An Application of Fuzzy Ergonomic Assessment for Human Motion Analysis in Modular Construction
Bibliographic record
Abstract
Work-related Musculoskeletal Disorders (WMSDs) account for about 34% of non-fatal injuries resulting in days away from work in the construction industry. Particularly in modular construction, due to the repetitive nature of the manual tasks, workers are highly exposed to ergonomic risks. To identify workers’ awkward postures that potentially lead to WMSDs, ergonomic assessment tools have been developed and widely used by ergonomists and Occupational Health and Safety (OHS) practitioners. However, the accuracy of these methods is highly affected by the subjectiveness towards the user’s inputs (e.g., body joint angles), which are difficult to accurately determine in given observation time. Consequently, the imprecise estimates of worker postures may result in inaccurate final results and risk intervention plans. In an effort to address this issue, this study applies fuzzy logic techniques to ergonomic evaluation tools—e.g., Rapid Upper Limb Assessment (RULA). By modelling the range of input values using fuzzy sets rather than discrete boundaries, the imprecision inherit in the inputs has less impact on the final RULA score. As a result, an automated fuzzy expert system has been developed by using membership functions and rules created based on the existing RULA method. An experiment is carried out in order to study the amount of imprecision in joint angle values from human estimations while observing a posture, and also to compare the sensitivity of RULA and the developed fuzzy RULA system to input imprecision. The results reveal that although the fuzzy RULA model has high correlation with RULA, it is more accurate and less sensitive to the variance in input values. The developed model presents a methodology to improve the accuracy of ergonomic assessment methods and handle the uncertainty inherent in ergonomic evaluation, providing the construction industry practitioners with an automated technique to evaluate the ergonomic safety of workers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".