Long-Term Finger Force Predictions Using Motoneuron Discharge Activities

Abstract

Surface electromyogram (EMG) signals have been a preferred modality for motor intent detections in the fields of robotic control, rehabilitation, and health monitoring. However, current EMG-based measurement techniques suffer a degradation in performance cross session over time due to factors such as shifts in electrode placement, changes in muscle states, and environmental noise. To address this challenge, we developed a novel neural-drive approach, capable of robust cross-day predictions of individual finger forces. Specifically, high-density EMG (HD-EMG) data were collected from flexor and extensor muscles during single-finger and multifinger tasks. The experimental procedure was repeated three times (sessions), with an average interval of 6.58 days between sessions. We first decomposed the EMG signals in a session to obtain separation matrices that contained motor unit (MU) information in the EMG signals. We then refined the separation matrices that accurately reflected individual fingers. The corresponding separation matrices were applied to EMG signals in the other two sessions to derive the neural drive for force predictions of individual fingers. Our results revealed that the cross-session performance was comparable with the within-session performance. In addition, the neural-drive approach can outperform the conventional EMG-amplitude approach, especially in the cross-session performance. Our developed approach can enhance the long-term reliability of finger force predictions and holds potential for various practical applications.