The Effect of Leg Muscle Activation State and Localized Muscle Fatigue on Tibial Response during Impact
Bibliographic record
Abstract
The purpose of this research was to examine the effects of voluntarily manipulating muscle activation and localized muscle fatigue on tibial response parameters, including peak tibial acceleration, time to peak tibial acceleration, and the acceleration slope, measured at the knee during unshod heel impacts. A human pendulum delivered consistent impacts to 15 female and 15 male subjects. The tibialis anterior and lateral gastrocnemius were examined using electromyography, thus allowing voluntary contraction to various activation states (baseline, 15%, 30%, 45%, and 60% of the maximum activation state) and assessing localized muscle fatigue. A skin-mounted uniaxial accelerometer, preloaded medial to the tibial tuberosity, allowed tibial response parameter determination. There were significant decreases in peak acceleration during tibialis anterior fatigue, compared to baseline and all other activation states. In females, increased time to peak acceleration and decreased acceleration slope occurred during fatigue compared to 30% and 45%, and compared to 15% through 60% of the maximum activation state, respectively. Slight peak acceleration and acceleration slope increases, and decreased time to peak acceleration as activation state increased during tibialis anterior testing, were noted. When examining the lateral gastrocnemius, the time to peak acceleration was significantly higher across gender in the middle activation states than at the baseline and fatigue states. The acceleration slope decreased at all activation states above baseline in females, and decreased at 60% of the maximum activation state in males compared to the baseline and fatigue states. Findings agree with localized muscle fatigue literature, suggesting that with fatigue there is decreased impact transmission, which may protect the leg. The relative effects of leg stiffness and ankle angle on tibial response need to be verified.
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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".