Eye Movement Analysis for Activity Recognition Using Electrooculography

Abstract

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.

Activity recognition in smart homes with self verification of assignments

“…The three classifiers: DT, ANN and SVM, are learned for activity recognition [26], where SVM shows a stable performance compared to others. Finally, an interesting approach on eye movement based activity recognition applies SVM in order to classify six static activities such as browsing a web and reading a printed paper [27].…”

“…Feature selection techniques have been applied in the activity recognition problem to select significant and discriminant subset of features [13,27,31,44]. Minimum Redundancy Maximum Relevance (mRMR) can be used to select the best feature subset for target classes, then SVM is used for the classification of activities [27].…”

“…Minimum Redundancy Maximum Relevance (mRMR) can be used to select the best feature subset for target classes, then SVM is used for the classification of activities [27]. The mRMR and Information Gain (IG) methods are applied for feature selection and then four classifiers SVM, LogitBoost, Bayes Belief Networks and ANN are compared for recognition of activities [31], where LogitBoost proves to be better among the four due to its ability of generating a stronger classifier by combining the several weak classifiers.…”

Use of image processing to easy e-content reader for physically differently-abled personnel

“…The hypothesis identified is by using head movements recognition or face recognition method difficulty of scroll down pages issues could be solved The problem as identified is that the differently-abled personnel, especially people with various amputations face the issue of not being able to use the devices that allow them to access e-content properly (Bulling, et al, 2011). This is due to the fact that almost all these devices use input devices such as keyboards, mice or touch screens.…”

Markerless monocular tracking system for guided external eye surgery

“…The different computer eye-tracking methods that exists today can be classified into two main paradigms depending on the method used to perform the tracking: electro-oculography (EOG) [6,7,8,9,10,11] and videooculography (VOG). EOG computes the eye position by measuring the potential difference between the cornea and the most internal choroid layer (known as Brunch's membrane).…”