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Real walking is the most natural way to locomote in virtual reality (VR), but a confined physical walking space limits its applicability. Redirected walking (RDW) is a collection of techniques to solve this problem. One of these techniques aims to imperceptibly rotate the user's view of the virtual scene in order to steer her along a confined path whilst giving the impression of walking in a straight line in a large virtual space. Measurement of perceptual thresholds for the detection of such a modified curvature gain have indicated a radius that is still larger than most room sizes. Since the brain is an adaptive system and thresholds usually depend on previous stimulations, we tested if prolonged exposure to an immersive virtual environment (IVE) with increased curvature gain produces adaptation to that gain and modifies thresholds such that, over time, larger curvature gains can be applied for RDW. Therefore, participants first completed a measurement of their perceptual threshold for curvature gain. In a second session, the same participants were exposed to an IVE with a constant curvature gain in which they walked between two targets for about 20 minutes. Afterwards, their perceptual thresholds were measured again. The results show that the psychometric curves shifted after the exposure session and perceptual thresholds for increased curvature gain further increased. The increase of the detection threshold suggests that participants adapt to the manipulation and stronger curvature gains can be applied in RDW, and therefore improves its applicability in such situations.

Locomotion and vison are closely linked. When users explore virtual environments by walking they rely on stable visible landmarks to plan and execute their next movement. In my research I am developing novel methods to predict locomotion paths of human subjects for the immediate future, i.e. the next few seconds. I aim to connect different types of behavioral data (eye, hand, feet and head tracking) and test their reliability and validity for predicting walking behavior in virtual reality. Such a prediction will be very valuable for natural interaction, for example in redirected walking schemes.My approach begins with an evaluation of the quality of data gathered with current tracking methods. Informative experimental conditions need to be developed to find meaningful patterns in natural walking. Next, raw tracked data of different modalities need to be connected with each other and aggregated in a useful way. Thereafter, possible valid predictors need to be developed and compared to already functioning predicting algorithms (e.g. [2],[6],[12]). As a final goal, all valid predictors shall be used to create a prediction algorithm returning the most likely future path when exploring virtual environments.

Modern progressive lenses are widely used to allow distance and near-view within one lens. Novice users experience motion sickness during adaptation and have problems performing everyday tasks when first using progressive lenses. To evaluate the effects of progressive lens distortion in psychophysical experiments these need to be simulated in real-time. We present a method to generate the distortion pattern of a progressive lens in virtual reality that allows testing perceptual distortions and behavioural consequences of exposure to such distortions. A user study showed that the heading angle on the horizontal axis was significantly underestimated through implemented distortion.