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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, Pages 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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