Estimation of Finger Joint Angle Based on Neural Drive Extracted from High-Density Electromyography

Robust human-machine interactions require accurate and intuitive interfaces. Neural signals associated with muscle activities are widely used as the interface signals. This preliminary study evaluated the feasibility of a novel neural-drive-based interface in estimating the individual finger joint angles. The motor unit pool discharge probability was used to predict the neural drive associated with the fine control of the finger joint angle during individual finger extension movement. To obtain the neural drive information, individual motor unit discharge events were extracted from the decomposition of high-density surface electromyogram (sEMG) signals, and discharge events from different motor units were pooled to from a composite discharge event train. The neural-drive-based estimate was obtained by calculating the probability (normalized frequency) of the populational motor unit discharge. The global EMG signal (root-mean-squared value) was also used to estimate the joint angles as a control condition. Our preliminary results showed that the accuracy and stability of the neural-drive-based approach outperformed the classic EMG-based method. Our findings suggest that the novel neural-drive-based interface could be used as a promising control input for intuitive dynamic control of a robotic hand.