Exploring the Change in Metabolic Cost of Walking before and after Familiarization with a Passive Load-Bearing Exoskeleton: A Case Series
Bibliographic record
Abstract
OCCUPATIONAL APPLICATIONSMilitary personnel are at greater risk of injuries due to frequent load carriage. Novel exoskeleton technology may have benefits for soldiers, such as reduced physical burden through load carriage support that may result in decreased metabolic cost, reduced fatigue, and lower risk of injuries during walking. However, as for most assistive devices, a familiarization period is likely necessary to obtain the full potential of the device. Our results show that the metabolic cost of walking (MWC) was initially increased significantly upon provision of the passive exoskeleton, though it returned to baseline values after a 9-day familiarization period. The exoskeleton remained effective after a three-month pause, with a MCW below baseline. These results suggest that to properly assess the assistance of an exoskeleton, a sufficient familiarization period should be mandatory.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".