Estimating the single-DoF kinematics of wrist from motor unit behaviors

Abstract

The aim of this study was to characterize the accuracy in the identification of motor unit discharges and to estimate wrist kinematics from motor unit behaviors. High-density electromyography (EMG) of forearm muscles and wrist torques in three degrees-of-freedom (DoFs) were recorded concurrently during wrist movements of 8 able-bodied subjects. The EMG signals were decomposed into motor unit spike trains (MUSTs) with a blind-source separation algorithm. Two methods based on principal component analysis and regression model respectively were proposed to estimate wrist torques in three DoFs. On average, 19±6 MUSTs were identified in each trial with accuracy > 85%. For all conditions, the Pearson correlation coefficient between estimations and recordings was always > 0.8. The average normalized root mean square error of two methods was 0.15 ± 0.03 and 0.16 0.05, respectively. These results indicated the identification ±of motor unit behaviors with high confidence, thus having the potential to be practical approaches for prosthesis control.