A Survey of Attention Management Systems in Ubiquitous Computing Environments

TL;DR

This survey reviews attention management systems in ubiquitous computing, focusing on cognitive theories, mathematical models, sensing, machine learning, design challenges, and future directions to support user attention amidst constant device interruptions.

Contribution

It provides a comprehensive overview of existing research, frameworks, and challenges in developing attention management systems for ubiquitous computing environments.

Findings

Mathematical models of human attention are central to system design.

Sensing and machine learning techniques enable personalized attention management.

Future research directions include new approaches for seamless attention support.

Abstract

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.