Ralf Biedert
ABSTRACT
While lots of reading happens on mobile devices, little research has been performed on how the reading-interaction actually takes place. Therefore we describe our findings on a study conducted with 18 users which were asked to read a number of texts while their touch and gaze data was being recorded. We found three reader types and identified their preferred alignment of text on the screen. Based on our findings we are able to computationally estimate the reading area with an approximate .81 precision and .89 recall. Our computed reading speed estimate has an average 10.9% wpm error in contrast to the measured speed, and combining both techniques we can pinpoint the reading location at a given time with an overall word error of 9.26 words, or about three lines of text on our device.
- Biedert, R., Buscher, G., and Dengel, A. 2010. The eyeBook - Using eye tracking to enhance the reading experience. Informatik-Spektrum 33, 3 (June), 272--281.Google Scholar
Cross Ref
- Biedert, R., Buscher, G., Schwarz, S., Hees, J., and Dengel, A. 2010. Text 2.0. CHI EA '10: Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems (Apr.), 4003--4008. Google ScholarDigital Library
- Biedert, R., Hosseiny, M., Georg, B., and Dengel, A. 2012. Towards Robust Gaze-Based Objective Quality Measures for Text. In Seventh ACM Symposium on Eye Tracking Research & Applications (ETRA), German Research Center for Artificial Intelligence. Google ScholarDigital Library
- Buscher, G., Biedert, R., Heinesch, D., and Dengel, A. 2010. Eye tracking analysis of preferred reading regions on the screen. In CHI EA '10: Proceedings of the 28th of the international conference extended abstracts on Human factors in computing systems, New York, NY, USA, 3307--3312. Google ScholarDigital Library
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Index Terms
- Reading and estimating gaze on smart phones
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