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
  • Publication
    GEAR: Gaze-enabled augmented reality for human activity recognition
    (ACM, 2023-05-30)
    Bektas, Kenan orcid-logo
    ;
    Strecker, Jannis Rene orcid-logo
    ;
    Mayer, Simon orcid-logo
    ;
    Garcia, Kimberly
    ;
    Hermann, Jonas
    ;
    Jenss, Kay Erik
    ;
    Antille, Yasmine Sheila orcid-logo
    ;
    Soler, Marc Elias
    Head-mounted Augmented Reality (AR) displays overlay digital information on physical objects. Through eye tracking, they allow novel interaction methods and provide insights into user attention, intentions, and activities. However, only few studies have used gaze-enabled AR displays for human activity recognition (HAR). In an experimental study, we collected gaze data from 10 users on a HoloLens 2 (HL2) while they performed three activities (i.e., read, inspect, search). We trained machine learning models (SVM, Random Forest, Extremely Randomized Trees) with extracted features and achieved an up to 98.7% activity-recognition accuracy. On the HL2, we provided users with an AR feedback that is relevant to their current activity. We present the components of our system (GEAR) including a novel solution to enable the controlled sharing of collected data. We provide the scripts and anonymized datasets which can be used as teaching material in graduate courses or for reproducing our findings.
    Type: conference paper
    DOI: https://doi.org/10.1145/3588015.3588402
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107618
  • Publication
    Pupillometry for Measuring User Response to Movement of an Industrial Robot
    ( 2023-05-30)
    Damian Hostettler
    ;
    Kenan Bektas orcid-logo
    ;
    Simon Mayer orcid-logo
    Interactive systems can adapt to individual users to increase productivity, safety, or acceptance. Previous research focused on different factors, such as cognitive workload (CWL), to better understand and improve the human-computer or human-robot interaction (HRI). We present results of an HRI experiment that uses pupillometry to measure users' responses to robot movements. Our results demonstrate a significant change in pupil dilation, indicating higher CWL, as a result of increased movement speed of an articulated robot arm. This might permit improved interaction ergonomics by adapting the behavior of robots or other devices to individual users at run time. CCS CONCEPTS • Human-centered computing → Ubiquitous and mobile computing systems and tools.
    Type: conference contribution
    DOI: https://doi.org/10.1145/3588015.3590123
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/117306