Application of Virtual Reality to Perform Ergonomic Risk Assessment in Industrialized Construction: Experiment Design
Bibliographic record
Abstract
Workers in the construction manufacturing industry are often exposed to the three primary causes of work-related musculoskeletal disorders (WMSDs): awkward body posture, forceful exertion, and repetitive motion. The investigation of the physical demands of body movement is thus needed during the design of machines and workstations to minimize WMSDs. In the context of the construction industry, this paper proposes the implementation of immersive virtual reality to ergonomically test the design of new workstations/machines with a particular focus on the assessment of human body posture. The application of VR techniques allows the testing of workstations and machines in a virtual environment that imitates their real operational settings while significantly reducing costs and implementation time. To demonstrate the applicability of VR to perform ergonomic risk assessment, a VR experiment is developed, and a pilot test is conducted. Information on body motion is collected and assessed using an existing risk assessment tool, rapid upper limb assessment; as a result, tasks with high ergonomic risks, in terms of awkward body posture, are identified. The obtained body motion data can be also used by engineers, industrial designers, and managers, to support decision making and thus results in improved machinery and workstation design.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".