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Florian Alt, Daniel Buschek, HASH(0x557daeafb428), HASH(0x557daeadb650)
Orbuculum -- Predicting When Users Intend To Leave Large Public Displays
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies. October 01, 2021. Association for Computing Machinery, New York, NY, USA. (bib)

We present a system, predicting the point in time when users of a public display are about to leave. The ability to react to usersâ 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 usersâ intention to leave from observations and video logs. (2) We implemented a system capable of detecting such features from Microsoft Kinectâs 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.