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  • Augmented Reality Based Bee Drift Analysis: A User Study

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    De Souza Junior264891-Accepted.pdf (295.7Kb)
    Author(s)
    Huyen, Nguyen
    Ketchell, Sarah
    Engelke, Ulrich
    Thomas, Bruce H
    de Souza, Paulo
    Griffith University Author(s)
    De Souza Junior, Paulo A.
    Year published
    2017
    Metadata
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    Abstract
    We present HoloBee, a system for visual analytics of bee drift data using a head-mounted augmented reality interface (Microsoft HoloLens). HoloBee facilitates interactive manipulation and exploration of the bee activity data on 3D geo- spatial terrains to gain insights into the drifting behaviour of honey bees in their natural habitat. We conducted a user study to compare the task performance and user experience of HoloBee with a conventional desktop application. The results show that HoloBee allows for solving tasks in a similar time and accuracy compared to the desktop interface, despite participants' having significantly ...
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    We present HoloBee, a system for visual analytics of bee drift data using a head-mounted augmented reality interface (Microsoft HoloLens). HoloBee facilitates interactive manipulation and exploration of the bee activity data on 3D geo- spatial terrains to gain insights into the drifting behaviour of honey bees in their natural habitat. We conducted a user study to compare the task performance and user experience of HoloBee with a conventional desktop application. The results show that HoloBee allows for solving tasks in a similar time and accuracy compared to the desktop interface, despite participants' having significantly more experience interacting with a desktop interface. We received positive feedback from a subset of participants on the natural and intuitive interactions that HoloBee provides, while others preferred using the desktop application. We discuss the implications and limitations of these results and provide guidance for future work in this direction.
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    Conference Title
    2017 International Symposium on Big Data Visual Analytics (BDVA)
    DOI
    https://doi.org/10.1109/BDVA.2017.8114581
    Copyright Statement
    © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subject
    Artificial Intelligence and Image Processing
    Science & Technology
    Computer Science, Cybernetics
    Computer Science, Software Engineering
    Publication URI
    http://hdl.handle.net/10072/401052
    Collection
    • Conference outputs

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