Browsing by Author "Rosenberg, Evan Suma"

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    Comparison of Audio and Visual Cues to Support Remote Guidance in Immersive Environments
    (The Eurographics Association, 2020) Wu, Fei; Thomas, Jerald; Chinnola, Shreyas; Rosenberg, Evan Suma; Argelaguet, Ferran and McMahan, Ryan and Sugimoto, Maki
    Collaborative virtual environments provide the ability for collocated and remote participants to communicate and share information with each other. For example, immersive technologies can be used to facilitate collaborative guidance during navigation of an unfamiliar environment. However, the design space of 3D user interfaces for supporting collaborative guidance tasks, along with the advantages and disadvantages of different immersive communication modalities to support these tasks, are not well understood. In this paper, we investigate three different methods for providing assistance (visual-only, audio-only, and combined audio/visual cues) using an asymmetric collaborative guidance task. We developed a novel experimental design and virtual reality scenario to evaluate task performance during navigation of a complex and dynamic environment while simultaneously avoiding observation by patrolling sentries. Two experiments were conducted: a dyadic study conducted at a large public event and a controlled lab study using a confederate. Combined audio/visual guidance cues were rated easier to use and more effectively facilitated the avoidance of sentries compared with the audio-only condition. The presented work has the potential to inform the design of future experiments and applications that involve communication modalities to support collaborative guidance tasks with immersive technologies.
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    Individualized Calibration of Rotation Gain Thresholds for Redirected Walking
    (The Eurographics Association, 2018) Hutton, Courtney; Ziccardi, Shelby; Medina, Julio; Rosenberg, Evan Suma; Bruder, Gerd and Yoshimoto, Shunsuke and Cobb, Sue
    Redirected walking allows the exploration of large virtual environments within a limited physical space. To achieve this, redirected walking algorithms must maximize the rotation gains applied while remaining imperceptible to the user. Previous research has established population averages for redirection thresholds, including rotation gains. However, these averages do not account for individual variation in tolerance of and susceptibility to redirection. This paper investigates methodologies designed to quickly and accurately calculate rotation gain thresholds for an individual user. This new method is straightforward to implement, requires a minimal amount of space, and takes only a few minutes to estimate a user's personal threshold for rotation gains. Results from a user study support the wide variability in detection thresholds and indicate that the method of parameter estimation through sequential testing (PEST) is viable for efficiently calibrating individual thresholds.