Myoelectric Pattern Recognition Based on Muscle Synergies for Simultaneous Control of Dexterous Finger Movements

Abstract

Motor activities during daily life always involve simultaneous control of multiple degrees of freedom (DOFs), which has not yet been fully explored in myoelectric control due to difficulty in sufficiently decoding the complex neural control information. This study presents a novel framework for simultaneous myoelectric control based on pattern recognition incorporated with a muscle synergy motor control strategy for each DOF. An experiment for discriminating 18 dexterous finger movement tasks was designed to evaluate the performance of the framework for the simultaneous control of 5 DOFs. Task discrimination was assessed with 18 neurologically intact subjects, and the framework exhibited high accuracy (96.79% ±2.46%), outperforming three other methods, including the routine myoelectric pattern-recognition approach for conventional sequential control ( p <0.001). Furthermore, the feasibility of the proposed framework is also demonstrated with data from paretic muscles of two stroke subjects. This study offers a feasible solution for simultaneous myoelectric control of multiple DOFs, which has great potential for natural implementation in prosthetic hand devices and robotic training systems, especially for dexterous finger movements.