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Hans Gellersen

Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

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In this work we analyse the eye movements of people in transit in an everyday environment using a wearable electrooculographic (EOG) system. We compare three approaches for continuous recognition of reading activities: a string matching algorithm which exploits typical characteristics of reading signals, such as saccades and fixations; and two variants of Hidden Markov Models (HMMs) - mixed Gaussian and discrete. The recognition algorithms are evaluated in an experiment performed with eight subjects reading freely chosen text without pictures while sitting at a desk, standing, walking indoors and outdoors, and riding a tram. A total dataset of roughly 6 hours was collected with reading activity accounting for about half of the time. We were able to detect reading activities over all subjects with a top recognition rate of 80.2% (71.0% recall, 11.6% false positives) using string matching. We show that EOG is a potentially robust technique for reading recognition across a number of typical daily situations.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science
EditorsJ. Indulska, D. J. Patterson, T. Rodden, M. Ott
Number of pages19
Volume5013
Publisherspringer
Publication year2008
Pages19-37
ISBN (print)978-3-540-79575-9
DOIs
Publication statusPublished - 2008
Externally publishedYes

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