A Fuzzy-Based Adaptive Rehabilitation Framework for Home-Based Wrist Training
Bibliographic record
Abstract
Computer-based rehabilitation systems have emerged as promising assistive tools for effective training and diagnosis and gained popularity in clinical settings. For many patients, home-based rehabilitation can be really beneficial in their therapy journeys since it can eliminate the obstacles encountered by many of them in clinics, such as travel distance and cost. However, an effective home-training system requires a good adaptation mechanism that conforms to both the patient's abilities and the therapist's performance requirements. This paper introduces a Web-enabled wrist rehabilitation framework that adopts the fuzzy logic approach to provide adaptive tasks for the patient while considering the therapist training guidance. We also assess the effectiveness of the framework while coping with different training parameters by simulating a number of performance scenarios and experimenting with normal subjects. Simulation results, as well as experimental analysis, demonstrated the ability of the proposed framework to adapt to patient's performance and therapist's feedback.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".