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

B.: A foveal inset for large display environments

a) b) Figure 1: Foveal inset. a: The user directs the projection of the foveal inset using a laser pointer. The size of the projected inset is significantly smaller than the tiles of the rear-projected display wall, thus providing a higher resolution. b: Magnified view of a high-resolution slice of a cryosection of a monkey brain. The boundary between the high-resolution foveal inset (left) and the lower-resolution display wall (right) is clearly visible. Note that the pixel dimensions of the foveal inset projector and the display wall projector are identical. (Aerial photographs courtesy of the City of Davis, CA. Monkey brain data set courtesy of E.G. Jones, UCD Center for Neuroscience.) We introduce a system that adds a foveal inset to large-scale projection displays. The effective resolution of the foveal inset projection is higher than the original display resolution, allowing the user to see more details and finer features in large data sets. The foveal inset is generated by projecting a high-resolution image onto a mirror mounted on a pan–tilt unit (PTU) that is controlled by the user with a laser pointer. Our implementation is based on Chromium and supports many OpenGL applications without modifications

Distanzabhängige Interaktion in großen hochauflösenden Displayumgebungen

Das Ziel der Arbeit ist die Entwicklung von Methoden, die den Nutzer bei seiner Tätigkeit in großen hochauflösenden Displayumgebungen unterstützen, indem die Visualisierung und die Interaktivität an den aktuellen Betrachtungsabstand angepasst werden. Das vorgestellte Interaction Scaling (IS) verwendet die physische Navigation für die Anpassung, indem die Berechnung eines distanzabhängigen Mappings mit automatischem/manuellem Wechsel der Präzisionsstufen kombiniert wird. In Studien wird aufgezeigt, dass IS für 2D Manipulation die Nutzerperformanz verbessert, wenn die benötigte Präzision steigt