A novel framework enables accurate AR registration with integrated inertial gyroscope and vision tracking technologies. The framework includes a two-channel complementary motion filter that combines the lowfrequency stability of vision sensors with the highfrequency tracking of gyroscope sensors, hence, achieving stable static and dynamic six-degree-of-freedom pose tracking. Our implementation uses an Extended Kalman filter (EKF). Quantitative analysis and experimental results show that the fusion method achieves dramatic improvements in tracking stability and robustness over either sensor alone. We also demonstrate a new fiducial design and detection system in our example AR annotation systems that illustrate the behavior and benefits of the new tracking method.

Inertial measurement components, which sense either acceleration or angular rate, are being embedded into common user interface devices more frequently as their cost continues to drop dramatically. These devices hold a number of advantages over other sensing technologies: they measure relevant parameters for human interfaces and can easily be embedded into wireless, mobile platforms. The work in this dissertation demonstrates that inertial measurement can be used to acquire rich data about human gestures, that we can derive efficient algorithms for using this data in gesture recognition, and that the concept of a parameterized atomic gesture recognition has merit. Further we show that a framework combining these three levels of description can be easily used by designers to create robust applications.

To enable a user to perform Virtual Reality tasks as efficiently as possible, reducing tracking inaccuracies from noise and latency is crucial. Much work has been done to improve tracking performance by using predictive filtering methods. However, it is unclear what the benefits of each of these methods are in practice, which parameters influence their performance, and what the extent of this influence is. We present an analysis of various orientation prediction and filtering methods using various hand tasks and synthetic signals, and evaluate their performance in relation to each other. We identify critical parameters and analyse their influence on accuracy. Our results show that for the tested datasets, the use of an EKF is sufficient for orientation prediction in VR/AR.

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Previous studies reported good results in using virtual reality for the treatment of acrophobia. Similarly this paper reports the use of a virtual environment for the treatment of acrophobia. In the study, 10 subjects were exposed to three sessions of simulated heights in a virtual reality (VR) system, and 5 others were exposed to a real environment. Both groups revealed significant progress in a range of anxiety, avoidance and behaviour measurements when confronted with virtual as well as real height circumstances. Despite VR participants experiencing considerably shorter treatment times than the real-world subjects, significant improvements were recorded on the Behavioural Avoidance Test, the Attitudes Toward Heights Questionnaire and the Acrophobia Questionnaire. These results are suggestive of a possible higher effectiveness and efficiency of VR in treating acrophobia.