Patient-Robot-Therapist Collaboration Using Resistive Impedance Controlled Tele-Robotic Systems Subjected to Time Delays
Bibliographic record
Abstract
In this paper, an approach to physical collaboration between a patient and a therapist is proposed using a bilateral impedance control strategy developed for delayed tele-robotic systems. The patient performs a tele-rehabilitation task in a resistive virtual environment with the help of online assistive forces from the therapist being provided through teleoperation. Using this strategy, the patient's involuntary hand tremors can be filtered out and the effort of severely impaired patients can be amplified in order to facilitate their early engagement in physical tasks. The response of the first desired impedance model is tracked by the master robot (interacting with the patient), and the master trajectory plus a deviation as the response of the second impedance model is tracked by the slave robot (interacting with the therapist). Note that the first impedance model is a virtual mass-damper-spring system that has a response trajectory to the combination of patient and therapist forces. Similarly, the second impedance model is a virtual mass-damper-spring system that generates the desired slave–master deviation trajectory as its response to the therapist force. Transmitted signals through the communication channels are subjected to time delays, which exist in home-based rehabilitation (i.e., tele-rehabilitation). Tracking of the impedance models responses in the presence of modeling uncertainties is achieved by employing a nonlinear bilateral adaptive controller and proven using a Lyapunov analysis. The stability of delayed teleoperation system is also proven using the absolute stability criterion. The proposed control method is experimentally evaluated for patient–therapist collaboration in resistive/assistive tasks. In these experiments, a healthy human operator simulates a poststroke patient behavior during the interaction with the master robot.
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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 | 0.001 | 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) | 0.001 | 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".