Multi-Lateral Teleoperation Based on Multi-Agent Framework: Application to Simultaneous Training and Therapy in Telerehabilitation
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Bibliographic record
Abstract
In this paper, a new scheme for multi-lateral remote rehabilitation is proposed. There exist one therapist, one patient, and several trainees, who are participating in the process of telerehabilitation (TR) in this scheme. This kind of strategy helps the therapist to facilitate the neurorehabilitation remotely. Thus, the patients can stay in their homes, resulting in safer and less expensive costs. Meanwhile, several trainees in medical education centers can be trained by participating partially in the rehabilitation process. The trainees participate in a "hands-on" manner; so, they feel like they are rehabilitating the patient directly. For implementing such a scheme, a novel theoretical method is proposed using the power of multi-agent systems (MAS) theory into the multi-lateral teleoperation, based on the self-intelligence in the MAS. In the previous related works, changing the number of participants in the multi-lateral teleoperation tasks required redesigning the controllers; while, in this paper using both of the decentralized control and the self-intelligence of the MAS, avoids the need for redesigning the controller in the proposed structure. Moreover, in this research, uncertainties in the operators' dynamics, as well as time-varying delays in the communication channels, are taken into account. It is shown that the proposed structure has two tuning matrices (L and D) that can be used for different scenarios of multi-lateral teleoperation. By choosing proper tuning matrices, many related works about the multi-lateral teleoperation/telerehabilitation process can be implemented. In the final section of the paper, several scenarios were introduced to achieve "Simultaneous Training and Therapy" in TR and are implemented with the proposed structure. The results confirmed the stability and performance of the proposed framework.
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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