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

Objective . 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. Methods . The 3-DoF wrist torques and high-density surface EMG signals were recorded concurrently from eight able-bodied 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 multi-DoF training). Main results . 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 R2 of 0.76±0.12 and normalized root mean square error of 11.4±3.1%. Conclusion and Significance . 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.