Effect of cycle time and duty cycle on psychophysically determined acceptable levels in a highly repetitive task
Bibliographic record
Abstract
Psychophysical methodology has been used to develop guidelines for lifting and more recently similar methods have been applied to repetitive upper limb movements. While a range of cycle times are usually used, there is often no control for duty cycle. The purpose of this paper is to present psychophysically determined acceptable torques for a common upper limb task, with both cycle time and duty cycle conditions set by the researcher. Eight female participants, sitting at adjustable workstations, performed a simulated in-line screw running task. A computer-controlled torque motor applied a torque every 3, 6, 12 or 20 s with a duty cycle of 25, 50 or 83%. The participants worked with one set of conditions each day and self-selected the highest torque that they felt was acceptable without developing undue pain and discomfort. Duty cycle was found to significantly affect the amount of torque selected. With duty cycle controlled, cycle time was no longer found to have any significant effect on selected torque. Acceptable torques for 25, 50 and 83% duty cycles were 1.09, 0.9 and 0.73 Nm. Discomfort and stiffness were concentrated on the back of the hand and on the thumb web. These findings suggest that increased perception of discomfort with increased frequency (decreased cycle time) may be related to decreased rest/recovery time for muscles.
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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 | 0.008 |
| 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".