Estimating maximum acceptable duty cycles (MADC) for overhead exertions
Bibliographic record
Abstract
Overhead work poses a significant risk for shoulder musculoskeletal disorders due to elevated arm postures and sustained loading, yet current ergonomics tools provide limited guidance on the acceptable percentage of time such tasks can be performed. This concept, referred to as the Maximum Acceptable Duty Cycle (MADC), represents the proportion of time within a work cycle that an exertion can be sustained at a psychophysically acceptable level. This study addresses that gap by reconfiguring an existing ergonomics assessment tool for overhead work to estimate MADC, rather than maximum acceptable forces. MADC values across the overhead workspace were mapped using computational modeling of over 800,000 overhead hand positions under three superior-directed load conditions (5, 10, and 20 N). MADC varied substantially with hand position and force demand: at 5 N, values ranged from 0-40 %, while at 20 N, MADC never exceeded 14 %. A consistent ergonomic 'sweet spot' was identified slightly above and forward of the shoulder, where MADC is maximized, providing the largest design space for allowable task duty cycles. The reconfigured tool offers actionable, evidence-based guidance for overhead task design by informing duty cycle limits in industrial settings, where current one-size-fits-all thresholds lack empirical justification.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".