Feasibility of a developed cognitive training system based on virtual reality with smart mirror for expert in community older cognitive disabled persons setting
Bibliographic record
Abstract
BackgroundNumerous stroke survivors reintegrating into the community experience cognitive challenges that restrict their engagement, subsequently contributing to additional cognitive decline and adversely affecting their quality of life.ObjectiveThis study seeks to feasibility a cognitive training system based on virtual reality with a smart mirror designed for cognitive disabled persons with chronic stroke in the community setting.MethodsTen cognitive disabled persons with chronic stroke aged 60 years older, each with independent mobility in the community, were involved in this study. The validation process included a 30-min cognitive training session administered twice a week for eight weeks. The feasibility of cognitive function assessments employed the MoCA-K and CoSAS. Additionally, a usability test was performed at the end of the experiment using SUS and the Adapted IMI. The Wilcoxon signed rank test was then employed to compare pre- and post-cognitive function results.ResultsThe feasibility of the implemented cognitive training system based on virtual reality with smart mirror revealed significant differences in the total score, delayed recall, and orientation items of the MoCA-K (p < 0.05). Additionally, a notable improvement was observed in the accuracy and response time of task performance in the CoSAS (p < 0.05). Usability test results indicated an SUS mean score of 73.5 (SD 17.25) and an Adapted IMI score of 5.63 (SD 1.55), surpassing suggested thresholds for usability tests.ConclusionsProviding a cognitive training system tailored for the community, this approach aims to the prevention and recovery of cognitive issues in the older cognitive disabled persons with chronic stroke.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".