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PAWdio: Hand Input for Mobile VR using Acoustic Sensing

Published:15 October 2016Publication History

ABSTRACT

Hand input offers a natural, efficient and immersive form of input for virtual reality (VR), but it has been difficult to implement on mobile VR platforms. Accurate hand-tracking requires a depth sensor and performing computer vision on a smartphone is computationally intensive, which may degrade the frame rate of a VR simulation and drain battery life. PAWdio is a novel 1 degree of freedom (DOF) hand input technique that uses acoustic sensing to track the relative position of an earbud from a headset that the user holds in their hand. PAWdio requires no instrumentation and its low computational overhead assures a high frame rate. A user study with 18 subjects evaluates PAWdio with button input that is commonly available on VR adapters. Results with a 3D target selection task found a similar accuracy and usability, a significantly slower performance, but higher immersion for PAWdio. We discuss limitations and game applications of PAWdio.

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      • Published in

        cover image ACM Conferences
        CHI PLAY '16: Proceedings of the 2016 Annual Symposium on Computer-Human Interaction in Play
        October 2016
        424 pages
        ISBN:9781450344562
        DOI:10.1145/2967934

        Copyright © 2016 ACM

        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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        Publication History

        • Published: 15 October 2016

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        CHI PLAY '16 Paper Acceptance Rate36of124submissions,29%Overall Acceptance Rate421of1,386submissions,30%

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