Investigating the feasibility of neuro-cognitive games for detecting the onset of dementia using a phantom arm compared to touchscreen version
Bibliographic record
Abstract
In this preliminary study, a virtual reality game was developed to detect the onset of dementia. The game takes place in a 3D virtual kitchen, and the player is tasked to identify displaced objects from memory and to recall the order of displacement. Two different hardware platforms were used to play the game; a touchscreen tablet and a phantom robotic arm. Cognitive abilities such as object recognition, spatial memory and memory retention were assessed. Study participants were 45 seniors, out of which four were diagnosed with Mild Cognitive Impairment (MCI) and 3 with Alzheimer’s disease (AD). Their performances were evaluated against the Montreal Cognitive Assessment (MoCA) test. They performed the experiments both with a phantom arm mimicking humans’ arm and with a touchscreen version. Healthy older adults performed significantly better than MCI participants, who in turn performed better than AD participants. MoCA significantly correlated with the game score on both hardware interfaces. There was also a significant difference between the performance score while using phantom robotic arm compared to that when using the touchscreen, pointing towards a deficit of visuomotor ability in ageing. The scores of performances using touchscreen version of the games was a significant predictor of MoCA, while the scores of using phantom arm was a significant predictor of age. MCI participants performed much worse on order recall tasks compared to object identification tasks, suggesting a more pronounced deficit in memory retention. More MCI and AD participants should be investigated to determine the designed experiments’ sensitivity and specificity in detecting dementia.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".