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Pen Input and Hand Occlusion

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We investigate, model, and design interaction techniques for hand occlusion with direct pen input. Our focus on occlusion follows from a qualitative and quantitative study of direct pen usability with a conventional graphical user interface (GUI). This study reveals overarching problems relating to poor precision, ergonomics, cognitive differences, limited input, and problems resulting from occlusion. To investigate occlusion more closely, we conduct three formal experiments to examine its area and shape, its affect on performance, and compensatory postures. We find that the shape of the occluded area varies across participants with some common characteristics. Our results provide evidence that occlusion affects target selection performance: especially for continuous tasks or when the goal is initially hidden. We observe how users contort their wrist posture during a simultaneous monitoring task, and show this can increase task time. Based on these investigations, we develop a five parameter geometric model to represent the shape of the occluded area and extend this to a user configurable, real-time version. To evaluate our model, we introduce a novel analytic testing methodology using optimization for geometric fitting and precision-recall statistics for comparison; as well as conducting a user study. To address problems with occlusion, we introduce the notion of occlusion-aware interfaces: techniques which can use our configurable model to track currently occluded regions and then counteract potential problems and/or utilize the occluded area. As a case study, we present the Occlusion-Aware Viewer: an interaction technique which displays otherwise missed previews and status messages in a non-occluded area. Within this thesis we also present a number of methodology contributions for quantitative and qualitative study design, multi-faceted study logging using synchronized video, qualitative analysis, image-based analysis, task visualization, optimization-based analytical testing, and user interface image processing

This work received the 2010 Bill Buxton Dissertation Award for best Canadian doctoral dissertation in Human-Computer Interaction (co-recipient)