Wearable device uses muscle sound to improve electrical muscle stimulation control
A Co-Located MMG-FES Wearable Interface for Action-Updated State-Gated Stimulation
Human-Computer Interaction
Summary
Sometimes machines help people move their muscles by sending electrical signals, but they can't always tell how the muscle is doing during use. The authors built a small wearable device that listens to the sounds muscles make when they move, which helps it know the muscle's condition better. They showed it can detect muscle actions accurately and adjust electrical signals accordingly. This new approach keeps working well over time and during real movements.
What this means in practice
- •For physical therapy device makers: Integrate on-skin MMG sensing with FES to track muscle state and improve timing of stimulation bursts during rehabilitation exercises.$Commercial implications: Enables development of smarter wearable FES therapy products that adapt stimulation per muscle action, improving user outcomes.
- •For assistive robotics developers: Use embedded MMG-based action monitoring in wearable systems to deliver muscle stimulation precisely after intended movements for better assistive control.
Authors
Cong Liu, Xiao Jin, Zhenhua Yu
Abstract
Functional electrical stimulation (FES) assists movement, but fixed schedules cannot track how a repeatedly activated muscle changes, and surface electromyography (EMG) near the stimulation electrodes is often obscured by artifacts. Prior work recorded mechanomyography (MMG) passively during closed-loop FES cycling and estimated FES-induced fatigue or predicted fatigue for FES control, without gating stimulation with stimulated-site MMG. In this study, we present VG-FES, a wearable module co-locating microphone-based MMG, programmable charge-balanced biphasic stimulation, and embedded state-gated control at one on-skin thigh site. After each action, we compare rectus femoris (RF) mean power frequency (MPF) with an early-session baseline of the same direction, giving a relative state. A threshold on its window means enables fixed-duration bursts separated by rest periods. During onboard stimulation, MMG remained recordable, whereas near-site EMG was artifact-dominated. Under prolonged external stimulation, cycle-wise MMG tracked an independent local-pressure reference (Spearman rho = 0.968). In leave-one-session-out evaluation on 2284 squat actions (11 sessions, eight participants), MMG-based action segmentation reached an event-detection F1 of 0.923 and direction classification a balanced accuracy of 0.925. Online, over 15.69 min of self-paced squats, one participant received four bursts while MMG acquisition and action monitoring continued. Mechanical feedback from the stimulated site offers wearable FES and assistive robotics a per-action basis for state-gated stimulation.