Bibliographic record
Abstract
Exoskeletons are robotic, brace-like devices that can apply assistive torques to joints, supplementing biological torques. For example, ankle exoskeletons are often designed to assist during the push-off phase of walking to reduce energy expenditure. However, the mechanism by which these exoskeletons reduce metabolic costs is often unclear due to complex human-machine interactions. Here, our purpose was to determine what lower-limb gait kinematic, kinetic, and muscle activity changes underly metabolic cost reductions during ankle exoskeleton-assisted walking. We had healthy participants (n=6) perform two, 12-minute walking trials, one without assistive torques applied (Exo Off) and one with assistive torques applied (Exo On). Participants walked on a force plate instrumented treadmill (Bertec) and were instrumented with optical motion capture (Qualisys) to measure lower-limb gait kinematics and kinetics using Opensim, electromyography (Delsys) to measure the muscle activity of nine lower-limb muscles of the dominant limb, and indirect calorimetry (Cosmed) to measure metabolic energy expenditure (Figure 1A). We aggregated the motion capture, electromyography, and metabolic data for a final analysis using custom scripts (Matlab). We found that metabolic energy expenditure was reduced by 9.52 1.25% (p=0.011; Figure 1B). This, however, did not coincide with any changes in biological ankle angle, torque, or power. However, there was a trending reduction in peak soleus activity (p=0.052). Surprisingly, we did find a reduction in knee power (p=7.3x10-3; Figure 1C) and an accompanying decrease in semitendinosus activity (p=5.9x10-3). Despite the exoskeleton acting at the ankle joint, we found the clearest benefits at the knee joint—highlighting that complex, multi-joint adaptations may underly cost savings. We are continuing data collection to increase our statistical power and better understand the mechanisms that underly exoskeleton assistance.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".