Bulling, Andreas and Ward, Jamie and Gellersen, Hans and Tröster, Gerhard (2011) Eye Movement Analysis for Activity Recognition Using Electrooculography. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33 (4). pp. 741-753. ISSN 0162-8828Full text not available from this repository.
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.
|Journal or Publication Title:||IEEE Transactions on Pattern Analysis and Machine Intelligence|
|Uncontrolled Keywords:||Ubiquitous computing ; Feature evaluation and selection ; Pattern analysis ; Signal processing|
|Departments:||Faculty of Science and Technology > School of Computing & Communications|
|Deposited On:||12 Apr 2012 11:13|
|Last Modified:||30 Apr 2017 02:54|
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