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Record W2525629739

Augmented Reality Serious Gaming for Cognitive Health

2016· dissertation· en· W2525629739 on OpenAlexaboutno aff
Konstantinos Boletsis

Bibliographic record

VenueBIBSYS Brage (BIBSYS (Norway)) · 2016
Typedissertation
Languageen
FieldPsychology
TopicEducational Games and Gamification
Canadian institutionsnot available
Fundersnot available
KeywordsAugmented realityCognitionPsychologyCognitive interviewHuman–computer interactionComputer sciencePsychiatry
DOInot available

Abstract

fetched live from OpenAlex

Cognitive impairment in the elderly can be associated with the normal ageing processes or be a symptom of early onset dementia. Even though, early detection of dementia has many benefits, cognitive impairment is still underrecognised and under-diagnosed. This lack of diagnosis often leads to confusion over behavioural changes and prevents social and medical intervention and planning. Cognitive screening represents the initial step in a process of further assessment for cognitive impairment, leading to early diagnosis; however, it presents certain intrinsic limitations. These include culture, gender, and educational biases, long test-retest periods, “white coat” and learning effects, limited test validation and the user’s potential lack of motivation. Serious games can address those limitations and be an alternative to traditional, pen-and-paper and computerised cognitive screening tests, potentially motivating and engaging the user to regularly perform cognitive screening tasks, thus increasing the potential to recognise cognitive impairment and trigger referral for a more comprehensive, formal assessment. The current work contributes by designing, implementing, and testing a novel, gaming approach for the cognitive screening process, utilising stimulating cognitive training. The study thoroughly describes all the design and development stages of a serious game for cognitive training and screening from its inception and its theoretical groundings to its Release Candidate version and the evaluation of its test validity, focusing on the iterative design process and the evaluation of each stage. Finally, a cognitive training game for cognitive health screening of the elderly is produced, utilising an interaction technique based on Augmented Reality (AR) and the manipulation of tangible, physical objects (cubes). The game succeeds in stimulating the cognitive function of the elderly players, presenting high concurrent validity versus the widely used Montreal Cognitive Assessment (MoCA) score. Directions for future research in the area include the use of wearable biosensors - such as smartwatches - for cognitive health screening purposes, suggesting an ecosystem with serious games in the centre. The term “cognitive passport” is defined and discussed, as a tool for tracking personal cognitive health.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.705
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.002

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.

Opus teacher head0.047
GPT teacher head0.400
Teacher spread0.352 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes1
Has abstractyes

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