Using Alternative Views for Layout, Comparison and Context Switching Tasks in Wall Displays

In this paper we first present a set of tasks that are relevant to wall display interaction. Among these, layout management, context switching and comparison tasks could benefit from the use of interactive shortcut views of remote areas of a wall display, presented close to the user. Such a shortcut view technique, the ScaleView portals, is evaluated against using a simple magnification lens and walking when performing these tasks. We observed that for a layout and comparison task with frequent context switching, users preferred ScaleView portals. But for simpler tasks, such as searching, regular magnification lenses and walking were preferred. General observations on how the display was used as a peripheral reference by different participants highlighted one of the benefits of using wall sized displays: users may visually refer to the large, spread out content on the wall display, even if they prefer to interact with it close to their location. Author Keywords Wall displays, interaction, alternative view

Predicting human behavior in smart environments: theory and application to gaze prediction

Predicting human behavior is desirable in many application scenarios in smart environments. The existing models for eye movements do not take contextual factors into account. This addressed in this thesis using a systematic machine-learning approach, where user profiles for eye movements behaviors are learned from data. In addition, a theoretical innovation is presented, which goes beyond pure data analysis. The thesis proposed the modeling of eye movements as a Markov Decision Processes. It uses Inverse Reinforcement Learning paradigm to infer the user eye movements behaviors