis is one of the main procedures to identify action, posture and gestures. These signals are basically complex by nature, with the utilization of proper signal processing tools, it can be classified muscular activities which are differentiated. In this paper we provide a comprehensive review of EMG Signal its feature selection & extraction and classification methods. Wavelet transforms and various classifiers such as PCA, LDA and SVM has been reviewed to classify and analyze the EMG Signal in an appropriate way. We generally first review the overall basis of EMG signals and its various processing stages. Finally the paper has been concluded with small discussion on recent challenges and future work.