The effect of localized muscle fatigue on tibial impact acceleration.
Bibliographic record
Abstract
Tibial impact acceleration measured during running is known to increase with general body fatigue. The purpose of this study was to determine if localized muscle fatigue of the shank muscles would also cause an increase in peak tibial acceleration. The human pendulum system was used to control impact velocity and joint angle. There were 24 women participating in the study: 12 between the ages of 20--25, and 12 between 50--60 years. Each lay supine on the pendulum, and their unshod dominant heel was impacted into a vertical force plate with a velocity between 1--1.15 m/s, and a force approximating 1.8--2.8 x body weight. A uni-axial accelerometer at the tibial tubercle measured peak tibial acceleration, time to peak acceleration and the slope of the acceleration/time curve. EMG activity of the tibialis anterior and gastrocnemius was used to define and monitor fatigue. The dorsiflexors and plantarflexors were fatigued on two separate days, at least a week apart. Statistical analysis revealed a significant decrease in peak acceleration and acceleration slope following fatigue. There were no significant main effects or interactions for age group or muscle group. In conclusion, localized muscle fatigue of the dorsiflexors or plantarflexors resulted in a significant decrease in the peak acceleration and acceleration slope measured at the knee, which is opposite to the effect of general body fatigue. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2003 .F59. Source: Masters Abstracts International, Volume: 42-03, page: 0986. Adviser: D. Andrews. Thesis (M.H.K.)--University of Windsor (Canada), 2003.
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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) | 0.003 | 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".