Effect of Cycle Time and Duty Cycle on Muscle Activity during a Repetitive Manual Task
Bibliographic record
Abstract
Lack of rest of the so called “Cinderella” fibres during repetitive or static tasks has been suggested as one cause for local metabolic disturbances leading to muscle pain. The purpose of this study was to examine the effect of duty cycle and cycle time on the ability of the muscle to rest during a simulated screw running task. Eight females sat at an adjustable workstation and grasped a cylindrical handle driven by a computer controlled torque motor. The motor applied 1.4 Nm of torque at combinations of one of four cycle times (3, 6, 12 and 20 s) and 3 duty cycles (25, 50 and 83% of time). While working, hand torque, hand grip force and EMG from the extensor carpi radialus brevis muscle were measured. “Biomechanical” duty cycles were calculated from the division of the mean of the whole cycle by the mean of the “work” portion of the cycle. A gap analysis was performed (EMG amplitude below .5% MVC for at least .2 s). The results show that biomechanical duty cycles (force and EMG) are longer than the applied motor duty cycle. The increase in duty cycle was greater for muscle than hand force and greatest at 25% motor duty cycle. At 83% applied duty cycle, the increase in muscle biomechanical duty cycle was such that EMG activity occupied more than 90% of the cycle time. The gap analysis showed that with short cycle times (< 6 s) the amount of time available for the muscle to completely shut off is reduced with increased potential for musculoskeletal disorders.
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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 | 0.003 |
| 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) | 0.002 | 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".