Feasibility of Onsite Biomechanical Analysis during Ladder Climbing
Bibliographic record
Abstract
Injuries from falls and overexertion during ladder climbing activities are common in construction. To prevent ladder-related injuries, it is important to understand why those injuries occur. Thus, there has been significant research effort put into identifying mechanisms and causes of falls and work-related musculoskeletal disorders (WMSDs) from ladder climbing. These include epidemiological studies, studies on the mechanical aspects of ladder-related injuries, and biomechanical studies. Biomechanical analysis during ladder climbing has been implemented widely to understand the fundamental causes of ladder-related injuries in terms of musculoskeletal stresses on the human body. However, previous experimental approaches that use marker-based or IMU (Inertial Measurement Unit)-based motion capture and force transducers to collect motion and force data for biomechanical analysis are limited because of the difficulty in mimicking all of the possible situations that can happen during ladder climbing on actual worksites. To address this issue, we propose onsite biomechanical analysis for ladder-climbing activities by combining vision-based motion capture systems and force prediction models. To test the feasibility of the proposed method, we conducted a case study. As a result, we found that the method has true potential to broaden our understanding of the causes of falls from ladders and of WMSDs by estimating musculoskeletal stresses on the human body during ladder climbing without using any invasive measures.
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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 | 0.000 |
| 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 | 0.001 |
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".