Dynamic Wrist Flexion and Extension Fatigue Induced via Submaximal Contractions Similarly Impairs Hand Tracking Accuracy in Young Adult Males and Females
Bibliographic record
Abstract
We evaluated the effects of muscle fatigue on hand-tracking performance in young adults. Differences were quantified between wrist flexion and extension fatigability, and between males and females. Participants were evaluated on their ability to trace a pattern using a 3-degrees-of-freedom robotic manipulandum before (baseline) and after (0, 1, 2, 4, 6, 8 and 10 mins) a submaximal-intensity fatigue protocol performed to exhaustion that isolated the wrist flexors or extensors on separate days. Tracking tasks were performed at all time points, while maximal voluntary contractions (MVCs) were performed at baseline, and 2, 6- and 10-mins post-task termination. We evaluated movement smoothness (jerk ratio, JR), shape reproduction (figural error, FE), and target tracking accuracy (tracking error, TE). MVC force was significantly lower in females (p<0.05), lower than baseline for all timepoints after task termination (p<0.05), with no muscle group-dependent differences. JR did not return to baseline until 10-mins post-task termination (most affected), while FE returned at 4-mins post-task termination, and TE at 1-min post-task termination. Males tracked the target with significantly lower JR (p<0.05), less TE (p<0.05), and less FE (p<0.05) than females. No muscle group-dependent changes in hand-tracking performance were observed. Based on this work, hand tracking accuracy is similarly impaired following repetitive submaximal dynamic wrist flexion or extension. The differences between male and female fatigability was independent of the changes in our tracking metrics.
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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 | 0.000 |
| 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) | 0.003 | 0.001 |
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".