Mélodie Vidal
Andreas Bulling
Hans Gellersen
ABSTRACT
Eye tracking research in human-computer interaction and experimental psychology traditionally focuses on stationary devices and a small number of common eye movements. The advent of pervasive eye tracking promises new applications, such as eye-based mental health monitoring or eye-based activity and context recognition. These applications might require further research on additional eye movement types such as smooth pursuits and the vestibulo-ocular reflex as these movements have not been studied as extensively as saccades, fixations and blinks. In this paper we report our first step towards an effective discrimination of these movements. In a user study we collect naturalistic eye movements from 19 people using the two most common measurement techniques (EOG and IR-based). We develop a set of basic signal features that we extract from the collected eye movement data and show that a feature-based approach has the potential to discriminate between saccades, smooth pursuits, and vestibulo-ocular reflex movements.
- D. Bannach, P. Lukowicz, and O. Amft. Rapid prototyping of activity recognition applications. IEEE Pervasive Computing, 7(2):22--31, 2008. Google Scholar
Digital Library
- F. Behrens, M. MacKeben, and W. Schröder-Preikschat. An improved algorithm for automatic detection of saccades in eye movement data and for calculating saccade parameters. Behavior Research Methods, 42(3):701--708, 2010.Google ScholarCross Ref
- A. Bulling and H. Gellersen. Toward mobile eye-based human-computer interaction. IEEE Pervasive Computing, 9(4):8--12, 2010. Google ScholarDigital Library
- A. Bulling, J. A. Ward, H. Gellersen, and G. Tröster. Eye movement analysis for activity recognition using electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(4):741--753, Apr. 2011. Google ScholarDigital Library
- H. Drewes, A. De Luca, and A. Schmidt. Eye-gaze interaction for mobile phones. In Proceedings of the 4th international conference on mobile technology, applications, and systems and the 1st international symposium on Computer human interaction in mobile technology, Mobility '07, pages 364--371, New York, NY, USA, 2007. ACM. Google ScholarDigital Library
- W. A. Fletcher and J. A. Sharpe. Smooth pursuit dysfunction in alzheimer's disease. Neurology, 38(2):272, Feb. 1988.Google ScholarCross Ref
- W. G. Iacono. Eye movement abnormalities in schizophrenic and affective disorders. In Neuropsychology of Eye Movements, pages 115--145. Hillsdale, N.J., C. W. Johnston and F. J. Pirozzolo, editors edition, 1988.Google Scholar
- D. H. Koh, S. Munikrishne Gowda, and O. Komogortsev. Real time eye movement identification protocol. In Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems, pages 3499--3504. ACM Press, 2010. Google ScholarDigital Library
- O. V. Komogortsev, S. Jayarathna, D. H. Koh, and S. M. Gowda. Qualitative and quantitative scoring and evaluation of the eye movement classification algorithms. In ETRA '10: Proceedings of the 2010 Symposium on Eye-Tracking Research & Applications, pages 65--68, New York, NY, USA, 2010. ACM Press. Google ScholarDigital Library
- M. A. Kuskowski. Eye movements in progressive cerebral neurological disease. In Neuropsychology of Eye Movements, page 146--176. Hillsdale, N.J., C. W. Johnston and F. J. Pirozzolo, editors edition, 1988.Google Scholar
- F. L. Mastaglia, J. L. Black, and D. W. Collins. Quantitative studies of saccadic and pursuit eye movements in multiple sclerosis. Brain: A Journal of Neurology, 102(4):817--834, Dec. 1979. PMID: 509196.Google Scholar
- M. Nyström and K. Holmqvist. An adaptive algorithm for fixation, saccade, and glissade detection in eyetracking data. Behavior Research Methods, 42(1):188--204, 2010.Google ScholarCross Ref
- D. D. Salvucci and J. H. Goldberg. Identifying fixations and saccades in eye-tracking protocols. In ETRA '00: Proceedings of the 2000 Symposium on Eye-Tracking Research & Applications, pages 71--78. ACM Press, 2000. Google ScholarDigital Library
Index Terms
- Analysing EOG signal features for the discrimination of eye movements with wearable devices
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