Bulling, Andreas and Ward, Jamie A and Gellersen, Hans and Tröster, Gerhard (2009) Eye Movement Analysis for Activity Recognition. In: Ubicomp '09 Proceedings of the 11th international conference on Ubiquitous computing. ACM, New York, pp. 41-50. ISBN 978-1-60558-431-7Full text not available from this repository.
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.
|Item Type:||Contribution in Book/Report/Proceedings|
|Uncontrolled Keywords:||Ubiquitous computing|
|Subjects:||Q Science > QA Mathematics > QA75 Electronic computers. Computer science|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||18 Jan 2010 13:11|
|Last Modified:||27 Apr 2017 02:54|
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