Exploring Localized Muscle Fatigue Responses at Current Upper-Extremity Ergonomics Threshold Limit Values
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate localized muscle fatigue responses at three upper-extremity ergonomics threshold limit value (TLV) duty cycles. BACKGROUND: Recently, a TLV equation was published to help mitigate excessive development of localized muscle fatigue in repetitive upper limb tasks. This equation predicts acceptable levels of maximal voluntary contraction (% MVC) for a given duty cycle (DC). Experimental validation of this TLV curve has not yet been reported, which can help guide utilization by practitioners. METHOD: Eighteen participants performed intermittent isometric elbow flexion efforts, in three separate counter-balanced sessions, at workloads defined by the American Conference of Governmental Industrial Hygenists' (ACGIH) TLV equation: low DC (20% DC, 29.6% MVC), medium DC (40% DC, 19.7% MVC), and high DC (60% DC, 13.9% MVC). Targeted localized muscle fatigue (LMF) of the biceps brachii was tracked across numerous response variables, including decline in strength (MVC), electromyography (EMG) amplitude and mean power frequency (MnPF), and several psychophysical ratings. RESULTS: At task completion, biceps MnPF and MVC (strength) were significantly different between each TLV workload, with the high DC condition eliciting the largest declines in MnPF and MVC. CONCLUSION: Findings demonstrate that working at different DCs along the ACGIH TLV curve may not be equivalent in preventing excessive LMF. Higher DC workloads elicited a greater LMF response across several response variables. APPLICATION: High DC work of the upper extremity should be avoided to mitigate excess LMF development. Current TLVs for repetitive upper-extremity work may overestimate acceptable relative contraction thresholds, particularly at higher duty cycles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".