Muscle Synergy-based Grasp Classification for Robotic Hand Prosthetics

Abstract

The main goal of this study is analyzing whether muscle synergies based on surface electromyography (EMG) measurements could be used for hand posture classification in the context of robotic prosthetic control. Target grasps were selected according to usefulness in daily activities. Additionally, due to the feasibility constraints of robotic prosthetics, only 14 gestures (13 feasible grasps and 1 resting state) were analyzed. EMG signals of intact-limb subjects were decomposed into base and activation components for muscle activity evaluation. The results demonstrate that features based on muscle synergies derived from non-negative matrix factorization (NMF) outperform the ones derived from principal component analysis (PCA). Moreover, we also examine the robustness of these methods in the absence of electrodes (muscle importance) and show that NMF is able to provide sufficiently accurate results.