Multidirectional manual arm strength and its relationship with resultant shoulder moment and arm posture
Bibliographic record
Abstract
Previous work has quantified manual force capabilities for ergonomics design, but the number of studies and range of conditions tested are limited in scope. Therefore, the aims of this study were to collect seated manual arm strength (MAS) data from 24 females in several unique exertion directions (n = 26) and hand locations relative to the shoulder (n = 8), and to investigate the associations between MAS and shoulder/elbow moments. MAS was generally highest when the direction of force application was oriented parallel to the vector from the shoulder to knuckle, and weakest when oriented orthogonal to that vector. Moderate correlations were found between MAS and: (1) resultant shoulder moment (r = 0.34), (2) resultant moment arms (r = -0.545) and (3) elbow flexion/extension moment (r = 0.481). Our strength data will be used in the development of a comprehensive MAS predictive method, so that strength capabilities can be predicted to help design acceptable tasks in the workplace. Practitioner Summary: This study sought to enhance our understanding of one-handed manual arm strength capabilities for ergonomics task evaluations. Our findings provide researchers and practitioners with manual strength data for off-axis force directions, as well as hand locations not previously measured. These data will contribute to future methods for predicting strength capabilities.
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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 | 0.003 |
| 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) | 0.003 | 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".