The Spatial Dependency of Shoulder Muscle Demands for Seated Lateral Hand Force Exertions
Bibliographic record
Abstract
As the modern workplace is dominated by submaximal repetitive tasks, knowledge of the effect of task location is important to ensure workers are unexposed to potentially injurious demands imposed by repetitive work in awkward or sustained postures. The purpose of this investigation was to develop a three-dimensional spatial map of the muscle activity for the right upper extremity during laterally directed submaximal force exertions. Electromyographic (EMG) activity was recorded from fourteen muscles surrounding the shoulder complex as the participants exerted 40N of force in two directions (leftward, rightward) at 70 defined locations. Hand position in both push directions strongly influenced total and certain individual muscle demands as identified by repeated measures analysis of variance (P < .001). During rightward exertions individual muscle activation varied from 1 to 21% MVE and during leftward exertions it varied from 1 to 27% MVE with hand location. Continuous prediction equations for muscular demands based on three-dimensional spatial parameters were created with explained variance ranging from 25 to 73%. The study provides novel information for evaluating existing and proactive workplace designs, and may help identify preferred geometric placements of lateral exertions in occupational settings to lower muscular demands, potentially mitigating fatigue and associated musculoskeletal risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".