Coordinated Human-Exoskeleton Locomotion Emerges from Regulating Virtual Energy
Bibliographic record
Abstract
Abstract Lower-limb exoskeletons showed great potential for the gait rehabilitation of individuals with motor impairments; however, maintaining human-exoskeleton coordination is still a challenge. Human-exoskeleton coordination problem, referred to as any mismatch or asynchrony between the user’s intended trajectories and exoskeleton desired trajectories, results in sub-optimal gait performance, particularly for the individuals with residual motor ability. Here, we investigate the virtual energy regulator (VER)’s ability to generate coordinated locomotion in lower limb exoskeleton in a study on nine individuals. A comprehensive analysis of metrics obtained at three different speeds demonstrates the emergence of natural and coordinated locomotion as a result of a shared control between human and exoskeleton maintained with the VER. We also introduce a metric to measure the user’s contribution to gait and demonstrated that the resultant locomotion limit cycle is a linear combination of human-intended limit cycle and the VER’s limit cycle. This results may have implications about how central nervous system controls our locomotion.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".