Time Derivative Moments Based Feature Extraction Approach for Recognition of Upper Limb Motions Using EMG

Abstract

Electromyography pattern recognition (EMG-PR) is extensively recognized in human–machine interactive applications such as prosthesis control and rehabilitation devices. Conventional time-domain (TD) features have been shown to produce a decent performance in upper limb movements’ classification. However, performance limitation exists in terms of classification accuracy. Hence, a novel feature set extraction on time derivative moments is proposed to improve the performance of EMG-PR in upper limb motion classification. A standardized and benchmark data base NinaPro (subdatabase2) has been used for examination of the proposed feature set. A total of eight intact subjects (sub1–sub8) have been selected for eight grasping motion's EMG signal collection. For classification of features, three classification techniques (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and support vector machine (SVM)) have been applied. The average improvisation with respect to the conventional TD features that was achieved for LDA, QDA, and SVM are 8.08%, 7.95%, and 6.41%, respectively.