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| Content Provider | ACM Digital Library |
|---|---|
| Author | Aizawa, Akiko Topić, Goran Yamaya, Akito |
| Abstract | Eye movement is expected to provide important clues for analyzing the human reading process. However, the noisy tracking environment makes it difficult to map the gaze data captured by eye-trackers to the user's intended word. In this paper, we propose an effective approach for accurately mapping a fixation to a word in the text. Our method regards consecutive horizontally progressive fixations as a sequential reading segment. We first classify transitions between segments according to six classes, and then identify the set of segments associated with each line of the document. Our experiments demonstrate that the proposed method achieves 87% mapping accuracy (15% higher than our previous work) with a classification performance of 84%. We also confirmed that manual annotation time can be reduced by using our approach as a reference. We believe that our method provides sufficiently good accuracy to warrant future analysis. |
| Starting Page | 36 |
| Ending Page | 40 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781450341400 |
| DOI | 10.1145/2876456.2879481 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2016-03-07 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Reading Eye-tracking Fixation-to-word mapping |
| Content Type | Text |
| Resource Type | Article |
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