A Small-Gain Approach for Nonpassive Bilateral Telerobotic Rehabilitation: Stability Analysis and Controller Synthesis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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) | 0.000 | 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) | 0.000 | 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 it