Neuromuscular fatigue reduces responsiveness when controlling leg external forces
Bibliographic record
Abstract
Abstract In legged movement, our legs push against the ground, generating external force vectors that enable agile movements. Neuromuscular fatigue can reduce agility by causing physiological changes, such as slowing muscle reaction time, altering proprioception, and delaying neuromuscular control. Fatigue may deteriorate the nervous system’s control of leg external forces, contributing to reductions in agility. In this study, we investigated the effect of fatigue on the performance of the nervous system in controlling the vertical component of leg external force ground reaction forces. We hypothesized that increased leg fatigue would lead to declines in both the responsiveness (speed) and accuracy of leg force control. To test this hypothesis, we used an apparatus that allowed participants to exert controlled vertical forces with one leg against a force plate while immobilizing the rest of their bodies. Participants adjusted their leg external force to match step targets displayed on a screen. We induced fatigue by having participants maintain submaximal leg forces, and we measured leg force control performance between fatigue trials. Results showed a significant 26% reduction in mean maximum force production, leading to a substantial decline in leg force control responsiveness, as evidenced by a 23% increase in rise time and a 25% narrowing of bandwidth. However, fatigue did not significantly reduce leg force control accuracy. Understanding the effects of fatigue on leg force control can inform the development of strategies and technologies to sustain agile performance, even in the presence of fatigue. New and Noteworthy We developed a method to probe the influence of neuromuscular fatigue on the control of leg external forces. Our findings demonstrate that while fatigue significantly diminishes responsiveness (speed), it does not compromise the accuracy of control. These insights enhance our understanding of legged agility and could guide the development of strategies for optimizing leg force control performance. This study paves the way for future research aimed at identifying and employing effective strategies to maintain agility in the face of fatigue.
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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) | 0.002 | 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".