Finger Joint Angle Estimation Based on Motoneuron Discharge Activities

Estimation of joint kinematics plays an important role in intuitive human-machine interactions. However, continuous and reliable estimation of small (e.g., the finger) joint angles is still a challenge. The objective of this study was to continuously estimate finger joint angles using populational motoneuron firing activities. Multi-channel surface electromyogram (sEMG) signals were obtained from the extensor digitorum communis muscles, while the subjects performed individual finger oscillatory extension movements at two different speeds. The individual finger movement was first classified based on the EMG signals. The discharge timings of individual motor units were extracted through high-density EMG decomposition, and were then pooled as a composite discharge train. The firing frequency of the populational motor unit firing events was used to represent the descending neural drive to the motor unit pool. A second-order polynomial regression was then performed to predict the measured metacarpophalangeal extension angle using the derived neural drive based on the neuronal firings. Our results showed that individual finger extension movement can be classified with >96% accuracy based on multi-channel EMG. The extension angles of individual fingers can be predicted continuously by the derived neural drive with R² values >0.8. The performance of the neural-drive-based approach was superior to the conventional EMG-amplitude-based approach, especially during fast movements. These findings indicated that the neural-drive-based interface was a promising approach to reliably predict individual finger kinematics.

I. Introduction

Human-Machine interactions have shown great promise in restoring motor function for individuals with neuromuscular disorders [1]–[3]. To drive these rehabilitative/assistive devices, biological signals, ranging from electrophysiological signals of the nerve system to limb biomechanical signals, are typically extracted to interface with the machine. In the past few years, we have seen substantial development in robust human-machine interface, in order to establish a reliable communication between humans and machines [4]–[6]. Specifically, the decoded neural information for the desired motor output can come from multiple sources, such as the brain, peripheral nerves, or muscles [7]–[9]. For example, motor intent has been decoded from neuronal activities of the motor cortex, and has been used to control neuroprosthesis of a subject with tetraplegia [9]. A proportional and simultaneous control of multiple degrees of freedom prostheses is also possible with decoded motor intent based on intramuscular electromyogram (EMG) signals [10].