Location

Media Lab, Room E15-235

Description

Context-awareness has emerged as a key area of research in ubiquitous computing and human-computer interaction. The context of a person is typically defined as a combination of different personal and environmental aspects. To get at the personal context, human physical activity is widely considered to be one of the most important contextual cues. Important advances in activity recognition were achieved using sensing modalities such as body movement and posture, sound, or interactions between people.
A promising new source of information for context recognition is the movement of the eyes. The movement patterns our eyes perform as we carry out specific activities reveal a lot about the activities themselves—independently of what we are looking at. Because we use our eyes in almost everything that we do, eye movements have the potential to provide useful information for a variety of context-recognition problems. Moreover, eye movements are linked to cognitive processes of visual perception, such as attention, visual memory, or learning. Inferring these processes from eye movements may allow us to extend the current notion of context with an as-yet-missing cognitive dimension, leading to so-called cognitive-aware systems. Such systems are able to sense and react to the cognitive state of a person and enable novel types of human-computer interaction not possible today.
The development of sensors to track eye movements in daily life, however, is still an active topic of research. Mobile settings call for highly miniaturized, low-power eye trackers with real-time processing capabilities. These requirements are increasingly addressed by commonly used video-based systems. However, these systems still require bulky equipment and demanding video processing. In contrast to video-based systems, electrooculography (EOG) is a cheap measurement technique for mobile eye movement recordings. It is computationally light-weight and can be implemented as a wearable sensor.

Biographies

Andreas Bulling received his MSc degree in computer science from the Technical University of Karlsruhe, Germany, in 2006, with a focus on embedded systems, robotics and automation, and biomedical engineering. In the same year, he joined the Swiss Federal Institute of Technology (ETH), Electronics Laboratory, in Zurich, Switzerland, as a doctoral candidate and member of the research staff. His current research concentrates on context recognition using eye movement analysis, cognitive-aware systems, novel methods for eye-based human-computer interaction, and the development of embedded sensors for wearable eye tracking.