Abstract
We present in this paper a detailed comparison of different algorithms and devices to determine the number of words read in everyday life. We call our system the “Wordometer”. We used three kinds of eye tracking systems in our experiment: mobile video-oculography (MVoG); stationary video-oculography (SVoG); and electro-oculography (EoG). By analyzing the movement of the eyes we were able to estimate the number of words that a user read. Recently, inexpensive eye trackers have appeared on the market. Thus, we undertook a large-scale experiment that compared three devices that can be used for daily reading on a screen: the Tobii Eye X SVoG; the JINS MEME EoG; and the Pupil MVoG. We found that the accuracy of the everyday life devices and professional devices was similar when used with the Wordometer. We analyzed the robustness of the systems for special reading behaviors: rereading and skipping. With the MVoG, SVoG and EoG systems, we obtained estimation errors respectively, 7.2%, 13.0%, and 10.6% in our main experiment. In all our experiments, we obtained 300 recordings by 14 participants, which amounted to 109,097 read words.
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