Validating Accelerometer-Based Inclinometer Models for Estimating Overhead Postures in Construction Workers: Considerations for In-Field Application
Bibliographic record
Abstract
OCCUPATIONAL APPLICATIONSAmong the three commonly used mathematical models for estimating overhead position from inclination data, the trigonometry model showed the most consistent alignment with in-lab motion capture data, with no statistically significant differences observed. Strong correlations were found between accelerometer-based wearable and inclination outputs during upright drilling and hammering tasks involving shoulder elevation above 150°. While no statistically significant differences were observed between observational and inclination-based estimates of overhead frequency, discrepancies were present in overhead duration estimates. These findings suggest that, with appropriate signal processing and modeling techniques, inclination data collected at the upper arm may offer a practical approach for estimating overhead exposures in field research. This method could support more refined monitoring and assessment of overhead work, potentially informing interventions aimed at reducing musculoskeletal strain and improving worker safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".