Integrating muscle energetics into biomechanical models to understand variance in the cost of movement
Bibliographic record
Abstract
In this Review, we explore the state of the art of biomechanical models for estimating energy consumption during terrestrial locomotion. We consider different mechanical models that provide a solid framework to understand movement energetics from the perspective of force and work requirements. Whilst such models are highly informative, they lack specificity for predicting absolute metabolic rates across a range of species or variations in movement patterns. Muscles consume energy when they activate to generate tension, as well as when they shorten to generate positive work. Phenomenological muscle models incorporating steady-state parameters have been developed and are able to reproduce how muscle fibre energy consumption changes under different contractile conditions; however, such models are difficult to validate when scaled up to whole muscle. This is, in part, owing to limited availability of data that relate muscle dynamics to energetic rates during contraction of large mammalian muscles. Furthermore, factors including the compliance of tendinous tissue, dynamic shape changes and motor unit recruitment can alter the dynamics of muscle contractile tissue and potentially improve muscle efficiency under some locomotion conditions. Despite the many challenges, energetic cost estimates derived from musculoskeletal models that simulate muscle function required to generate movement have been shown to reasonably predict changes in human metabolic rates under different movement conditions. However, accurate predictions of absolute metabolic rate are still elusive. We suggest that conceptual models may be adapted based on our understanding of muscle energetics to better predict the variance in movement energetics both within and between terrestrial species.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".