Reassessing the Role of Foot Power in Human Gait
Bibliographic record
Abstract
Abstract The foot acts as the primary interface to the ground during bipedal locomotion. It absorbs and returns energy over stance as the longitudinal arch deforms and recoils. The term ‘arch recoil’ evokes the concept that the foot’s returned energy directly propels the centre of mass forward by lifting the talus. However, recent work has shown that arch recoil does not directly drive the body forward; instead, it lowers and posteriorly tilts the talus, putting it into a more favourable position for upright gait. Here, we aim to supply a kinetic explanation for this mechanism. We applied the unified deformable power approach to highly accurate talus kinematics from biplanar videoradiography and force plate measurements to measure the power absorbed/produced by the foot. We coupled these measurements with a simple mathematical model that allowed us to restrict rotation and linear actuation of the talus caused by the recoil of the arch to demonstrate that positive foot power primarily contributes to posteriorly tilting the talus. This suggests the role of positive foot power during propulsion is to keep the talocrural surface in a more favourable position for upright gait rather than directly propelling the centre of mass forwards. These findings highlight that arch mobility during push-off is critical for allowing the ankle to directly propel the body forward and upward during the propulsive phase of gait.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".