Taking advantage of external mechanical work to reduce metabolic cost: the mechanics and energetics of split-belt treadmill walking
Bibliographic record
Abstract
In everyday tasks such as walking and running, we exploit the work performed by external sources such as gravity to reduce the work performed by muscles. There has been considerable recent effort to design devices capable of performing mechanical work to improve walking function or reduce effort. The success of these devices relies on the user adapting their natural control strategies to take advantage of assistance provided by the device. Although locomotor adaptation is central to this process, the study of adaptation is often done using approaches that on the surface, seem to have little in common with the use of external assistance. Here, we show that one of the most common approaches for studying this process, which is adaptation to walking on a split-belt treadmill, can be understood from a perspective in which people learn to take advantage of mechanical work performed by the treadmill. During adaptation, people systematically adjust their step lengths, defined as the distance between the feet at heel strike, from one step to the next. Initially, the step length on the slow belt is longer than the step length on the fast belt, measured as a negative step length asymmetry, but people naturally reduce this asymmetry with practice. Here, we demonstrate that these modifications of step length asymmetry allow people to extract positive work from the treadmill belts to reduce the positive work performed by the legs and simultaneously reduce metabolic cost. Moreover, we show that walking with a positive step length asymmetry minimizes metabolic cost, and people prefer to walk in this manner when allowed to select their walking pattern. Together, our results suggest that split-belt adaptation can be interpreted as a process by which people learn to take advantage of mechanical work performed by an external device to improve walking economy.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".