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Record W4412616297 · doi:10.1002/sdtp.19002

P‐5.13: Research on Elderly Cognitive Memory Assessment Based on AR Technology

2025· article· en· W4412616297 on OpenAlexaboutno aff
Zhaoyan Li, Kang Zhang, Ruoxuan Wang, Xuefei Zhong

Bibliographic record

VenueSID Symposium Digest of Technical Papers · 2025
Typearticle
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyCognitionCognitive Assessment SystemCognitive psychologyGerontologyCognitive impairmentMedicineNeuroscience

Abstract

fetched live from OpenAlex

This study evaluated the feasibility of using augmented reality (AR) devices to assess memory and cognitive function in the elderly. This study developed an immersive and interactive memory test on the AR device, which includes four tasks: tool classification, picture memory, number memory, and word‐pair memory. Twenty participants over the age of 50 completed the both AR Memory Test and the traditional Montreal Cognitive Assessment (MoCA) and provided subjective feedback through questionnaires. The results showed that there was a significant positive correlation between the AR memory test and the MoCA assessment results. All participants expressed positive attitudes toward the AR Memory Test and reported no significant physiological discomfort. These findings suggest that the AR Memory Test effectively evaluates memory function in older adults, has the potential to replace traditional assessment scales, and enhances engagement through immersive and interactive experiences.

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.015
GPT teacher head0.331
Teacher spread0.317 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes1
Has abstractyes

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