Motion Intensity Extraction Scheme for Simultaneous Recognition of Wrist/Hand Motions

Abstract

Surface electromyography contains muscular information representing gestures and corresponding forces. However, conventional sEMG-based motion recognition methods, such as pattern classification and regression, have intrinsic limitations due to the complex characteristics of sEMG signals. In this paper, motion intensity, a highly selective sEMG feature proportional to the level of muscle contraction, is proposed. The motion intensity feature allows proportional and simultaneous recognition of multiple degrees of freedom. The proposed method was demonstrated in terms of simultaneous recognition of wrist/hand motions. The result shows that the proposed method can successfully decompose sEMG signals into highly selective signals to target motions. In future works, the proposed method will be adapted for more subjects and to sEMG applications for practical evaluation considering various grasping motions.