Synergy-Based Control of Assistive Lower-Limb Exoskeletons by Skill Transfer
Bibliographic record
Abstract
Considering neuronal coordination between limbs, this article presents a study on the control of lower-limb exoskeletons for assistance of human gait by transferring the motor skills. The synergy-based robotic controller captures kinesiological information and biological signals from the healthy leg and generates intended motor patterns for the assisted leg in different gait phases of the slope walking behavior. First, we have developed a computationally efficient stiffness estimation model of the lower-limb joints and identified the experimental parameters in accord with the subject's locomotion behavior. The estimated stiffness matrix at minimum muscular contraction is scaled by cocontraction index and mapped to joint stiffness to be utilized in the control design. Then, we have proposed the impedance matching model and realized human skills transfer by surface electromyography signals. Considering the uncertain dynamics of the human-robot system, we have developed an adaptive fuzzy approximator to estimate robot's dynamic parameters and drive the robot tracking the referenced trajectories. The developed synergy-based control has been verified using three subjects with varying locomotor abilities. Results from these participants have shown a symmetrical and consistent adaptability between two legs with the synergy-based control, while the range of motion of the assisted leg in the affected side is more volitional and individualized.
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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".