Effects of Fatigue on Intermuscular Coordination during Repetitive Hammering
Bibliographic record
Abstract
Fatigue affects the capacity of muscles to generate forces and is associated with characteristic changes in EMG signals. It may also influence interjoint and intermuscular coordination. To understand better the global effects of fatigue on multijoint movement, we studied movement kinematics and EMG changes in healthy volunteers asked to hammer repetitively. Movement kinematics and the activity of 20 muscles of the arm, trunk, and leg were recorded before and after subjects became fatigued (as measured using a Borg scale). When fatigue was reached, maximal grip strength and elbow range of motion decreased while the EMG amplitude of the contralateral external oblique muscle was increased. Fatigue did not affect shoulder and wrist kinematics or movement frequency. Results suggest that fatigue influences motion at both local and global levels. Specifically, interjoint and intermuscular coordination adapt to compensate for local effects of fatigue and to maintain key movement characteristics, such as the trajectory of the end effector and the movement frequency. Nonlocal compensations may be a focus of future studies of how fatigue affects complex movements such as those typically performed 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".