Comparative Analysis of Various Types of Classifier for Surface EMG Signal in Order to Improve Classification Accuracy

Surface EMG is an important signal originating from human body while doing different movements. This can be utilized for various applications like movement classification, diagnosing neuromuscular disorders, prosthetic control and many more. Surface EMG signal analysis is complex in nature because of its random nature. Several researchers are trying to provide solutions for tackling this problem in the form of improving acquisition circuit for surface EMG signal, increasing the density of sensors during acquisition process, extracting novel features which could give more information and so on. One of the crucial stages while analyzing surface EMG signal is selection of feature sets and classification algorithm. In present work the authors tried different time domain feature sets and their combinations to improve classification accuracy. It was observed that a combination of feature sets improves classification accuracy (95.7%) but response time is increased. The present study explains the optimized solution for the aforesaid problem. It was also observed that Ensemble classifier in bagged tree variant gives maximum classification accuracy but takes too much time in training and classification.