As, nowadays, eye trackers have become more accurate and affordable, their use also increased. One of their applied fields is the analysis of reading behavior. In laboratory settings, various traits of reading like engagement, or text difficulty could be shown. But, there is usually one unanswered question preventing their automatic application in daily life: when is somebody reading? We have developed a tool that uses eye gaze data to detect the period of reading. Our specific use case is the vocabulometer, a website for learning English by reading texts. We used supervised learning on data from nonnative English speakers to train decision trees on reading detection. With features based on vertical eye movement, an accuracy of 93.5% on detecting reading and 92.7% on detecting not reading could be achieved.
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