Upper arm muscle activity is influenced by both forearm posture and wrist exertion direction during isometric wrist flexion and extension
Bibliographic record
Abstract
The purpose of this study was to determine how wrist exertion direction and forearm posture independently influence upper arm muscle activity during isometric wrist contractions. Surface electromyography was recorded from three muscles of the upper-limb: biceps brachii, triceps brachii, and brachioradialis. Participants were seated with their forearm supported in one of three postures (supinated/neutral/pronated) with an adjustable force transducer that could be placed either above, below, or to the right/left of the participant's hand. Participants performed randomized trials of isometric wrist flexion or extension at five relative intensities: 20, 40, 60, 80, or 100% of maximal force. Trials lasted 4.5 s and both wrist force and electromyography data were assessed. In general, the elbow flexors were more active during wrist flexion, while the triceps were more active in wrist extension, but this pattern reversed in certain forearm postures and wrist exertion directions. Both forearm posture and wrist exertion direction resulted in unique effects on upper arm muscle activity. These findings suggest that muscle activity of the upper arm muscles is influenced independently by both posture and force direction, which should be carefully considered by both motor control specialists and ergonomists.
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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".