A Gaussian Mixture Framework for Co-Operative Rehabilitation Therapy in Assistive Impedance-Based Tasks
Bibliographic record
Abstract
Rehabilitation robots can aid patients to practice activities of daily living in order to enhance muscle strength and recover motor functions. In this paper, we focus on robot-assisted rehabilitation for co-operative therapy tasks that elicit impedance-based behaviors from the patient. For instance, if the rehabilitation robot is controlled to behave as a self-closing door and if pulling this simulated door open is the therapy task the patient needs to complete, the patient's hand should display a minimum required impedance to complete the task. When a patient is unable to complete the task, determining the minimum assistance to be provided to the patient by the rehabilitation robot such that the task can be accomplished is of interest. In this paper, we compare the impedance behavior of a therapist in multiple trials of the task with that of the patient using a learning from demonstration (LfD) technique that utilizes Gaussian mixture models. First and during the demonstration phase, the therapist performs the tasks individually so that the robot gains insight into how a healthy person would perform the task. Next and during the reproduction phase, the robot will co-operate with the patient in the therapist's absence and provide him/her with adaptive external assistance on a patient-specific and as-needed basis so that the task can be completed. To encourage active participation, provision of assistance to the patient is coupled to the variability observed in the therapist's behavior across various trials of the task. Therefore, the presented framework transfers the constraints and underlying characteristics of a given impedance-based task to the rehabilitation robot leading to co-operative interaction between the robot and the patient where the robot provides just-enough assistance. Experimental results involving 1-D and 2-D impedance-based tasks show that the proposed framework effectively provides the patient with assistance as needed during co-operative therapy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".