A predictive equation for maximum acceptable efforts based on duty cycle in repetitive back-involved tasks
Bibliographic record
Abstract
• Development of a predictive equation correlating MAE with DC for back-involved repetitive tasks. • The equation shows a strong negative relationship between DC and normalized MAE. • Statistical analysis indicates the equation's universality across different scenarios with minor effects of parameters. • Predictive equation provides an individual-specific approach to mitigate the risk of musculoskeletal disorders. Introduction: There are many different tasks involved within a workplace, and assessing the required efforts for performing them depends on factors such as the human’s body posture, task nature, and number of repetitions. This study aims to develop an equation for back-involved repetitive tasks that relates the maximum acceptable effort (MAE), the maximum acceptable efforts that an individual can sustain for a specific task and is expressed as a percentage of the maximum strength, to the duty cycle, the amount of time an individual is engaged in a task relative to the total time. The equation was derived based on psychophysical data collected from previous studies on lifting, lowering, and carrying tasks. The literature search identified studies reporting maximum acceptable loads (e.g., forces and toques) for back-involved tasks. Method: Data analysis was done by calculating duty cycles and for each task. Statistical tests were conducted to compare the results across different parameters, such as sex, task nature, lifting box size, box distance from the body, and population percentages. Results: The results showed a strong negative relationship between duty cycle and MAE. This relationship shows that by increasing the duty cycle, MAE should be decreased to be acceptable and prevent worker’s fatigue. The developed equation was compared to existing equations for upper-limb tasks and demonstrated a close resemblance. Additionally, statistical analysis indicated that the proposed equation eliminated the effects of various parameters. The proposed equation provides an individual-specific approach for estimating MAEs and can contribute to preventing workers’ fatigue and injury and reducing their associated costs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".