Abstract
In this article we introduce the analysis of eye motion as a new input modality for activity recognition, context-awareness and mobile HCI
applications. We describe a novel embedded eye tracker that, in contrast
to common systems using video cameras, relies on Electrooculography
(EOG). This self-contained wearable device consists of goggles with
dry electrodes integrated into the frame and a small pocket-worn
component with a DSP for real-time EOG signal processing. It can
store data locally for long-term recordings or stream processed EOG
signals to a remote device over Bluetooth. We show how challenges
associated with wearability, eye motion analysis and signal artefacts
caused by physical activity can be addressed with a combination of
a special mechanical design, optimised algorithms for eye movement
detection and adaptive signal processing. In two case studies, we
demonstrate that EOG is a suitable measurement technique for the
recognition of reading activity and eye-based human-computer interaction.
Eventually, wearable EOG goggles may pave the way for seamless eye
movement analysis and new forms of context-awareness not possible
today.
