The Energetics of Maintaining the Lateral Balance Are Terrain-Specific; a Normal Lookahead Significantly Reduces Active Balance Maintenance Work
Bibliographic record
Abstract
Abstract Humans must actively control their lateral balance through frontal plane work or adjusting lateral foot placement. With constant muscle efficiency and considering the energetic consequences of Center of Mass (COM) work variability, we estimated the metabolic cost of lateral balance maintenance and compared it with the Workman model. Like the Workman model, we found that lateral balance energetics were mainly associated with terrain amplitude. Increased walking speed’s effect on step transition work might be offset by reduced step width. A significant rise in lateral work magnitude (+157.1%) with restricted lookahead was potentially linked to wider steps. Comparing mechanical work with the Workman model, we found significant differences in magnitudes, suggesting that the Workman model included additional costs such as force rate generation, muscle coactivation, or posture maintenance not reflected in the COM lateral work and its variability. Practitioner Summary Lateral balance maintenance during walking requires active regulation, yet it is not studied mechanically. Our study found that lateral control costs are terrain-specific and increase with restricted lookahead. With age, only variability increases. We assume associated energetics rise with both work and variability.
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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 | 0.000 | 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) | 0.004 | 0.001 |
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".