Prediction of finger kinematics from discharge timings of motor units: implications for intuitive control of myoelectric prostheses.

Abstract

The aim of the study was to characterize the accuracy in the identification of motor unit discharges during natural movements using high-density electromyography (EMG) signals and to investigate their correlation with finger kinematics. High-density EMG signals of forearm muscles and finger joint angles were recorded concurrently during hand movements of ten able-bodied subjects. EMG signals were decomposed into motor unit spike trains (MUSTs) with a blind-source separation method. The first principle component (FPC) of the low-pass filtered MUST was correlated with finger joint angles. On average, [Formula: see text] motor units were identified during each individual finger task with an estimated decomposition accuracy [Formula: see text]85%. The FPC extracted from discharge rates was strongly associated to the joint angles ([Formula: see text]), and preceded the joint angles on average by [Formula: see text] ms. Moreover, the FPC outperformed two time-domain features (the EMG envelop and the root mean square of EMG) in estimating joint angles. These results indicated the possibility of identifying individual motor unit behavior in dynamic natural contractions. Moreover, the strong association between motor unit discharge behaviors and kinematics proves the potential of the approach for the simultaneous and proportional control of prostheses.