• Medientyp: E-Artikel
  • Titel: Orbuculum - Predicting When Users Intend to Leave Large Public Displays
  • Beteiligte: Alt, Florian; Buschek, Daniel; Heuss, David; Müller, Jörg
  • Erschienen: Association for Computing Machinery (ACM), 2021
  • Erschienen in: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
  • Sprache: Englisch
  • DOI: 10.1145/3448075
  • ISSN: 2474-9567
  • Schlagwörter: Computer Networks and Communications ; Hardware and Architecture ; Human-Computer Interaction
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  • Beschreibung: <jats:p>We present a system, predicting the point in time when users of a public display are about to leave. The ability to react to users' intention to leave is valuable for researchers and practitioners alike: users can be presented additional content with the goal to maximize interaction times; they can be offered a discount coupon for redemption in a nearby store hence enabling new business models; or feedback can be collected from users right after they have finished interaction without interrupting their task. Our research consists of multiple steps: (1) We identified features that hint at users' intention to leave from observations and video logs. (2) We implemented a system capable of detecting such features from Microsoft Kinect's skeleton data and subsequently make a prediction. (3) We trained and deployed a prediction system with a Quiz game which reacts when users are about to leave (N=249), achieving an accuracy of 78%. The majority of users indeed reacted to the presented intervention.</jats:p>