Mechanics and energetics of swinging the human leg
Bibliographic record
Abstract
We measured how much metabolic energy is expended to swing a human leg. A previous dynamical model of walking predicted that increasing metabolic costs for walking with step length and step frequency trade-off against each other to determine the optimum step combination at a given speed. Simple pendulum dynamics indicate that the cost of walking at high step frequencies could be associated with driving the legs back and forth relative to the body, at a rate increasing approximately with the fourth power of frequency, possibly due to the low economy of producing muscle force for short durations. A similar cost would be expected for isolated swinging of a leg at faster than its natural frequency. We constructed an apparatus to measure work performed on the leg, and measured metabolic cost as human subjects (N=12) swung one leg at frequencies 0.5-1.1 Hz and fixed amplitude. Rate of mechanical work ranged from 0.02-0.27 W kg(-1) over these frequencies. Net metabolic rate for leg swinging (subtracting that for quiet standing) increased from 0.41-2.10 W kg(-1), approximately with the fourth power of frequency (R(2)=0.92) and in proportion to a hypothesized cost of force production for short durations. The costs of producing force and work could account for the increase. In a crude comparison, moving the legs back and forth at a typical stride frequency of 0.9 Hz, might consume about one-third of the net energy (2.8+/-0.8 W kg(-1)) needed for walking at 1.3 m s(-1).
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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 | 0.000 |
| 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".