Finger Force Estimation Using Motor Unit Discharges Across Forearm Postures

Background: Myoelectric- based decoding has gained popularity in upper- limb neural-machine interfaces. Motor unit (MU) firings decomposed from surface electromyographic (EMG) signals can represent motor intent, but EMG properties at different arm configurations can change due to electrode shift and differing neuromuscular states. This study investigated whether isometric fingertip force estimation using MU firings is robust to forearm rotations from a neutral to either a fully pronated or supinated posture. Methods: We extracted MU information from high- density EMG of the extensor digitorum communis in two ways: (1) Decomposed EMG in all three postures (MU-AllPost); and (2) Decomposed EMG in neutral posture (MU-Neu), and extracted MUs (separation matrix) were applied to other postures. Populational MU firing frequency estimated forces scaled to subjects’ maximum voluntary contraction (MVC) using a regression analysis. The results were compared with the conventional EMG-amplitude method. Results: We found largely similar root-mean-square errors (RMSE) for the two MU-methods, indicating that MU decomposition was robust to postural differences. MU-methods demonstrated lower RMSE in the ring (EMG = 6.23, MU-AllPost = 5.72, MU-Neu = 5.64% MVC) and pinky (EMG = 6.12, MU-AllPost = 4.95, MU-Neu = 5.36% MVC) fingers, with mixed results in the middle finger (EMG = 5.47, MU-AllPost = 5.52, MU-Neu = 6.19% MVC). Conclusion: Our results suggest that MU firings can be extracted reliably with little influence from forearm posture, highlighting its potential as an alternative decoding scheme for robust and continuous control of assistive devices.

I. Introduction

In recent decades, neural-machine interfaces have advanced with promise to assist and rehabilitate individuals with motor impairments by decoding user intent to control assistive devices [1]. Different techniques have been developed to record activity at varying levels of the nervous system. Electroencephalography [2], electrocorticography [3], and intracortical arrays [4] allow brain- machine interfaces, while peripheral nerve implants [5] and surface electromyography (EMG) [6] enable communication from the peripheral nervous system. The EMG signal reflects the summation [7] of motor unit action potentials (MUAPs) from a number of motor units (MUs) (each a motor neuron and all the muscle fibers it innervates), considered the smallest independent control units of muscle activation [8]. The EMG- amplitude signal gives a global measure of activation, and historically has been employed widely in myoelectric control of assistive robots in the upper limb [9], [10], as it provides a noninvasive, easy-to-implement input signal.