Mapping Individual Motor Unit Activity to Continuous Three-DoF Wrist Torques: Perspectives for Myoelectric Control

The surface electromyography (EMG) decomposition techniques provide access to motor neuron activities and have been applied to myoelectric control schemes. However, the current decomposition-based myoelectric control mainly focuses on discrete gestures or single-DoF continuous movements. In this study, we aimed to map the motor unit discharges, which were identified from high-density surface EMG, to the three degrees of freedom (DoFs) wrist movements. The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight non-disabled subjects. The experimental protocol included single-DoF movements and their various combinations. We decoded the motor unit discharges from the EMG signals using a segment-wise decomposition algorithm. Then the neural features were extracted from motor unit discharges and projected to wrist torques with a multiple linear regression model. We compared the performance of two neural features (twitch model and spike counting) and two training schemes (single-DoF and multiDoF training). On average, 145 +/- 33 motor units were identified from each subject, with a pulse-to-noise ratio of 30.8 +/- 4.2 dB. Both neural features exhibited high estimation accuracy of 3-DoF wrist torques, with an average R-2 of 0.76 +/- 0.12 and normalized root mean square error of 11.4 +/- 3.1%. These results demonstrated the efficiency of the proposed method in continuous estimation of 3-DoF wrist torques, which has the potential to advance dexterous myoelectric control based on neural information.

Automated summary

In this study, motor unit discharges were mapped to three degrees of freedom wrist movements using high-density surface electromyography (EMG). The proposed method efficiently estimated 3-DoF wrist torques, demonstrating its potential for advancing dexterous myoelectric control based on neural information.