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HUC
2009
Springer

Eye movement analysis for activity recognition

9 years 7 months ago
Eye movement analysis for activity recognition
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 ...
Andreas Bulling, Jamie A. Ward, Hans Gellersen, Ge
Added 25 Jul 2010
Updated 25 Jul 2010
Type Conference
Year 2009
Where HUC
Authors Andreas Bulling, Jamie A. Ward, Hans Gellersen, Gerhard Tröster
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