Evidence map›Paper›PMID 41254749›Full record

ArticleJournal of neuroengineering and rehabilitation2025

Perceived fatigue progression tracking during manual handling tasks using sEMG recordings.

Armin Bonakdar, Catherine Disselhorst-Klug, Karla Beltran Martinez, Ali Golabchi, Mahdi Tavakoli, Hossein Rouhani

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. [Cluster-guided adaptive Transformer for muscle fatigue prediction].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Armin BonakdarDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada.
Catherine Disselhorst-KlugDepartment of Rehabilitation & Prevention Engineering, Institute of Applied Medical Engineering, RWTH Aachen University, Aachen, Germany.
Karla Beltran MartinezDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada.
Ali GolabchiDepartment of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada.
Mahdi TavakoliDepartment of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada.
Hossein RouhaniDepartment of Mechanical Engineering, University of Alberta, Edmonton, AB, T6G 1H9, Canada. hrouhani@ualberta.ca.

Funding

the Natural Sciences and Engineering Research Council of Canada in partnership with Alberta Innovates Grant # ALLRP 567348 - 21
6 · The paper itself

Abstract

Physical fatigue significantly contributes to work-related musculoskeletal disorders, highlighting the need to understand its effects during manual handling tasks for effective prevention strategies. This study examines the correlation between changes in the myoelectric manifestation of fatigue (MMF) indicators and participants' perceived exertion during prolonged manual handling tasks using surface electromyography (sEMG) sensors. Given that the task involves various activities with different muscle engagements and ranges of motion, joint angles were obtained using inertial measurement units and used to segment the sEMG recordings based on activity and the joints' range of motion. Linear and complexity-based MMF indicators were then extracted from these segments, and their correlation with perceived exertion was evaluated. Linear indicators, such as activation level and median frequency, showed significant correlations with perceived fatigue (p < 0.05) in the lower leg muscles, including Lateral Gastrocnemius and Tibialis Anterior, with inconsistent results in other muscles. In contrast, complexity-based indicators, including mobility, fuzzy entropy, and Dimitrov's index, demonstrated significant correlations (p < 0.05) in eight out of ten studied muscles across all activities, revealing reduced signal variability, increased irregularity, and shifts in spectral properties as fatigue progressed. Finally, a deep learning model was developed, achieving 69% accuracy for a five-stage fatigue classification using MMF indicators. Our study showed that complexity-based MMF indicators outperform linear ones in monitoring perceived fatigue and that the full set of indicators can be used for classifying fatigue stages using machine learning, offering a practical approach for personalized fatigue and health monitoring in occupational settings.

Indexed as

ElectromyographyFatigueMuscle FatigueMuscle, SkeletalAdultFemaleHumansMalePhysical ExertionRange of Motion, ArticularYoung AdultDeep learningInertial measurement unitsPerceived fatigue measurementSurface electromyographyWork-related musculoskeletal disorders

Identifiers

PMID41254749
PMCPMC12625538

What OpenQuestion holds

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LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.