Forearm posture and grip effects during push and pull tasks
Bibliographic record
Abstract
Direction of loading and performance of multiple tasks have been shown to elevate muscle activity in the upper extremity. The purpose of this study was to evaluate the effects of gripping on muscle activity and applied force during pushing and pulling tasks with three forearm postures. Twelve volunteers performed five hand-based tasks in supinated, neutral and pronated forearm postures with the elbow at 90 degrees and upper arm vertical. All tasks were performed with the right (dominant) hand and included hand grip alone, push and pull with and without hand grip. Surface EMG from eight upper extremity muscles, hand grip force, tri-axial push and pull forces and wrist angles were recorded during the 10 s trials. The addition of a pull force to hand grip elevated activity in all forearm muscles (all p < 0.017). During all push with grip tasks, forearm extensor muscle activity tended to increase when compared with grip only while flexor activity tended to decrease. Forearm extensor muscle activity was higher with the forearm pronated compared with neutral and supinated postures during most isolated grip tasks and push or pull with grip tasks (all p < 0.017). When the grip dynamometer was rotated so that the push and pull forces could act to assist in creating grip force, forearm muscle activity generally decreased. These results provide strategies for reducing forearm muscle loading in the workplace. STATEMENT OF RELEVANCE: Tools and tasks designed to take advantage of coupling grip with push or pull actions may be beneficial in reducing stress and injury in the muscles of the forearm. These factors should be considered in assessing the workplace in terms of acute and cumulative loading.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.002 | 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".