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Robust Recognition of Reading Activity in Transit Using Wearable Electrooculography

Andreas Bulling, Jamie A. Ward, Hans Gellersen, Gerhard Tröster

Proc. International Conference on Pervasive Computing (Pervasive), pp. 19-37, 2008.


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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BibTeX

@inproceedings{bulling08_pervasive, author = {Bulling, Andreas and Ward, Jamie A. and Gellersen, Hans and Tr{\"{o}}ster, Gerhard}, keywords = {Activity Recognition, Electrooculography (EOG), Reading Activity, Recognition of Reading, Transit, wearable}, title = {Robust {R}ecognition of {R}eading {A}ctivity in {T}ransit {U}sing {W}earable {E}lectrooculography}, booktitle = {Proc. International Conference on Pervasive Computing (Pervasive)}, year = {2008}, pages = {19-37}, doi = {10.1007/978-3-540-79576-6_2} }