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
| Authors | Mannan, Farzana A, Porffy, Lilla A, Joyce, Dan W, Shergill, Sukhwinder S and Celiktutan, O. |
|---|---|
| Abstract | 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. |
| Keywords | Linear 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 |
| Year | 2023 |
| Journal | Sensors |
| Journal citation | 23 (2), p. 1026 |
| ISSN | 1424-8220 |
| Digital Object Identifier (DOI) | https://doi.org/10.3390/s23021026 |
| Official URL | https://www.mdpi.com/1424-8220/23/2/1026 |
| Publication dates | |
| Online | 16 Jan 2023 |
| 01 Jan 2023 | |
| Publication process dates | |
| Accepted | 10 Jan 2023 |
| Deposited | 22 Feb 2023 |
| Output status | Published |
| Additional information | Publications router: Date 2023-01-16 of type 'publication_date' with format 'electronic' included in notification |
Publications router: License for this article: cc by included in notification | |
| License | CC BY |
| File |
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