Home > Research > Publications & Outputs > Monitoring dementia with automatic eye movement...

Electronic data

  • AuthorAccepted

    Rights statement: The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-39627-9_26

    Accepted author manuscript, 7 MB, PDF-document

    Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License

Links

Text available via DOI:

View graph of relations

Monitoring dementia with automatic eye movements analysis

Research output: Contribution in Book/Report/ProceedingsConference contribution

Published
Publication date18/06/2016
Host publicationIntelligent Decision Technologies 2016: Proceedings of the 8th KES International Conference on Intelligent Decision Technologies (KES-IDT 2016) – Part II
EditorsIreneusz Czarnowski, Alfonso Mateos Caballero, Robert J. Howlett, Lakhmi C. Jain
PublisherSpringer
Pages299-309
Number of pages11
ISBN (Electronic)9783319396279
ISBN (Print)9783319396262
<mark>Original language</mark>English
Event8th KES International Conference on Intelligent Decision Technologies (KES-IDT) - Puerto de la Cruz, Spain

Conference

Conference8th KES International Conference on Intelligent Decision Technologies (KES-IDT)
CountrySpain
CityPuerto de la Cruz
Period15/06/1617/06/16

Publication series

NameSmart Innovation, Systems and Technologies
PublisherSpringer
Volume57
ISSN (Print)2190-3018

Conference

Conference8th KES International Conference on Intelligent Decision Technologies (KES-IDT)
CountrySpain
CityPuerto de la Cruz
Period15/06/1617/06/16

Abstract

Eye movement patterns are found to reveal human cognitive and mental states that can not be easily measured by other biological signals. With the rapid development of eye tracking technologies, there are growing interests in analysing gaze data to infer information about people’ cognitive states, tasks and activities performed in naturalistic environments. In this paper, we investigate the link between eye movements and cognitive function. We conducted experiments to record subject’s eye movements during video watching. By using computational methods, we identified eye movement features that are correlated to people’s cognitive health measures obtained through the standard cognitive tests. Our results show that it is possible to infer people’s cognitive function by analysing natural gaze behaviour. This work contributes an initial understanding of monitoring cognitive deterioration and dementia with automatic eye movement analysis.