Abstract
Smooth pursuit eye movements hold information about the health, activity
and situation of people, but to date there has been no efficient
method for their automated detection. In this work we present a method
to tackle the problem, based on machine learning. At the core of
our method is a novel set of shape features that capture the characteristic
shape of smooth pursuit movements over time. The features individually
represent incomplete information about smooth pursuits but are combined
in a machine learning approach. In an evaluation with eye movements
collected from 18 participants, we show that our method can detect
smooth pursuit movements with an accuracy of up to 92%, depending
on the size of the feature set used for their prediction. Our results
have twofold significance. First, they demonstrate a method for smooth
pursuit detection in mainstream eye tracking, and secondly they highlight
the utility of machine learning for eye movement analysis.
