Mechanomyography enables fatigue detection during muscle stimulation therapy
A Mechanomyography-Based Integrated Sensing-Stimulation System for Fatigue-Aware Neuromuscular Therapy
Human-Computer Interaction
Summary
Muscle stimulation therapy helps people move but can make muscles tired, which limits how well it works. The usual way to monitor muscle fatigue uses electrical signals, but these get mixed up with the stimulation itself. The authors designed a system that uses mechanical vibrations from muscles, called mechanomyography, to track fatigue accurately even during stimulation. Their tests showed this method can tell when muscles are getting tired, which could help make therapy smarter and more effective.
What this means in practice
- •For physical therapists: Monitor muscle fatigue accurately during electrical stimulation therapy to adjust treatment intensity and timing.
- •For medical device developers: Integrate artifact-resistant mechanomyography sensors into neuromuscular stimulators for real-time fatigue monitoring.$Commercial implications: Enables building advanced muscle stimulation devices that adapt therapy based on fatigue, improving patient outcomes.
Authors
Xiao Jin, Cong Liu, Zhenhua Yu
Abstract
Muscle fatigue limits the effectiveness of functional electrical stimulation (FES), yet fatigue monitoring during active stimulation remains challenging because electromyography (EMG) is severely contaminated by stimulation artifacts. To address this issue, this study proposes an integrated mechanomyography-based sensing framework (MMG-FES) for artifact-resistant fatigue monitoring during stimulation. Fatigue states were first characterized during voluntary movement using established EMG spectral indicators, including median frequency (MF) and mean power frequency (MPF), and then linked to MMG-derived features, including high-frequency energy ratio (HFER) and spectral bandwidth (BW), for fatigue recognition under stimulation conditions. Experimental results showed consistent fatigue-related trends, with EMG spectral features decreasing and MMG features exhibiting coordinated spectral redistribution across repeated contractions. Using these features, the proposed framework achieved fatigue-state recognition accuracies of 0.93 and 0.96 with logistic regression and random forest, respectively. These findings support MMG as a physiologically meaningful and stimulation-compatible modality for fatigue-aware closed-loop neuromuscular stimulation.