Perceived fatigue progression tracking during manual handling tasks using sEMG recordings
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".