A Unified Gait Phase Estimation and Control of Exoskeleton using Virtual Energy Regulator (VER)
Bibliographic record
Abstract
Virtual Energy Regulator (VER) is a time independent controller that can generate stable limit cycles in lower-limb exoskeleton devices. In this work, we apply VER to control a lower-limb exoskeleton for assistive walking. We design two different limit cycles for hip and knee joints to assist the user during overground walking with the Indego explorer lower-limb exoskeleton. We tested the designed VER on a single participant for overground walking at a self-selected speed. Interestingly, due to VER time-independent nature, it can properly coordinate with the user’s motions and produce mechanically stable overground walking in which the user can walk overground without a walker or crutches. The resultant gait is also more similar to a normal gait with improved range of motion compared to cases without controller; range of motion improved from $42.9 \pm 4.8{deg}$ and $44.9 \pm 4.9 {deg}$ to $46.6 \pm 1.3 {deg}$ and $63.0 \pm 6.8 {deg}$ at hip and knee joints, respectively. Especially, for the knee joint, the user is able to fully extend her knee during stance phase only when the VER is in the loop. In VER, the radius of each desired limit cycle is a function of phase. Accordingly, during walking, the internal phase of the VER is a monotonically increasing parameter that can be considered as a candidate for real-time gait phase estimation and heel-strike event detection. Hence, for gait phase estimation, VER relies only on a single joint position provided by the exoskeleton.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".