Behavioural energetics in human locomotion: how energy use influences how we move
Bibliographic record
Abstract
Nearly a century of research has shown that humans, and other animals, tend to move in ways that minimize energy use. A growing body of evidence suggests that energetic cost is not only an outcome of our movement, but also plays a central role in continuously shaping it. This has led to an emerging research area, at the nexus between biomechanics and neuroscience, termed behavioural energetics, which is focused on understanding the mechanisms of energy optimization and how this shapes our coordination and behaviour. In this Review, we first summarize the existing evidence for and against our preferred locomotor behaviours coinciding with energy optima. Although evidence of our preference for energetically optimal gaits has existed for decades, new research is revealing its relevance across a surprising array of dynamic locomotor tasks and complex environments. We next discuss evidence that we adapt our gait toward energy optima over short timescales and in novel environments, which we view as a more stringent test that energy expenditure is optimized in real-time. This necessitates that we sense energy use, or proxies for it, on similar timescales. We therefore next provide an overview of candidate sensory mechanisms of energy expenditure. Finally, we discuss how behavioural energetics can be applied to novel wearable assistive technologies and rehabilitation paradigms, and conclude the Review by outlining what we see as the most important future challenges and opportunities in behavioural energetics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".