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Home > Research > Publications & Outputs > Eye Movement Analysis for Activity Recognition
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Eye Movement Analysis for Activity Recognition

Research output: Contribution in Book/Report/ProceedingsPaper

Published

Publication date30/09/2009
Host publicationUbicomp '09 Proceedings of the 11th international conference on Ubiquitous computing
Place of publicationNew York
PublisherACM
Pages41-50
Number of pages10
ISBN (Print)978-1-60558-431-7
Original languageEnglish

Conference

ConferenceInternational Conference on Ubiquitous Computing (UbiComp 2009)
CityOrlando, FL, USA
Period30/09/093/10/09

Conference

ConferenceInternational Conference on Ubiquitous Computing (UbiComp 2009)
CityOrlando, FL, USA
Period30/09/093/10/09

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.

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