Investigating the Muscular and Kinematic Responses to Sudden Wrist Perturbations During a Dynamic Tracking Task
Bibliographic record
Abstract
Sudden disturbances (perturbations) to the hand and wrist are commonplace in daily activities and workplaces when interacting with tools and the environment. It is important to understand how perturbations influence forearm musculature and task performance when identifying injury mechanisms. The purpose of this work was to evaluate changes in forearm muscle activity and co-contraction caused by wrist perturbations during a dynamic wrist tracking task. Surface electromyography was recorded from eight muscles of the upper-limb. Participants performed trials consisting of 17 repetitions of ±40° of wrist flexion/extension using a robotic device. During trials, participants received radial or ulnar perturbations that were delivered during flexion or extension, and with known or unknown timing. Co-contraction ratios for all muscle pairs showed significantly greater extensor activity across all experimental conditions. Of all antagonistic muscle pairs, the flexor carpi radialis (FCR)-extensor carpi radialis (ECR) muscle pair had the greatest change in co-contraction, producing 1602% greater co-contraction during flexion trials than during extensions trials. Expected perturbations produced greater anticipatory (immediately prior to the perturbation) muscle activity than unexpected, resulting in a 30% decrease in wrist displacement. While improving performance, this increase in anticipatory muscle activity may leave muscles susceptible to early-onset fatigue, which could lead to chronic overuse injuries in the workplace.
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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".