Sensitivity of Internal Tibial Forces and Moments to Static Optimization Moment Constraints at the Subtalar and Ankle Joints
Bibliographic record
Abstract
We examined the sensitivity of internal tibial forces and moments during running to different subtalar/ankle moment constraints in a static optimization routine. Seventeen participants ran at 2.20, 3.33, and 4.17 ms-1 while force and motion data were collected. Ankle joint contact force was estimated using inverse-dynamics-based static optimization. Three sets of joint moment constraints were tested. All sets included the flexion-extension and abduction-adduction moments at the hip and the flexion-extension moment at the knee but differed in the constraints used at the subtalar/ankle: (1) flexion-extension at the ankle (Sag), (2) flexion-extension and inversion-eversion at ankle (Sag + Front), and (3) flexion-extension at the ankle and supination-pronation at the subtalar (Sag + SubT). Internal tibial forces and moments were quantified at the distal one-third of the tibia, by ensuring static equilibrium with applied forces and moments. No interaction was observed between running speed and constraint for internal tibial forces or moments. Sag + SubT resulted in larger internal mediolateral force (+41%), frontal (+79%), and transverse (+29%) plane moments, compared to Sag and Sag + Front. Internal axial force was greatest in Sag + Front, compared to Sag and Sag + SubT (+37%). Faster running speeds resulted in greater internal tibial forces and moments in all directions (≥+6%). Internal tibial forces and moments at the distal one-third of the tibia were sensitive to the subtalar and ankle joint moment constraints used in the static optimization routine, independent of running speed.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".