Beyond heat maps - Mining common swipe gestures

Heat maps are a common tool in research to visualize humancomputer- interaction. Despite being widely used to track navigation, clicks, cursor moves or eye gaze, heat maps have not yet been explored as a means to understand users' gestural interaction with mobile devices. This understanding is particularly relevant in the case of older adult users who are often novice users and may also struggle with accuracy in gesture performance. This paper explores the application of the DBScan clustering algorithm to uncover the most relevant swipe gestures in a data sets containing the user interaction of two mobile applications. An intuitive visualization of the clustering results will be presented and compared in a case study with a heat map visualization, discussing the novelty and usefulness of these visualizations for user behaviour and usability studies.