Kenan Bektas
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Dr.
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Bektas
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Kenan
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kenan.bektas@unisg.ch
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+41 71 224 27 63
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  • Publication
    Gaze-enabled activity recognition for augmented reality feedback
    ( 2024-03-16)
    Kenan Bektas orcid-logo
    ;
    Jannis Rene Strecker orcid-logo
    ;
    Simon Mayer orcid-logo
    ;
    Kimberly Garcia
    ;
    Andrew Duchowski
    ;
    Krzysztof Krejtz
    Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they provide insights into user attention, intentions, and activities, and allow novel interaction methods based on this information. However, in physical environments, the implications of using gaze-enabled AR for human activity recognition have not been explored in detail. In an experimental study with the Microsoft HoloLens 2, we collected gaze data from 20 users while they performed three activities: Reading a text, Inspecting a device, and Searching for an object. We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved up to 89.6% activity-recognition accuracy. Based on the recognized activity, our system—GEAR—then provides users with relevant AR feedback. Due to the sensitivity of the personal (gaze) data GEAR collects, the system further incorporates a novel solution based on the Solid specification for giving users fine-grained control over the sharing of their data. The provided code and anonymized datasets may be used to reproduce and extend our findings, and as teaching material.
    Type: journal article
    Journal: Computers & Graphics
    Volume: 119
    Issue: Special Section on Eye Gaze VISA
    URL: https://www.sciencedirect.com/science/article/pii/S009784932400044X
    DOI: https://doi.org/10.1016/j.cag.2024.103909
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/119694