A Modified Approach to Define Walking Center of Mass Mechanical Energy Recovery: Human Walking Involves Energy Loss Throughout Stance
Bibliographic record
Abstract
Abstract An exchange between potential and kinetic energy over the step has long been considered a key feature in the energetic effectiveness of human walking. However, it is difficult to identify mechanisms responsible for limiting such an exchange in human walking. This study proposes a modified definition of center-of-mass (COM) energy recovery ( R c ) that quantifies the proportion of mechanical energy transferred from one step to the next while accounting for total step dissipation. Simulations show that R c decreases nearly linearly with walking speed on level ground, indicating no preferred speed. This behavior arises from analytical formulations that neglect active work during single support (pendular motion). In contrast, empirical data reveal consistently lower R c , likely due to elevated collision losses or negative net single-support work not captured by the analytical model. When both single- and double-support phases are considered analytically, R c exhibits a maximum of 59.4% at 1.21 m.s −1 , coinciding with minimal active muscle work over the step. We further show that the R c trajectory is asymmetric, contrary to prior assumptions, and is governed by total step dissipation. Accordingly, challenging walking conditions associated with higher metabolic cost, such as restricted visual lookahead, are predicted to reduce R c (maximum 58.5%).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".