A fuzzy logic approach to posture-based ergonomic analysis for field observation and assessment of construction manual operations
Bibliographic record
Abstract
In construction, workers are frequently exposed to ergonomic risks that can lead to musculoskeletal disorders. To prevent ergonomic injuries, proper assessment of ergonomic risk is a key to identifying risk factors and modifying work practice in a timely manner. In field observation, however, difficulties in visually estimating human postures (e.g., body joint angles) required for ergonomic analysis have led to inconsistent results due to the subjectiveness of observers. This study thus proposes a fuzzy logic approach to posture-based ergonomic evaluation tools. Rapid Upper Limb Assessment (RULA) is selected as a case study to describe the fuzzy logic modelling of RULA scoring systems and discuss the application to modular construction shops. The results of validation comparing correlations with biomechanical analysis — used as a ground truth — reveal that the proposed system produces more accurate results than traditional methods and hence helps minimize human errors in observation for reliable on-site ergonomic assessment.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".