VR–MOCAP-Enabled Ergonomic Risk Assessment of Workstation Prototypes in Offsite Construction
Bibliographic record
Abstract
Workers in offsite construction facilities are often exposed to repetitive motion and awkward body postures that are associated with the risk of developing work-related musculoskeletal disorders despite the use of automated equipment on production lines. To reduce the exposure to these risks, an investigation of the physical demands that workstations impose on workers’ bodies is needed. Since traditional methods used to collect human body motions have limitations, such as workplace interruptions and biased results due to subjective observation, this paper proposes a virtual reality (VR)–motion capture (MOCAP)-based ergonomic assessment method to evaluate ergonomic risks in a laboratory setting during the design phase of workstation development. It is expected that the number of iterations of physical workstation prototypes would be reduced if ergonomic risk ratings are identified proactively in the initial phases of workstation design, which would thereby reduce the cost and time required to develop and implement an improved workstation design. The present study includes a feasibility analysis of the proposed method in which participants representative of specific percentiles of the population based on their physical stature were invited to voluntarily participate in a research experiment. The results obtained demonstrate that the proposed method can successfully simulate the elemental motions, referred to as therbligs, of reaching and positioning (Pearson’s correlation coefficient is found to equal 0.80 and 0.94, respectively), while the simulation of the assembling therblig requires further investigation. The contribution of this study is a virtual reality–motion captured-enabled ergonomic risk assessment method applied to workstation design for offsite construction production lines. In addition, the deployment of the proposed method allows a holistic ergonomic assessment that considers objective and subjective parameters.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".