Development of a Novel Virtual Reality Serious Game with Age Sensitive Measurement for Spatial Orientation Training
Bibliographic record
Abstract
When designing a cognitive serious game for older adult, it is also equally important to design a cognitive sensitive measurement. A significant number of previous studies in this era have formulated measurements with time and distance traversed in virtual environments. Both these parameters are susceptible to decline in motor skills that come with normal aging. Hence, they may not the best parameters for cognitive assessment among older adults. In this pilot study we developed a novel virtual environment and used the concept of error measurement from our lab’s previous study to come up with a new formula specific to this game for assessing spatial orientation in young and older adults. The spatial measure formula is compared with the measure of traversed distance by its power to predict participants’ age. The game and its proposed spatial measure formula was evaluated using data from 10 healthy young participants (21- 39 yr old) and 10 older healthy older adults (60-79 yr old). The participants’ cognitive status was tested by the Montreal Cognitive Assessment (MoCA). The results show that our proposed spatial error measurement has a medium strength correlation with age (more than that of traversed distance), which can be attributed to normal aging. Thus, while it is age sensitive, it is not affected by experience of playing video games in general. A See Path Again help button was used in the game, usage of which showed that some paths were easier to retrace than others; that might be because no mental rotation was involved in those paths.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".