Autonomous Assistance-as-Needed Control of a Lower Limb Exoskeleton With Guaranteed Stability
Bibliographic record
Abstract
The use of exoskeletons for clinical lower-limb stroke rehabilitation offers the potential of improved and customized rehabilitation that reduces the requirements and demands placed on multiple staff members. Initial research with lower-limb exoskeletons show potential to alleviate this problem. Conventional assistance-based exoskeleton devices simply enforce the desired gait trajectory for the patient in order to ensure safety and stability. Unfortunately, if the end-user does not have to work to contribute to successful motion, rehabilitation often does not occur. Recent evidence has suggested that assistance-as-needed control prevents users from slacking, facilitating functional motor recovery. Assistance-as-needed control turns off assistive torques during periods when the patient is able to execute a desired gait pattern but if the patients gait deviates sufficiently from a desired trajectory then assistive torques are generated to compensate for the patients loss of strength. This strategy encourages the patient to contribute effort while still enabling the exoskeleton to guide movements. Assistance-as-needed control inherently leads to aperiodic gait patterns and has accordingly been difficult to employ in lower-limb exoskeletons due to the need to ensure stability. This work demonstrates how virtual constraint control-a method with robust stability properties used in prostheses and assistive exoskeletons control-can be combined with a velocity-modulated deadzone to ensure stability. Simulations suggest that the method can accommodate a large deadzone while remaining stable across a range of unanticipated gait pathologies, as demonstrated using Lorenz mappings that can accommodate the aperiodic nature of the resulting gait.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".