Abstract
| Original language | English |
|---|---|
| Title of host publication | Intelligent Decision Technologies 2016 |
| Publisher | Springer |
| Pages | 299-309 |
| Number of pages | 11 |
| ISBN (Print) | 9783319396262 |
| DOIs | |
| Publication status | Published - 9 Jun 2016 |
Publication series
| Name | Smart Innovation, Systems and Technologies |
|---|
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Keywords
- Machine learning
- Eye movements analysis
- Health monitoring
- Dementia
- Cognitive function
- COGNITIVE IMPAIRMENT
- ALZHEIMERS-DISEASE
- INHIBITORY CONTROL
- MEMORY
Cite this
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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 -