Eye Movement Analysis for Activity Recognition
Andreas Bulling, Jamie A. Ward, Hans Gellersen, Gerhard Tröster
Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), pp. 41-50, 2009.
Abstract
In this work we investigate eye movement analysis as a new modality for recognising human activity. We devise 90 different features based on the main eye movement characteristics: saccades, fixations and blinks. The features are derived from eye movement data recorded using a wearable electrooculographic (EOG) system. We describe a recognition methodology that combines minimum redundancy maximum relevance feature selection (mRMR) with a support vector machine (SVM) classifier. We validate the method in an eight participant study in an office environment using five activity classes: copying a text, reading a printed paper, taking hand-written notes, watching a video and browsing the web. In addition, we include periods with no specific activity. Using a person-independent (leave-one-out) training scheme, we obtain an average precision of 76.1% and recall of 70.5% over all classes and participants. We discuss the most relevant features and show that eye movement analysis is a rich and thus promising modality for activity recognition.Links
Paper: bulling09_ubicomp.pdf
BibTeX
@inproceedings{bulling09_ubicomp,
author = {Bulling, Andreas and Ward, Jamie A. and Gellersen, Hans and Tr{\"{o}}ster, Gerhard},
keywords = {Activity Recognition, Electrooculography (EOG), Eye Movement Analysis, Recognition of Office Activities},
title = {Eye Movement Analysis for Activity Recognition},
booktitle = {Proc. ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp)},
year = {2009},
pages = {41-50},
doi = {10.1145/1620545.1620552}
}
