Voluntary and FES-Induced Finger Movement Estimation Using Muscle Deformation Features

Abstract

It is practically crucial to estimate dexterous hand movement for applications such as prosthetics and rehabilitation, especially scenarios actively assisted with functional electrical stimulation (FES). This paper attempts to estimate hand finger movement through forearm muscle deformation for both clinical scenarios with voluntary and FES-induced muscle contractions based on A-mode ultrasound sensing approaches. Six healthy subjects participated in the experiment of middle finger movement. In order to evaluate the sensing approach, a least-squares support-vector-machine (LS-SVM) model and a linear-ridge (LR) model were developed to estimate the hand finger movement for datasets captured from the flexor digitorum superficialis (FDS) muscle belly, extensor digitorum muscle belly with or without FES. The average results of using FDS datasets as inputs revealed excellent evaluation performance ( $R^2>0.9$ and $\text{RMSE} ) both for LR and LS-SVM regression technologies. Furthermore, finger-movements-related muscle deformation features corresponding to voluntary and FES-induced contraction were proved to be different. These results demonstrated the potential application of the forearm muscle deformation in wearable A-mode ultrasound-based prosthesis finger movements control and optimizing the closed-loop FES system for motor function rehabilitation.