TY - JOUR
T1 - Task Classification Model for Visual Fixation, Exploration, and Search
AU - Kumar, Ayush
AU - Tyagi, Anjul
AU - Burch, Michael
AU - Weiskopf, Daniel
AU - Mueller, Klaus
PY - 2019/7/29
Y1 - 2019/7/29
N2 - Yarbus' claim to decode the observer'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's eye movement data.
AB - Yarbus' claim to decode the observer'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's eye movement data.
KW - Machine Learning
UR - https://arxiv.org/abs/1907.12635
U2 - 10.48550/arXiv.1907.12635
DO - 10.48550/arXiv.1907.12635
M3 - Article
VL - 2019
JO - arXiv.org, e-Print Archive, Mathematics
JF - arXiv.org, e-Print Archive, Mathematics
M1 - 1907.12635
ER -