Dynamic Biomechanical Analysis for Construction Tasks Using Motion Data from Vision-Based Motion Capture Approaches
Bibliographic record
Abstract
In the labor-intensive construction industry, workers are frequently exposed to manual handling tasks involving forceful exertions and awkward postures. As a result, construction workers are at about a 16 percent higher risk of work-related musculoskeletal disorders (WMSDs) than workers in other industries. A biomechanical model-based musculoskeletal stress analysis is one of the widely used methods to identify the risk of WMSDs during occupational tasks. However, the use of biomechanical analysis has been limited to only laboratory experiments due to the difficulty of collecting motion data required for biomechanical models under real conditions. To reflect postural variations when performing construction tasks, an effective and easily accessible mean that enables us to conduct biomechanical analysis under real conditions is required. To address this issue, we propose a motion-data-driven biomechanical analysis by enabling automatic processes to convert motion data from vision-based motion capture into available data for representing motions in biomechanical analysis tools. We conduct a case study on masonry work to determine the feasibility of the proposed method. The results show that the proposed approach has the potential to assess an individual's motions and to provide personalized feedback for the purpose of reducing biomechanical loads and WMSD risk in real workplaces.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".