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Auflistung nach Autor:in "Chiossi, Francesco"

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  • Zeitschriftenartikel
    Adapting visualizations and interfaces to the user
    (it - Information Technology: Vol. 64, No. 4-5, 2022) Chiossi, Francesco; Zagermann, Johannes; Karolus, Jakob; Rodrigues, Nils; Balestrucci, Priscilla; Weiskopf, Daniel; Ehinger, Benedikt; Feuchtner, Tiare; Reiterer, Harald; Chuang, Lewis L.; Ernst, Marc; Bulling, Andreas; Mayer, Sven; Schmidt, Albrecht
    Adaptive visualization and interfaces pervade our everyday tasks to improve interaction from the point of view of user performance and experience. This approach allows using several user inputs, whether physiological, behavioral, qualitative, or multimodal combinations, to enhance the interaction. Due to the multitude of approaches, we outline the current research trends of inputs used to adapt visualizations and user interfaces. Moreover, we discuss methodological approaches used in mixed reality, physiological computing, visual analytics, and proficiency-aware systems. With this work, we provide an overview of the current research in adaptive systems.
  • Zeitschriftenartikel
    Broadening the mind: how emerging neurotechnology is reshaping HCI and interactive system design
    (i-com: Vol. 23, No. 2, 2024) Schneegass, Christina; Wilson, Max L.; Shaban, Jwan; Niess, Jasmin; Chiossi, Francesco; Mitrevska, Teodora; Woźniak, Paweł W.
    People are increasingly eager to know more about themselves through technology. To date, technology has primarily provided information on our physiology. Yet, with advances in wearable technology and artificial intelligence, the current advent of consumer neurotechnology will enable users to measure their cognitive activity. We see an opportunity for research in Human-Computer Interaction (HCI) in the development of these devices. Neurotechnology offers new insights into user experiences and facilitates the development of novel methods in HCI. Researchers will be able to create innovative interactive systems based on the ability to measure cognitive activity at scale in real-world settings. In this paper, we contribute a vision of how neurotechnology will transform HCI research and practice. We discuss how neurotechnology prompts a discussion about ethics, privacy, and trust. This trend highlights HCI’s crucial role in ensuring that neurotechnology is developed and utilised in ways that truly benefit people.
  • Konferenzbeitrag
    Crossing Mixed Realities: A Review for Transitional Interfaces Design
    (Proceedings of Mensch und Computer 2024, 2024) Mayer, Elisabeth; Chiossi, Francesco; Mayer, Sven
    Transitioning seamlessly from the real world into the digital world through the mixed reality continuum remains challenging. This paper investigates transitional design principles across the MR spectrum, anchored by a review of “The MagicBook”, a pioneering work that introduced the concept of transitional interfaces to the HCI community. Employing a forward-backward method, we reviewed 309 publications to understand the landscape of MR transitions. Our analysis outlines four distinct transition types within MR environments, offering a novel classification scheme. From this literature corpus, we identify four categories, setting a foundation for UX evaluation of transitional interfaces.
  • Konferenzbeitrag
    Multimodal Detection of External and Internal Attention in Virtual Reality using EEG and Eye Tracking Features
    (Proceedings of Mensch und Computer 2024, 2024) Long, Xingyu; Mayer, Sven; Chiossi, Francesco
    Future VR environments will sense users’ context, enabling a wide range of intelligent interactions, thus enabling diverse applications and improving usability through attention-aware VR systems. However, attention-aware VR systems based on EEG data suffer from long training periods, hindering generalizability and widespread adoption. At the same time, there remains a gap in research regarding which physiological features (EEG and eye tracking) are most effective for decoding attention direction in the VR paradigm. We addressed this issue by evaluating several classification models using EEG and eye tracking data. We recorded that training data simultaneously during tasks that required internal attention in an N-Back task or external attention allocation in Visual Monitoring. We used linear and deep learning models to compare classification performance under several uni- and multimodal feature sets alongside different window sizes. Our results indicate that multimodal features improve prediction for classical and modern classification models. We discuss approaches to assess the importance of physiological features and achieve automatic, robust, and individualized feature selection.