Electrooculography Dataset for Reading Detection in the Wild

Shoya Ishimaru, Takanori Maruichi, Manuel Landsmann, Koichi Kise, Andreas Dengel

In: UbiComp 2019. ACM International Symposium on Wearable Computers (ISWC) London United Kingdom Seiten 85-88 ISBN 978-1-4503-6869-8 Association for Computing Machinery 9/2019.


Because of the diversity of document layouts and reading styles, detecting reading activities in real life is a challenging task compared to the detection in the laboratory setting. For contributing to the implementation of robust reading detection algorithms, we introduce a dataset which contains 220 hours of sensor signals from JINS MEME electrooculography glasses and corresponding ground truth activity labels. As a baseline study, we propose a statistical feature based reading detection approach and evaluate it on the dataset.

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Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence