4.6 Article

Automatic Detection of Cognitive Impairment with Virtual Reality

Journal

SENSORS
Volume 23, Issue 2, Pages -

Publisher

MDPI
DOI: 10.3390/s23021026

Keywords

feature engineering; linear regression; statistical learning; psychosis; cognitive assessment; virtual reality

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Cognitive impairment has a severe impact on safety and daily task performance. Virtual Reality (VR) systems are being used to recognize, diagnose and treat cognitive impairment. In this study, a novel VR-based measure of cognitive performance is described and shown to correlate with clinically-validated measures. The most important feature extracted from participants' behavior in the VR environment is found to be hesitation score.
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 R-2 = 0.369, F-Stat = 7.158, p(F-Stat) = 0.000128.

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