Attention-driven Interaction Systems for Augmented Reality

Augmented reality (AR) glasses enable the embedding of visual content in a real-world surroundings. In this PhD project, I will implement user interfaces which adapt to the cognitive state of the user, for example by avoiding distractions or re-directing the user’s attention towards missed information. For this purpose, sensory data from the user is captured (Brain activity via EEG of fNIRS, eye tracking, physiological measurements) and modeled with machine learning techniques. The focus of the cognitive state estimation is centered around attention related aspects. The main task is to build models for an estimation of a person’s attentional state from the combination and classification of multimodal data streams and context information, as well as their evaluation. Furthermore, the goal is to develop prototypical user interfaces for AR glasses and to test their usability in different scenarios.