Eye Movement Analysis for Activity Recognition Using Electrooculography

—In this work, we investigate eye movement analysis as a new sensing modality for activity recognition. Eye movement data were recorded using an electrooculography (EOG) system. We first describe and evaluate algorithms for detecting three eye movement characteristics from EOG signals—saccades, fixations, and blinks—and propose a method for assessing repetitive patterns of eye movements. We then devise 90 different features based on these characteristics and select a subset of them using minimum redundancy maximum relevance (mRMR) feature selection. We validate the method using an eight participant study in an office environment using an example set of five activity classes: copying a text, reading a printed paper, taking handwritten notes, watching a video, and browsing the Web. We also include periods with no specific activity (the NULL class). Using a support vector machine (SVM) classifier and person-independent (leave-one-person-out) training, we obtain an average precision ...

• Type: Journal
• Year: 2011
• Where: PAMI
• Authors: Andreas Bulling, Jamie A. Ward, Hans Gellersen, Gerhard Tröster
• Keywords: Activity Recognition, Eye Movement Data, Eye Movements, Operations Research, PAMI 2011