Discovering Types of Smartphone Usage Sessions from User-App InteractionsIn this paper, we examine how embedding physical user-app activity (e.g., taps and scrolls) can provide a rich basis for summarising device usage. Using a large dataset of 82,758,449 interaction events from 86 users over an 8-week period we combine feature embedding and unsupervised learning to extract prominent interactions within clusters of smartphone usage sessions.

Utilising the co-occurrence of user interface interactions as a risk indicator for smartphone addictionThe study highlights a novel methodology to transform and analyse large amounts of interaction events to infer a user's level of smartphone addiction. This is a step forward from using commonly used metrics such as pure screen on time which can misrepresent the cognitive complexities and dependencies of human behaviour.