Learning and Online Replication of Grasp Forces from Electromyography Signals for Prosthetic Finger Control
Journal:
arXiv
Published Date:
May 5, 2025
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.
