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.
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Bulling, A., Ward, J.A., Gellersen, H., Tröster, G. (2008). Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography. In: Indulska, J., Patterson, D.J., Rodden, T., Ott, M. (eds) Pervasive Computing. Pervasive 2008. Lecture Notes in Computer Science, vol 5013. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-79576-6_2
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DOI: https://doi.org/10.1007/978-3-540-79576-6_2
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