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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".