@Article{alt2021imwut,
  author     = {Florian Alt AND Daniel Buschek AND David Heuss AND J\"{o}rg M\"{u}ller},
  journal    = {{Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies}},
  title      = {{Orbuculum -- Predicting When Users Intend To Leave Large Public Displays}},
  year       = {2021},
  note       = {alt2021imwut},
  number     = {1},
  volume     = {5},
  abstract   = {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 that reacts when users are about to leave (N=249), achieving an accuracy of 78%. The majority of users indeed reacted to the presented intervention.},
  address    = {New York, NY, USA},
  articleno  = {47},
  doi        = {10.1145/3448075},
  issue_date = {Mar 2021},
  numpages   = {24},
  publisher  = {Association for Computing Machinery},
  timestamp  = {2021.10.01},
  url        = {http://florian-alt.org/unibw/wp-content/publications/alt2021imwut.pdf},
}