"This research proposal is embedded in the research fields of ambient intelligence and human-computer interaction. One goal of both fields is to develop computing systems that are able to continuously monitor, learn from and proactively adapt to the state (or context) of the user. The proposal aims to add an exciting new concept to this topic - the use of eye movements to infer the cognitive user context. This project has the potential to lead to the development of cognition-aware systems, thus opening the door to a new area of research on the boundary between the cognitive and computer sciences.
This highly inter-disciplinary project will focus on simultaneous assessment of eye and body movements as particularly promising means to infer the cognitive context of the user. The two main objectives are 1) the development of a machine learning framework for real-time inference of selected aspects of visual cognition from eye movements and 2) the extension of this framework to using additional sensing modalities, particularly body movements and physiological parameters.
These objectives will be achieved by running a series of empirical studies, by developing pattern recognition and machine learning techniques specifically geared for simultaneous classification of eye and body movements, and by evaluating these techniques in real-time in a driving simulator. Experimental data will be collected using a wearable eye tracker and body-worn motion and physiological sensors.
The current proposal directly contributes to Challenge 1 of the FP7 Work Program (Objective ICT-2011.1.3) and Challenge 2 (Cognitive Systems and Robotics). Its outcomes are expected to contribute to our understanding of natural cognitive systems, specifically cognitive processes in natural visual behaviour, attention and eye-hand coordination, as well as to contribute new computational methods for analysis, modeling and machine recognition of visual cognitive processes from time series eye movement data."
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