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.
