Abstract
Muscle fatigue, as a common physiological phenomenon, has attracted much attention in the fields of rehabilitation and athletic training. A wearable technology for monitoring the muscle fatigue anytime and anywhere is urgently needed. In this paper we apply Electrical impedance myography (EIM) technique, usually used for non-invasive detection of neuromuscular diseases with the four-electrode array, for evaluation of the local muscle fatigue status via the variation of electrical impedance. An equivalent multilayer inhomogeneous 3D finite element model of human arm was built in order to optimize the four-electrode configuration to improve EIM detection sensitivity. Current density in muscle layer and differential potential of induction electrodes were selected as the evaluation indexes for optimization. Then the in vivo experiments of dynamic contraction with different maximal voluntary contractions (MVC) were performed on the biceps brachii muscle of eight healthy volunteers. The results showed that muscle resistance ( $R$ ) decreased almost $8~\Omega $ from the completely relaxed muscle to exhaustion, which is the same trend as for the median frequency (MF) of measured surface electromyography (sEMG) signals. The model and experiments in this paper indicate the feasibility and efficiency of EIM for detection of muscle fatigue using wearable devices.
Highlights
The continuous movement of muscles gradually reduces their work capacity, maximum contraction force and output power [1]
The AC/DC module in COMSOL Multiphysics 5.2a simulation software is used to find the optimal configuration of Electrical impedance myography (EIM) electrodes in EIM method by using finite element method
The governing equations of the EIM finite element method are based on the Maxwell’s equations because they can describe the nature of the electromagnetic field acting on organisms
Summary
The continuous movement of muscles gradually reduces their work capacity, maximum contraction force and output power [1]. A wearable device that can monitor muscle fatigue at any time would be very useful for exercise rehabilitation, muscle disease diagnosis, sports training, and other fields. Several indicators, such as muscle oxygen saturation [3], lactic acid concentration [4], ultrasound image entropy [5], and surface electromyography (sEMG) [6], [7], are used for muscle fatigue evaluation. Among these indicators, sEMG is the most common and widely used [8]–[10]. SEMG has small amplitude (order of microvolts), wide frequency variation range, and is prone to interference
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