EyeContext: Recognition of High-level Contextual Cues from Human Visual Behaviour

Andreas Bulling, Christian Weichel, Hans Gellersen

Proc. ACM SIGCHI Conference on Human Factors in Computing Systems (CHI), pp. 305-308, 2013.

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Abstract

In this work we present EyeContext, a system to infer high-level contextual cues from human visual behaviour. We conducted a user study to record eye movements of four participants over a full day of their daily life, totalling 42.5 hours of eye movement data. Participants were asked to self-annotate four non-mutually exclusive cues: social (interacting with somebody vs. no interaction), cognitive (concentrated work vs. leisure), physical (physically active vs. not active), and spatial (inside vs. outside a building). We evaluate a proof-of-concept EyeContext system that combines encoding of eye movements into strings and a spectrum string kernel support vector machine (SVM) classifier. Our results demonstrate the large information content available in long-term human visual behaviour and opens up new venues for research on eye-based behavioural monitoring and life logging.

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