Predictive Force Control of Fingertip Induced by Functional Electrical Stimulation Based on a Hill-Type Muscle Model

Functional electrical stimulation (FES) is an effective technique for restoring or enhancing hand motor function in patients with neurological impairments, such as those recovering from stroke or spinal cord injuries. Although many studies have used phenomenological models to investigate the control of FES, few studies have simultaneously employed both methods to study finger output force. This study aims to accurately predict finger output force using the Hill model and a multi-joint finger model under different current conditions. The Hill model describes the mechanical properties of muscles and establishes the relationship between muscle activation and joint motion. In this study, personalized Hill models were customized for each participant, and the accuracy of the models was validated by comparing expected forces with actual force outputs. The experimental results show that under optimal conditions the normalized root mean square error between the measured force and the target force can be reduced to 5.1% of the maximum target force. Furthermore, this method enabled coordinated control of multiple fingers, facilitating a variety of grasping tasks. The proposed method offers significant potential for improving FES applications in hand rehabilitation and assistive robotics, providing a promising approach for precise force control in the rehabilitation of paralyzed patients.