Feasibility of a Virtual-Reality-Enabled At-Home Telerehabilitation Program for Stroke Survivors: A Case Study
Bibliographic record
Abstract
Stroke rehabilitation is a lengthy procedure that is necessary for stroke recovery. However, stroke rehabilitation may not be readily available for patients who live rurally due to barriers such as transportation and expenses. This shortage in wearable technology, in turn, causes health disparity among the rural population, which was exacerbated by the COVID-19 pandemic restrictions. Telerehabilitation (TR) is a potential solution for stroke rehabilitation in rural areas. This one-case study aimed to examine the feasibility and safety of a technology-enabled at-home TR program for stroke survivors living in a rural area in Canada. A VR setup was installed successfully in the home of our participant. A tablet was also supplied for the TR program. Each program consisted of 24 sessions to be completed over a 12-week period. Our participant was assessed on day one using the Fugl-Meyer assessment, the Modified Ashworth Scale, the 10 m walk test, and the Mini-Mental State Exam. Three questionnaires were also completed, including the Motor Activity Log (MAL), the Stroke Index Scale (SIS), and the Treatment Self-Regulation Questionnaire. These assessments were completed thrice, on day 1, at week 6, and at week 12. The participant found the tablet and its accompanying exercises easy to use, with a few limitations. The participant found the VR system more challenging to manage independently as a lack of comfortability, the visual contrast during the first trials, and certain technical aspects of the technology created several functional barriers. Although some limitations with the technology were noted, this case study indicates that telerehabilitation is feasible under certain circumstances when used in conjunction with traditional rehabilitation services.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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 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".