Optimum Push-Off for Uneven Walking Based on the Just-In-Time Strategy: Walking With Interrupted Push-Off Is Mechanically Costly
Bibliographic record
Abstract
We examine the limits of push-off, and we explore when alternate joint actuation might replace it. Using a powered simple walking model (point mass with rigid massless legs), the optimal analytical push-off was derived based on walking speed and step elevation changes. It was observed that higher speeds increased the available push-off to attain greater step-up, e.g., a walker at 1.4 m/s could manage a step-up amplitude of Δh = 0.106 m with a push-off only. Step-up amplitude also required a minimum walking speed for the required push-off. When poststep transition energy compensation was necessary, delayed push-off exerted along the trailing leg led to some wasted work. Walking over smooth surfaces, the delayed push-off cost was up to 7.5 times higher than the optimal push-off, whereas hip-driven compensation was lower at 3.2 times the optimal work. A similar pattern was also observed for step-ups. Our simulation results match young adult walking when terrain view was unrestricted. We utilized available empirical data to quantify the incremental energetic cost of disrupted push-off. It showed that the frequency and poststep transition compensation costs were comparable. As step length further decreased, the cost of poststep transition compensation rose more rapidly, possibly defining a lower limit for step length. The poststep transition compensation rose by 0.18 W/kg/%ΔHz in young adults and 0.2 W/kg/%ΔHz in older adults. Additionally, visual constraints amplified the cost by 0.05 W/kg/%ΔHz.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".