Dynamic Biomechanical Simulation for Identifying Risk Factors for Work-Related Musculoskeletal Disorders During Construction Tasks
Bibliographic record
Abstract
Dynamic Biomechanical Simulation for Identifying Risk Factors for Work-Related Musculoskeletal Disorders During Construction Tasks J. Seo, S. Lee, T. J. Armstrong, S. Han Pages 1074-1084 (2013 Proceedings of the 30th ISARC, Montréal, Canada, ISBN 978-1-62993-294-1, ISSN 2413-5844) Abstract: We propose a dynamic biomechanical simulation method that uses motion capture to evaluate the risk of Work-related Musculoskeletal Disorders (WMSDs). Statistics show that WMSDs accounted for 33% of all non-fatal occupational injuries and illness in construction in 2009, and were a leading cause of temporary and permanent disability. Present methods rely largely on self-reports from workers, observational techniques, and direct measurements of motion and muscle activity to assess awkward postures, physical loads, repetitiveness, and the duration of exposure. While these methods have helped to prevent WMSDs in construction work, they may not be suitable for estimating the internal tissue loads associated with WMSDs. We propose a dynamic biomechanical simulation method to estimate internal forces and moments at each body joint of construction workers with motion capture data. Particularly, we explore the biomechanical loads by simulating active 3D musculoskeletal models based on measured postures and movements. To demonstrate the feasibility of this approach, we studied a ladder climbing task using a portable ladder under controlled laboratory conditions. Postures and motions were determined with a commercial motion capture system (e.g., VICON). The results were analyzed to investigate the feasibility of identifying risk factors based on biomechanical simulation. The results show that the proposed approach allows us to determine the biomechanical basis for WMSDs, and to identify postures and movements associated with excessive physical demands on each body joint. When combined with marker-less motion capture which is our ongoing work, 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. Keywords: Work-related musculoskeletal disorders, Biomechanical model, Motion capture DOI: https://doi.org/10.22260/ISARC2013/0118 Download fulltext Download BibTex Download Endnote (RIS) TeX Import to Mendeley
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".