Online Reference Trajectory Adaptation: A Personalized Control Strategy for Lower Limb Exoskeletons
Bibliographic record
Abstract
Abstract This paper presents a novel method for reference trajectory adaptation in lower limb rehabilitation exoskeletons during walking. Our adaptation rule is extracted from a cost function that penalizes both interaction force and trajectory modification. By adding trajectory modification term into the cost function, we restrict the boundaries of the reference trajectory adaptation according to the patient’s motor capacity. The performance of the proposed adaptation method is studied analytically in terms of convergence and optimality. We also developed a realistic dynamic walking simulator and utilized it in performance analysis of the presented method. The proposed trajectory adaptation technique guarantees convergence to a stable, reliable, and rhythmic reference trajectory with no prior knowledge about the human intended motion. Our simulations demonstrate the convergence of exoskeleton trajectories to those of simulated healthy subjects while the exoskeleton trajectories adapt less to the trajectories of patients with reduced motor capacity (less reliable trajectories). Furthermore, the gait stability and spatiotemporal parameters such as step time symmetry and minimum toe off clearance enhanced by the adaptation in all subjects. The presented mathematical analysis and simulation results show the applicability and effectiveness of the proposed method and its potential to be applied for trajectory adaptation in lower limb rehabilitation exoskeletons.
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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 | 0.000 |
| 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".