Instrumented Ergonomic Risk Assessment Using Wearable Inertial Measurement Units: Impact of Joint Angle Convention
Bibliographic record
Abstract
The Rapid Upper Limb Assessment (RULA) is frequently used to monitor body posture for early risk prevention of work-related musculoskeletal disorders. However, RULA measurements that are based on workers' self-report or external rater observation suffer from low repeatability. Thus, the objective of this study was to investigate the accuracy and repeatability of an inertial measurement unit (IMU) system for in-field RULA score assessment during manual material handling tasks using 3D Cardan angles and 2D projection angles against reference values obtained by a motion-capture camera system. The experimental results showed that for trunk and neck joint angles, the 2D convention had significantly (p <; 0.05) smaller root-mean-square error (RMSE), while for other upper-body angles, the convention with significantly smaller RMSE depended on the angle under analysis. Also, the 3D convention showed a “moderate” agreement with the reference system, while the 2D convention showed a “substantial” agreement for two tasks and a “moderate” agreement for one task. Moreover, the intraclass correlation coefficients ranged from 0.82 to 0.94 for the 3D convention and 0.87 to 0.95 for the 2D convention for repeated trials performed by each participant. Therefore, the wearable IMU system, along with the 2D convention, could be considered as an accurate and repeatable ergonomic risk assessment tool.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".