A review of methodologies for integrating human factors and ergonomics in engineering design
Bibliographic record
Abstract
The requirements of Human Factors and Ergonomics (HF/E) in engineering design must be satisfied, including usability, safety, reliability, and operability in the workplace and work environment. This study presents a review of the methodologies for integrating HF/E information in engineering design. The primary purpose of this review is to identify and summarise the current research in this field, thereby giving the recommendations of future research. The focus is on the interaction design between the system (product) and its user (human), including the design of a complex machine, equipment, system, and simple product. Publications in this field between 1982 and 2017 were reviewed from two aspects: (1) the stage of HF/E information integration in engineering design, including conceptual design, embodiment, and detailed design, and (2) the category of the HF/E, including physical ergonomics, cognitive ergonomics, and organisational ergonomics. The benefits and limitations of the reviewed design methodologies were stated in their respective sections. A critical analysis of the research topics from these two aspects was performed with comparison summarising the applicability of these methodologies for researchers and designers. The suggestions for future research were also offered according to the main findings.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".