Exploring the Influence of Structured Familiarization to an Adjustable, Passive Load-Bearing Exoskeleton on Oxygen Consumption and Lower Limb Muscle Activation During Walking
Bibliographic record
Abstract
Walking patterns are modified during load carriage, resulting in an increased activation of lower limb muscles and energy expenditure. Negative effects of load carriage could be minimized by wearing an exoskeleton, but evidence on the effects are conflicting. The objectives of this study were to describe the influence of an adjustable, passive load-bearing exoskeleton on the metabolic cost of walking (MCW) and associated muscle activations, and to explore changes in MCW after a familiarization process. Thirteen participants walked on a treadmill with a 22.75 kg payload at six preselected speeds (from 0.67 to 1.56 m/s) under three walking conditions: 1) without exoskeleton (NoExo); 2) with exoskeleton before familiarization (ExoPre); and 3) with exoskeleton after familiarization (ExoPost). Metabolic data was normalized to walking speed to provide MCW. Multi-muscle surface electromyography (EMG) was time and amplitude normalized to the gait cycle to provide muscle activation patterns. The familiarization occurred over three weeks including exposure to the exoskeleton. Differences in MCW and muscle activations were compared using a nonparametric analysis of longitudinal data. There were statistically significant increases in MCW for all speeds in the ExoPre and ExoPost conditions compared the NoExo. The average muscle activation showed an increase during ExoPre and ExoPost for the three speeds evaluated. Post-hoc analysis showed no significant effect of the familiarization period on metabolic data. In conclusion, a first exposure to the adjustable exoskeleton increased MCW and muscle activations, but the familiarization process did not provide any benefits toward a reduction in MCW or reduction in muscle activations at all speeds evaluated.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".