Modeling and Emulating a Physiotherapist's Role in Robot‐Assisted Rehabilitation
Bibliographic record
Abstract
In home‐based rehabilitation, one possible approach is haptic teleoperation in which a hospital‐based therapist is haptically linked and tele‐presented to a home‐based patient to effectively simulate traditional in‐hospital therapies over a distance. In this context, this article proposes a learn‐and‐replay (LAR) paradigm that consists of two phases: a therapist‐in‐loop (interactive) phase where the therapist interacts through the haptic teleoperation loop with the patient to perform the cooperative therapy task, and a therapist‐out‐of‐loop (standalone) phase where the therapist's task is played by the patient‐side robot in future repetitions. During the interactive phase, the therapist demonstrates impedance during cooperating with the patient. During the standalone phase, the patient‐side robot is automatically controlled to mimic the therapist's demonstrated impedance which is learned in the interactive phase. The direct force reflection (DFR) architecture is utilized as the control method for the bilateral telerehabilitation system. Case studies involving 1‐degree‐of‐freedom and 2‐degree‐of‐freedom cooperative manipulation tasks are tested for proof of concept. The results show that the impedance of the therapist's arm can be replicated by the patient‐side robot for both tasks and proposed LAR telerehabilitation paradigm that assists the therapist in the rehabilitation procedure to take care of other tasks or attend to other patients.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".