Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

Bulling, Andreas and Ward, Jamie A and Gellersen, Hans and Tröster, Gerhard (2008) Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography. In: Lecture Notes in Computer Science. Springer, Sydney, Australia, pp. 19-37. ISBN 978-3-540-79575-9

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Abstract

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.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa75
Subjects:
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 13046
Deposited By: ep_importer_comp
Deposited On: 25 Jun 2008 18:59
Refereed?: Yes
Published?: Published
Last Modified: 24 Aug 2019 23:57
URI: https://eprints.lancs.ac.uk/id/eprint/13046

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