Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control

TL;DR

This paper presents a neural network-based system that estimates fingertip forces from EMG signals to enable intuitive, online control of a prosthetic finger, improving prosthesis functionality for partial hand amputees.

Contribution

It introduces a novel EMG-driven force estimation model and a prototype prosthetic finger with real-time control, validated through experiments with unimpaired subjects.

Findings

Effective force prediction from EMG signals.

Precise online control demonstrated in user trials.

Potential for improved prosthetic finger functionality.

Abstract

Partial hand amputations significantly affect the physical and psychosocial well-being of individuals, yet intuitive control of externally powered prostheses remains an open challenge. To address this gap, we developed a force-controlled prosthetic finger activated by electromyography (EMG) signals. The prototype, constructed around a wrist brace, functions as a supernumerary finger placed near the index, allowing for early-stage evaluation on unimpaired subjects. A neural network-based model was then implemented to estimate fingertip forces from EMG inputs, allowing for online adjustment of the prosthetic finger grip strength. The force estimation model was validated through experiments with ten participants, demonstrating its effectiveness in predicting forces. Additionally, online trials with four users wearing the prosthesis exhibited precise control over the device. Our findings highlight the potential of using EMG-based force estimation to enhance the functionality of prosthetic fingers.