Robotics-Assisted Mirror Rehabilitation Therapy: A Therapist-in-the-Loop Assist-as-Needed Architecture
Bibliographic record
Abstract
This paper presents a therapist-in-the-loop framework for robotics-assisted mirror rehabilitation integrated with adaptive assist-as-needed therapy (ANT) that is adjusted based on the impairment level of the patient's affected limb. The framework, which is designed for patients with hemiparesis and/or hemispatial neglect, uses a patient's functional limb as the medium to transfer therapeutic training from the therapist to the patient's impaired limb (PIL). This allows the patient to use his/her functional limb to adjust the desired trajectory generated by the therapist if the trajectory is painful or uncomfortable for the PIL. In order to realize the adaptive patient-targeted therapy, two motor-function assessment metrics, performance symmetry and level of guidance, are proposed, providing real time, task-independent, and objective assessment of the PIL's motor deficiency. An adaptation law is also presented to adjust the intensity of the therapy delivered to the patient in real time and based on the aforementioned estimation of the impairment level of the PIL. Closed-loop system stability has been investigated in the presence of communication delays to facilitate tele/in-home rehabilitation. For this purpose, a combination of the Circle Criterion and the Small-Gain theorem has been applied to account both for communication time delays and the time-varying adaptive ANT. Results of experiments to investigate the performance of the proposed framework are reported.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".