Exploring Mobility Behavior Around Ambient Displays Using Clusters of Multi-dimensional Walking Trajectories

Abstract

Spatial information has become crucial in ambient display research and helps to better understand how people behave in a display's vicinity. Walking trajectories have long been used to uncover such information and tools have been developed to capture them anonymously and automatically. However, more research is needed on the level of automation during mobility behavior analyses. Particularly, working with depth-based skeletal data still requires significant manual effort to, for instance, determine walking trajectories similar in shape. To advance on this situation, we adopt both agglomerative hierarchical clustering and dynamic time warping in this research. To the best of our knowledge, both algorithms have so far not found application in our field. Using a multi-dimensional data set obtained from a longitudinal, real-world deployment, we demonstrate here the applicability and usefulness of this approach. In doing so, we contribute insightful ideas for future discussions on the methodological development in ambient display research.