Shoulder loading while performing automotive parts assembly tasks: A field study
Bibliographic record
Abstract
The purpose of this study was to complete in depth task analyses for a series of automobile parts assembly jobs and quantify the range of mechanical shoulder loading sustained by the workers. Nine jobs were selected from within an automobile parts assembly plant and 26 participants (12 males, 14 females) were filmed while they performed regular assembly line duties. Workers spent the majority of their time in neutral shoulder postures, and about 1/3 of the shift in mild shoulder flexion or abduction. Cumulative shoulder flexion moments ranged from 76–160 kNm*s while cumulative shoulder abduction moments ranged from 42–119 kNm*s. Peak shoulder flexion moments ranged from 26–124 Nm and peak shoulder abduction moments ranged from 30–93 Nm. The analysis revealed a wide range of shoulder loading between jobs, and workers completing the same job. This study demonstrates the importance of measuring a variety of postural and mechanical demands for every job to adequately address all aspects of the work that might influence the development of injury. This is the first study to document entire shift cumulative shoulder moments during automotive parts assembly work. This quantitative assessment provides insight into the range of loading that workers are currently experiencing, and demonstrates the variability between workers completing the same task.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".