Design of a Virtual Reality Interactive Serious Game for Older Adults for Improving Spatial Cognitive function
Bibliographic record
Abstract
It is known that isolation, especially in old age, is a risk factor for cognitive and mental health decline. The need for socialization in assisted living housing and nursing homes became blatantly apparent during the recent pandemic, more so than ever. This paper introduces the design of a virtual reality (VR) interactive social environment aimed at promoting cognitive engagement and enhancing mental well-being. The platform features online multiplayer functionality, PC/laptop connectivity, and online guided interactive VR Castle game activities led by an instructor. The users will need only a headset, a computer, and a reliable Wi-Fi connection to use the designed platform. Users of our designed program will interact, practice with the help of an instructor, and receive feedback on their performance from their own location. This is particularly beneficial for older adults who have difficulty in commuting, which adds to their isolation. We will take the rehabilitation setup to their home rather than requiring them to commute. While no evaluation of the efficacy of the VR Castle game has been done yet, this paper primarily focuses on presenting the design, implementation, and feasibility of the VR Castle game. Future research will report on the experimental validation of the VR Castle's impact on spatial cognitive function, which is presently awaiting ethical approval and participant recruitment. Given that interactive digital interventions are beneficial for cognitive and mental health among older adults, our designed platform will improve executive cognitive functions and spatial orientation in a social, engaging, and challenging environment, hence also improving their emotional well-being.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".