Reading is central to learning and communicating, however, divided attention in the form of distraction may be present in learning environments, resulting in a limited understanding of the reading material. This paper presents a novel system that can spatio-temporally detect divided attention in users during two different reading applications: typical document reading and speed reading. Eye tracking and electroencephalography (EEG) monitor the user during reading and provide a classifier with data to decide the user's attention state. The multimodal data informs the system where the user was distracted spatially in the user interface and when the user was distracted. Classification was evaluated with two exploratory experiments. The first experiment was designed to divide the user's attention with a multitasking scenario. The second experiment was designed to divide the users attention by simulating a real-world scenario where the reader is interrupted by unpredictable audio distractions. Results from both experiments show that divided attention may be detected spatiotemporally well above chance on a single-trial basis.
This paper presents the design and implementation of a system for simulating mixed reality in setups combining mobile devices and large backdrop displays. With a mixed reality simulator, one can perform usability studies and evaluate mixed reality systems while minimizing confounding variables. This paper describes how mobile device AR design factors can be flexibly and systematically explored without sacrificing the touch and direct unobstructed manipulation of a physical personal MR display. First, we describe general principles to consider when implementing a mixed reality simulator, enumerating design factors. Then, we present our implementation which utilizes personal mobile display devices in conjunction with a large surround-view display environment. Standing in the center of the display, a user may direct a mobile device, such as a tablet or head-mounted display, to a portion of the scene, which affords them a potentially annotated view of the area of interest. The user may employ gesture or touch screen interaction on a simulated augmented camera feed, as they typically would in videosee-through mixed reality applications. We present calibration and system performance results and illustrate our system's flexibility by presenting the design of three usability evaluation scenarios.
We present a method for predicting articulated hand poses in realtime with a single depth camera, such as the Kinect or Xtion Pro, for the purpose of interaction in a Mixed Reality environment and for studying the effects of realistic and non-realistic articulated hand models in a Mixed Reality simulator. We demonstrate that employing a randomized decision forest for hand recognition benefits realtime applications without the typical tracking pitfalls such as reinitialization. This object recognition approach to predict hand poses results in relatively low computation, high prediction accuracy and sets the groundwork needed to utilize articulated hand movements for 3D tasks in Mixed Reality workspaces.
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