Lower-limb joint mechanics during maximum acceleration sprinting
Bibliographic record
Abstract
We explored how humans adjust the stance phase mechanical function of their major lower-limb joints (hip, knee, ankle) during maximum acceleration sprinting. Experimental data (motion capture and ground reaction force (GRF)) were recorded from eight participants as they performed overground sprinting trials. Six alternative starting locations were used to obtain a dataset that incorporated the majority of the acceleration phase. Experimental data were combined with an inverse-dynamics-based analysis to calculate lower-limb joint mechanical variables. As forward acceleration magnitude decreased, the vertical GRF impulse remained nearly unchanged whereas the net horizontal GRF impulse became smaller due to less propulsion and more braking. Mechanical function was adjusted at all three joints, although more dramatic changes were observed at the hip and ankle. The impulse from the ankle plantar-flexor moment was almost always larger than those from the hip and knee extensor moments. Forward acceleration magnitude was linearly related to the impulses from the hip extensor moment (R2=0.45) and the ankle plantar-flexor moment (R2=0.47). Forward acceleration magnitude was also linearly related to the net work done at all three joints, with the ankle displaying the strongest relationship (R2=0.64). The ankle produced the largest amount of positive work (1.55±0.17 J/kg) of all the joints, and provided a significantly greater proportion of the summed amount of lower-limb positive work as running speed increased and forward acceleration magnitude decreased. We conclude that the hip and especially the ankle represent key sources of positive work during the stance phase of maximum acceleration sprinting.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".