Towards resolving the co-existing Impacts of multiple dynamic factors on the performance of EMG- pattern recognition based prostheses

Abstract

Background and Objective: Mobility of subject (MoS) and muscle contraction force variation (MCFV) have been shown to individually degrade the performance of multiple degrees of freedom electromyogram (EMG) pattern recognition (PR) based prostheses control systems. Though these factors (MoS-MCFV) co-exist simultaneously in the practical use the prosthesis, their combined impact on PR-based system has rarely been studied especially in the context of amputees who are the target users of the device. Methods: To address this problem, this study systematically investigated the co-existing impact of MoS-MCFV on the performance of PR-based movement intent classifier, using EMG recordings acquired from eight participants who performed multiple classes of targeted limb movements across static and non-static scenarios with three distinct muscle contraction force levels. Then, a robust feature extraction method that is invariant to the combined effect of MoS-MCFV, namely, invariant time-domain descriptor (invTDD), is proposed to optimally characterize the multi-class EMG signal patterns in the presence of both factors.