2018/06/18 by Christoph Anderson, Isabel Hübener, Ann-Kathrin Seipp +3 · 1 citation
Computer Science · Decision Sciences · #Context-aware pervasive systems #Focus (optics) #Gaze Tracking and Assistive Technology #Information management #Information system #Key (lock) #Management information systems #Personal Information Management and User Behavior #Personal information management #Point (geometry) #Ubiquitous computing #Visual Attention and Saliency Detection #cs.HC
paper · pdf · doi:10.1145/3214261
published as Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 2, no. 2, pp. 58:1-58:27, June 2018 · 27 pages, 7 figures
arxiv created 2018/06/18 · arxiv updated 2018/06/19 · openalex created_date 2018/06/21 · openalex publication_date 2018/07/05 · openalex updated_date 2026/08/05
Today's information and communication devices provide always-on connectivity, instant access to an endless repository of information, and represent the most direct point of contact to almost any person in the world. Despite these advantages, devices such as smartphones or personal computers lead to the phenomenon of attention fragmentation, continuously interrupting individuals' activities and tasks with notifications. Attention management systems aim to provide active support in such scenarios, managing interruptions, for example, by postponing notifications to opportune moments for information delivery. In this article, we review attention management system research with a particular focus on ubiquitous computing environments. We first examine cognitive theories of attention and extract guidelines for practical attention management systems. Mathematical models of human attention are at the core of these systems, and in this article, we review sensing and machine learning techniques that make such models possible. We then discuss design challenges towards the implementation of such systems, and finally, we investigate future directions in this area, paving the way for new approaches and systems supporting users in their attention management.