Abstract
In this work we investigate eye movement analysis as a new sensing
modality for activity recognition. Eye movement data was 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 feature
selection (mRMR). 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 hand-written
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 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. 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.