An electromyography-based multi-muscle fatigue model to investigate operational task performance

We developed a multi-muscle fatigue model (MMFM) by incorporating electromyography (EMG)-based amplitude and frequency parameters, the fast-to-slow twitch muscle fiber ratio, a time multiplier to linearize the cumulative effect of time, and a muscle multiplier to standardize the combined effect of the number of muscles being considered. We validated the model by investigating fatigue development patterns of 10 male subjects performing one sustained-till-exhaustion static and two repetitive dynamic tasks (low and high task difficulty levels) using 0.91 kg and 2.72 kg dumbbells. The results indicated that the MMFM was sensitive to fatigue-related neuromuscular changes and predicted shoulder joint fatigue accurately.

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Publication Details

Journal
Computer Methods in Biomechanics & Biomedical Engineering
Published
2025-05-27
DOI
https://doi.org/10.1080/10255842.2025.2510369
Citations
1
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
0.67
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article

An electromyography-based multi-muscle fatigue model to investigate operational task performance

S. Chowdhury, Leonardo H. Wei
1 citations
Computer Methods in Biomechanics & Biomedical Engineering
Muscle activation and electromyography studies
0.67
article

An electromyography-based multi-muscle fatigue model to investigate operational task performance

S. Chowdhury, Leonardo H. Wei
article en
1 citations

Abstract

We developed a multi-muscle fatigue model (MMFM) by incorporating electromyography (EMG)-based amplitude and frequency parameters, the fast-to-slow twitch muscle fiber ratio, a time multiplier to linearize the cumulative effect of time, and a muscle multiplier to standardize the combined effect of the number of muscles being considered. We validated the model by investigating fatigue development patterns of 10 male subjects performing one sustained-till-exhaustion static and two repetitive dynamic tasks (low and high task difficulty levels) using 0.91 kg and 2.72 kg dumbbells. The results indicated that the MMFM was sensitive to fatigue-related neuromuscular changes and predicted shoulder joint fatigue accurately.

Computer Methods in Biomechanics & Biomedical Engineering
Texas Tech University (US), University of Florida (US)
Climate action
Openalex Percentile: Top 31%
Muscle activation and electromyography studies
0.67
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