Virtual Human Factors Tools for Proactive Ergonomics: Qualitative Exploration And Method Development
Bibliographic record
Abstract
<p>This thesis presents two studies that explore the use of Virtual Human Factors Tools (VHFT), such as Predetermined Motion Time Systems (PMTS), Digital Human Models (DHM) and Discrete Event Simulation (DES). Study 1 investigated the needs and expectations of ergonomist and engineers through a series of explorative workshops. Nine characteristics of concern to participants were identified in the tools: time, cost, training, difficulty to use, trustworthiness, graphics, flexibility, usefulness and report presentation. These characteristics can influence VHFT uptake and application decisions. Study 2 explored the integration of DES, PMTS, DHM and existing fatigue models, in an assembly context, to predict the accumulation of muscular fatigue. This study demonstrated the feasibility of using VHFT in conjunction and that DES can predict ergonomic outcomes such as work-related fatigue and recovery. The study also uncovered some problems with the existing fatigue models, highlighting the need for further research and development of those models.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".