Optimum Push-off During Uneven Walking for Just-in-Time Strategy; Delayed Push-off Exertion is Mechanically Costly
Bibliographic record
Abstract
Abstract It is shown that step mechanical work roughly describes walking energetics, and optimal walking economy is achieved by pre-emptive step work. We suggest this is also true for uneven walking. Using a simple powered walking model, we estimated the preferred pre-emptive push-offs to cover the entire step energy. The maximum push-off is exerted when the subsequent heel-strike dissipation is zero, setting an upper bound for step-up amplitude achievable with pre-emptive push-off. For instance, at a walking speed of 1.4 m · s −1 , the maximum step-up is 0.106 m. Conversely, for any step-up amplitude, there is a minimum walking speed. For a step-up height (Δh) of 0.06 m, the minimum walking speed is 1.06 m · s −1 . The importance of pre-emptive push-off and optimal timing of push-off and collision is widely discussed. However, there are cases where this timing is undermined, such as during uneven walking, necessitating post-transition mechanical energy compensation. The ankle (via delayed push-off) or hip can provide mid-flight energy, but no mechanical determinant prefers one source over the other. Our modeling demonstrates that delayed push-off entails mechanical energy waste, likely converted to heat by stretching the stance leg. This stretch may also release energy stored during the heel-strike (e.g., in the Achilles tendon), exacerbating the required mechanical work performance in the subsequent step transition. Hence, we propose that during the double support phase, when the stance leg is switched, hip actuation becomes mechanically preferable. Physiological observations also support our proposition.
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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) | 0.001 | 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".