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• W1562634897 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." @default.
• W1562634897 created "2016-06-24" @default.
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• W1562634897 date "2008-01-01" @default.
• W1562634897 modified "2023-10-17" @default.
• W1562634897 title "Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography" @default.
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• W1562634897 doi "https://doi.org/10.1007/978-3-540-79576-6_2" @default.
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