Automatic detection of cognitive impairment with virtual reality

Journal article

Mannan, Farzana A, Porffy, Lilla A, Joyce, Dan W, Shergill, Sukhwinder S and Celiktutan, O. 2023. Automatic detection of cognitive impairment with virtual reality. Sensors. 23 (2), p. 1026. https://doi.org/10.3390/s23021026
AuthorsMannan, Farzana A, Porffy, Lilla A, Joyce, Dan W, Shergill, Sukhwinder S and Celiktutan, O.
AbstractCognitive 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.
KeywordsLinear regression; Virtual reality; Psychosis; Cognitive Assessment; Statistical Learning; Feature Engineering; Humans; Biometry; User-Computer Interface; Adult; Aged; Middle Aged; Young Adult; Cognitive Dysfunction; Virtual Reality; Recognition, Psychology
Year2023
JournalSensors
Journal citation23 (2), p. 1026
ISSN1424-8220
Digital Object Identifier (DOI)https://doi.org/10.3390/s23021026
Official URLhttps://www.mdpi.com/1424-8220/23/2/1026
Publication dates
Online16 Jan 2023
Print01 Jan 2023
Publication process dates
Accepted10 Jan 2023
Deposited22 Feb 2023
Output statusPublished
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