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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.

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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} }