A systematic approach to simulating field-based occupational whole-body vibration exposure in the lab using a 6df robot
Bibliographic record
Abstract
BACKGROUND: Whole-body vibration is a significant workplace risk factor for discomfort and injury in many work sectors. The current approach for evaluating vibration exposures typically involves field studies of seatpan acceleration while the operators perform typical workplace activities. These vibration exposures are then compared to international standards to evaluate the risk of discomfort or injury. This approach does not enable systematic and controlled study of specific workplace factors such as the effect of seating, and it is difficult and expensive to perform. APPROACH TO PAPER: We have developed a systematic approach for studying whole-body vibration in the laboratory setting. This approach involves field studies of occupational exposures measuring the 6 degree of freedom chassis accelerations (translational and rotational) and replication of these exposures in the laboratory. FINDINGS: To date, as a research team, we have collected chassis vibration data from specific vehicles in the forestry (skidders), mining (load-haul-dump vehicles), and construction (scrapers) sectors. We have processed these exposures to develop a library of representative vibration motions, and have replicated these motions in the laboratory using a robotic platform. CONCLUSIONS: This systematic approach of combining field- and laboratory-based measures has facilitated research into specific relevant questions such as the effects of multi axis vibrations on the physical risks to operator health and direct evaluation of the vibration attenuation properties of industrial seats.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".