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Automatic Detection of Cognitive Impairment with Virtual Reality

Mannan, Farzana A., Porffy, Lilla A., Joyce, Dan W., Shergill, Sukhwinder S., Celiktutan, Oya (2023) Automatic Detection of Cognitive Impairment with Virtual Reality. Sensors, 23 (2). Article Number 1026. ISSN 1424-8220. (doi:10.3390/s23021026) (KAR id:99890)

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Cognitive impairment features in neuropsychiatric conditions and when undiagnosed can have a severe impact on the affected individual’s safety and ability to perform daily tasks. Virtual Reality (VR) systems are increasingly being explored for the recognition, diagnosis and treatment of cognitive impairment. In this paper, we describe novel VR-derived measures of cognitive performance and show their correspondence with clinically-validated cognitive performance measures. We use an immersive VR environment called VStore where participants complete a simulated supermarket shopping task. People with psychosis (k=26) and non-patient controls (k=128) participated in the study, spanning ages 20–79 years. The individuals were split into two cohorts, a homogeneous non-patient cohort (k=99 non-patient participants) and a heterogeneous cohort (k=26 patients, k=29 non-patient participants). Participants’ spatio-temporal behaviour in VStore is used to extract four features, namely, route optimality score, proportional distance score, execution error score, and hesitation score using the Traveling Salesman Problem and explore-exploit decision mathematics. These extracted features are mapped to seven validated cognitive performance scores, via linear regression models. The most statistically important feature is found to be the hesitation score. When combined with the remaining extracted features, the multiple linear regression model resulted in statistically significant results with R2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.

Item Type: Article
DOI/Identification number: 10.3390/s23021026
Additional information: For the purpose of open access, the author has applied a CC BY public copyright licence (where permitted by UKRI, an Open Government Licence or CC BY ND public copyright licence may be used instead) to any Author Accepted Manuscript version arising
Uncontrolled keywords: feature engineering; linear regression; statistical learning; psychosis; cognitive assessment; virtual reality
Subjects: R Medicine
R Medicine > RC Internal medicine
Divisions: Divisions > Division of Natural Sciences > Kent and Medway Medical School
Funders: Medical Research Council (
King's College London (
National Institute for Health Research (
South London and Maudsley NHS Foundation Trust (
SWORD Depositor: JISC Publications Router
Depositing User: JISC Publications Router
Date Deposited: 03 Feb 2023 16:28 UTC
Last Modified: 22 Feb 2023 14:20 UTC
Resource URI: (The current URI for this page, for reference purposes)
Shergill, Sukhwinder S.:
Celiktutan, Oya:
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