|
Activity Number:
|
383
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
Section on Physical and Engineering Sciences
|
| Abstract - #305248 |
|
Title:
|
Statistical Analysis of Eye Movement Data
|
|
Author(s):
|
Ritaja Sur*+ and Benjamin Kedem
|
|
Companies:
|
University of Maryland and University of Maryland
|
|
Address:
|
Department of Mathematics, College Park, MD, 20742,
|
|
Keywords:
|
Eye Movement ; Time Series ; Discrimination ; Higher order crossings ; Cluster analysis ; Distance metrics
|
|
Abstract:
|
In this work, we consider the eye gaze of human subjects as a response to the motion of other body parts. The objective of this study is to determine whether eye movements differ significantly for two different cases denoted as "watch" and "imitate." The study of discrimination between these two cases is important for development of artificial intelligence. We consider the eye gaze as a time series data. For the purpose of discrimination, both parametric and nonparametric distance metrics are used. In particular, we consider a metric based on the higher order crossing (HOC) sequences. We compare the performance of these different distance measures in cluster analysis. Both hierarchical and non-hierarchical cluster algorithms are used. Results will show the first-time application of HOC sequences to the eye gaze data.
|
