An Adaptive Oscillators-Based Approach to Achieve Transparent Control of a Six DoF Lower-Limb Exoskeleton
Bibliographic record
Abstract
Lower-limb exoskeletons have been effectively used in rehabilitation to reduce gait impairments. However, making the exoskeleton transparent to provide suitable net interaction torques and assist patient’s movements is yet an open challenge. In this study, we designed a torque control method based on adaptive oscillators (AOs) to reduce interaction forces with a six degree-of-freedom exoskeleton, named the ExoRoboWalker. A synchronization layer was designed with a pool of AOs to estimate user’s gait phase, and a baseline torque controller was introduced to generate a torque profile based on user-robot interaction during the previous gait cycles. A zero-torque controller was used in parallel to the main torque controller with the objective of improving users’ control of the system and their balance. The proposed controller was experimentally evaluated in six healthy subjects who walked with the exoskeleton for 200 gait strides at different walking speeds. A transparent controller we previously implemented based on a zero-impedance model was also evaluated and results were compared with the new controller. The controller based on AOs was able to reduce the average interaction torques by 40% at the highest gait speed of 0.8 m/s relative to the zero-impedance controller we previously implemented.
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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 | 0.000 |
| 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".