Monitoring dementia with automatic eye movements analysis

Yanxia Zhang; Thomas Wilcockson; Kwang In Kim; Trevor Jeremy Crawford; Hans-Werner Georg Gellersen; Peter Harvey Sawyer

doi:10.1007/978-3-319-39627-9_26

Monitoring dementia with automatic eye movements analysis

Yanxia Zhang, Thomas Wilcockson, Kwang In Kim, Trevor Jeremy Crawford, Hans-Werner Georg Gellersen, Peter Harvey Sawyer

Engineering & Applied Science

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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.

Original language	English
Title of host publication	Intelligent Decision Technologies 2016
Publisher	Springer
Pages	299-309
Number of pages	11
ISBN (Print)	9783319396262
DOIs	https://doi.org/10.1007/978-3-319-39627-9_26
Publication status	Published - 9 Jun 2016

Publication series

Name	Smart Innovation, Systems and Technologies

Keywords

Machine learning
Eye movements analysis
Health monitoring
Dementia
Cognitive function
COGNITIVE IMPAIRMENT
ALZHEIMERS-DISEASE
INHIBITORY CONTROL
MEMORY

Access to Document

10.1007/978-3-319-39627-9_26

https://link.springer.com/chapter/10.1007/978-3-319-39627-9_26

Fingerprint Dive into the research topics of 'Monitoring dementia with automatic eye movements analysis'. Together they form a unique fingerprint.

Profiles

Peter Sawyer

Engineering & Applied Science
Computer Science - Professor in Computer Science
Aston Institute of Urban Technology and the Environment (ASTUTE)

Person: Academic

Cite this

Zhang, Y., Wilcockson, T., Kim, K. I., Crawford, T. J., Gellersen, H-W. G., & Sawyer, P. H. (2016). Monitoring dementia with automatic eye movements analysis. In Intelligent Decision Technologies 2016 (pp. 299-309). (Smart Innovation, Systems and Technologies). Springer. https://doi.org/10.1007/978-3-319-39627-9_26

@inbook{b3ec29bf5e2b422c92235d3987cb8ead,

title = "Monitoring dementia with automatic eye movements analysis",

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.",

keywords = "Machine learning, Eye movements analysis, Health monitoring, Dementia, Cognitive function, COGNITIVE IMPAIRMENT, ALZHEIMERS-DISEASE, INHIBITORY CONTROL, MEMORY",

author = "Yanxia Zhang and Thomas Wilcockson and Kim, {Kwang In} and Crawford, {Trevor Jeremy} and Gellersen, {Hans-Werner Georg} and Sawyer, {Peter Harvey}",

year = "2016",

month = jun,

day = "9",

doi = "10.1007/978-3-319-39627-9_26",

language = "English",

isbn = "9783319396262",

series = "Smart Innovation, Systems and Technologies",

publisher = "Springer",

pages = "299--309",

booktitle = "Intelligent Decision Technologies 2016",

address = "Germany",

}

Monitoring dementia with automatic eye movements analysis. / Zhang, Yanxia; Wilcockson, Thomas; Kim, Kwang In; Crawford, Trevor Jeremy; Gellersen, Hans-Werner Georg; Sawyer, Peter Harvey.

Intelligent Decision Technologies 2016. Springer, 2016. p. 299-309 (Smart Innovation, Systems and Technologies).

Research output: Chapter in Book/Report/Conference proceeding › Chapter

TY - CHAP

T1 - Monitoring dementia with automatic eye movements analysis

AU - Zhang, Yanxia

AU - Wilcockson, Thomas

AU - Kim, Kwang In

AU - Crawford, Trevor Jeremy

AU - Gellersen, Hans-Werner Georg

AU - Sawyer, Peter Harvey

PY - 2016/6/9

Y1 - 2016/6/9

N2 - 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.

AB - 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.

KW - Machine learning

KW - Eye movements analysis

KW - Health monitoring

KW - Dementia

KW - Cognitive function

KW - COGNITIVE IMPAIRMENT

KW - ALZHEIMERS-DISEASE

KW - INHIBITORY CONTROL

KW - MEMORY

UR - https://link.springer.com/chapter/10.1007/978-3-319-39627-9_26

U2 - 10.1007/978-3-319-39627-9_26

DO - 10.1007/978-3-319-39627-9_26

M3 - Chapter

SN - 9783319396262

T3 - Smart Innovation, Systems and Technologies

SP - 299

EP - 309

BT - Intelligent Decision Technologies 2016

PB - Springer

ER -