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Monitoring Dementia with Automatic Eye Movements Analysis

  • Yanxia Zhang
  • Thomas Wilcockson
  • Kwang In Kim
  • Trevor Crawford
  • Hans Gellersen
  • Pete Sawyer
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 57)

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 

Notes

Acknowledgments

The work described in this paper is funded by EPSRC project EP/M006255/1 Monitoring Of Dementia using Eye Movements (MODEM).

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© Springer International Publishing Switzerland 2016

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Authors and Affiliations

  • Yanxia Zhang
    • 1
  • Thomas Wilcockson
    • 1
  • Kwang In Kim
    • 1
  • Trevor Crawford
    • 1
  • Hans Gellersen
    • 1
  • Pete Sawyer
    • 1
  1. 1.Lancaster UniversityBailriggUK

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