Exploring Mechanomyography as a Tool for Fatigue Monitoring in Neurorehabilitation

Abstract

Neurological conditions such as stroke often result in motor impairments that limit functional independence and quality of life. Neurorehabilitation aims to restore motor function through intensive, repetitive therapy, relying increasingly on technologies that provide objective measures and therapeutic assistance. Among these, functional electrical stimulation (FES) is a widely used technique that activates paralyzed or weakened muscles to produce functional movements and promote recovery. However, a significant limitation of FES is the rapid onset of muscle fatigue, which shortens the duration of effective contractions, reducing the therapeutic benefits of treatment. Addressing this challenge requires reliable, real-time monitoring of fatigue to enable adaptive control of stimulation parameters. This thesis investigates mechanomyography (MMG) as a robust, low-cost tool for detecting muscle fatigue during both static and dynamic tasks. Experimental studies showed that MMG features reflected fatigue development in static contractions and continued to perform reliably during dynamic movements. A direct comparison between MMG and electromyography (EMG) revealed that MMG offers comparable sensitivity to fatigue-induced changes while avoiding several practical limitations inherent to EMG recordings. A fatigue-thresholding method using MMG signal features was proposed for integration into real-time FES control systems, aiming to adjust stimulation parameters dynamically to prolong effective contractions. Overall, the findings highlight MMG’s potential to improve fatigue detection and support next generation neurorehabilitation technologies like adaptive FES.