Adjustments for Improved REBA and RULA Methods Based on Postural Sway Analysis
Bibliographic record
Abstract
Ergonomic risk assessment methods are widely utilized in the construction sector to mitigate work-related musculoskeletal disorders (WMSDs).However, current methods, such as the Rapid Entire Body Assessment (REBA) and the Rapid Upper Limb Assessment (RULA), often encounter issues related to overestimation and fluctuation, primarily due to sharp boundaries defined by ambiguous terms.Specifically, a 0° threshold is frequently used as a strict boundary for adjustment factors, disregarding minor joint movements and muscle activation.This study aims to quantify the upper arm-related risk adjustment factors in REBA and RULA by analyzing angle tolerances through muscle activation data.Angle tolerances are derived using surface electromyography (sEMG) data from specific muscles during arm abduction movement.A total of 23 participants' data are used for this study.The main results include 1) revealing 0° threshold is unsuitable as an adjustment boundary through muscle activation analysis before arm abduction.2) identifying joint tolerance defined by the endurance limit of 10% maximal voluntary isometric contraction (%MVC).Finally, these tolerances are subsequently used to refine posture categorization within the risk rating adjustment process, resulting in a modified upper arm adjustment factor in the REBA and RULA frameworks, which incorporates posture sway for more accurate risk estimation.The outcomes of this study are expected to mitigate the overestimation and fluctuation issues inherent in REBA and RULA while introducing a novel integration of muscle status and risk detection for risk assessment.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".