Issue No. 04 - April (2011 vol. 33)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPAMI.2010.86
Andreas Bulling , University of Cambridge, Cambridge and Lancaster University, Lancaster
Jamie A. Ward , Lancaster University, Lancaster
Hans Gellersen , Lancaster University, Lancaster
Gerhard Tröster , Swiss Federal Institute of Technology (ETH), Zurich
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 of 76.1 percent and recall of 70.5 percent over all classes and participants. The work demonstrates the promise of eye-based activity recognition (EAR) and opens up discussion on the wider applicability of EAR to other activities that are difficult, or even impossible, to detect using common sensing modalities.
Ubiquitous computing, feature evaluation and selection, pattern analysis, signal processing.
J. A. Ward, G. Tröster, A. Bulling and H. Gellersen, "Eye Movement Analysis for Activity Recognition Using Electrooculography," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 33, no. , pp. 741-753, 2010.