Performance Evaluation of EMG Pattern Recognition Techniques While Increasing The Number of Movement Classes

Abstract

:In the past few years of research done in the field of myoelectric control, many researchers have proposed several models imploying a combination of different features and classifiers to increase the movement classes, but all that work fails to explain if there is any correlation between multi-class classification and its accuracy. This paper focuses on finding the factors that decide the limit of movement classes that machine learning algorithms can accurately differentiate and to evaluate the performance of pattern classification techniques using the sEMG signal when the number of movement classes is increased while keeping the simplicity of the system. The results were obtained for eight channels sEMG signal using 7 independent time-domain features and four feature set combinations over 4 classifiers (Support Vector Machine(SVM), K-Nearest Neighbour(K-NN), Decision Tree(DT), and Naïve Bayes(NB)). Then the number of classes was increased in the manner of 5, 7, 10, 12, and 15 classes to determine the highest number of movement classes that the sEMG system with above-described features can classify efficiently. And the effect of increasing the number of movement classes on system accuracy was observed. The highest accuracies for all five class progression were obtained for SVM with the MFL feature, and for DT using MAV, it was successfully observed that the NB classifier had minimum performance depletion for the features used in this work