A Small-Gain Approach for Nonpassive Bilateral Telerobotic Rehabilitation: Stability Analysis and Controller Synthesis
Bibliographic record
Abstract
In this paper, the design of a novel bilateral telerobotic architecture for rehabilitation purposes is proposed and the related feasibility, stability, and control challenges are studied. The objective is to incorporate the supervision of a local/remote human physiotherapist into haptics-enabled rehabilitation systems and allow the therapist to provide nonpassive nonlinear assistive/resistive forces in response to the patient's movements. This can address a challenge of conventional software-based rehabilitation systems, i.e., limited capability in adjusting the therapy. To guarantee human-robot interaction safety, a new design framework and a stabilizing controller are developed based on the small-gain approach. System stability and transparency are analyzed in the presence of the nonpassive, nonlinear, and nonautonomous behavior of the terminals (the therapist and the patient) and time-varying delays for the case of remote and cloud-based therapy. Several practical considerations have been taken into account to match the clinical needs and minimize the implementation cost. Simulation studies, practical implementation, and experimental evaluations are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".