Reduction of pendular energy exchange at very slow human walking speeds reveals deviations from simple walking models
Bibliographic record
Abstract
Walking can be modelled as a simple inverted pendulum, where the human body takes advantage of exchanging kinetic and gravitational potential energy to remain in motion. This exchange is well documented at normal speeds but could break down at very slow speeds. We examined energy transduction through the phase shift between potential and kinetic energy in human walking at a wide range of speeds (0.1-2.0 m s-1), specifically capturing very slow speeds (<0.6 m s-1). We measured phase shift with two different methods. One method used two classical phase shifts of α and β from ( Cavagna and Legramandi, 2020). The second method utilized cross-correlation across each stride. We calculated phase shift from a human gait study and from two simple inverted pendulum-type walking models ( Srinivasan and Ruina, 2006; Rebula and Kuo, 2015). Participants walked at 13 prescribed speeds between 0.1 and 2.0 m s-1 on a split belt instrumented treadmill. We found that phase shifts increased as speed decreased. However, at speeds slower than 0.3 m s-1, the phase shifts approached zero. The simple walking models were unable to demonstrate phase shift behaviour at any speed. Our study demonstrates that as speed slows, humans walk less similarly to the inverted pendulum models, and more complex models may be required to characterize walking at very slow speeds.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".