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In eye-tracked augmented and virtual reality (AR/VR), instantaneous and accurate hands-free selection of virtual elements is still a significant challenge. Though other methods that involve gaze-coupled head movements or hovering can improve selection times in comparison to methods like gaze-dwell, they are either not instantaneous or have difficulty ensuring that the user’s selection is deliberate. In this paper, we present EyeShadows, an eye gaze-based selection system that takes advantage of peripheral copies (shadows) of items that allow for quick selection and manipulation of an object or corresponding menus. This method is compatible with a variety of different selection tasks and controllable items, avoids the Midas touch problem, does not clutter the virtual environment, and is context sensitive. We have implemented and refined this selection tool for VR and AR, including testing with optical and video see-through (OST/VST) displays. Moreover, we demonstrate that this method can be used for a wide range of AR and VR applications, including manipulation of sliders or analog elements. We test its performance in VR against three other selection techniques, including dwell (baseline), an inertial reticle, and head-coupled selection. Results showed that selection with EyeShadows was significantly faster than dwell (baseline), outperforming in the select and search and select tasks by 29.8% and 15.7%, respectively, though error rates varied between tasks. 

Many researchers and industry professionals believe Augmented Reality (AR) to be the next step in personal computing. However, the idea of an always-on context-aware AR device presents new and unique challenges to the way users organize multiple streams of information. What does multitasking look like and when should applications be tied to specific elements in the environment? In this exploratory study, we look at one such element: physical objects, and explore an object-centric approach to multitasking in AR. We developed 3 prototype applications that operate on a subset of objects in a simulated test environment. We performed a pilot study of our multitasking solution with a novice user, domain expert, and system expert to develop insights into the future of AR application design.