@inproceedings{49c18b6e39424d4188541b11e2fd1489,
title = "Task classification model for visual fixation, exploration, and search",
abstract = "Yarbus{\textquoteright} claim to decode the observer{\textquoteright}s task from eye movements has received mixed reactions. In this paper, we have supported the hypothesis that it is possible to decode the task. We conducted an exploratory analysis on the dataset by projecting features and data points into a scatter plot to visualize the nuance properties for each task. Following this analysis, we eliminated highly correlated features before training an SVM and Ada Boosting classifier to predict the tasks from this filtered eye movements data. We achieve an accuracy of 95.4\% on this task classification problem and hence, support the hypothesis that task classification is possible from a user{\textquoteright}s eye movement data.",
keywords = "Classifier, Eye movements, Task decoding, Visual attention, Yarbus",
author = "Ayush Kumar and Anjul Tyagi and Michael Burch and Daniel Weiskopf and Klaus Mueller",
note = "Publisher Copyright: {\textcopyright} 2019 Association for Computing Machinery.; 11th ACM Symposium on Eye Tracking Research and Applications, ETRA 2019 ; Conference date: 25-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
day = "25",
doi = "10.1145/3314111.3323073",
language = "English",
series = "Eye Tracking Research and Applications Symposium (ETRA)",
publisher = "Association for Computing Machinery",
editor = "Spencer, \{Stephen N.\}",
booktitle = "Proceedings - ETRA 2019",
}