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

Published: 19 April 2023 Publication History

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.

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References

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    cover image ACM Conferences
    CHI EA '23: Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems
    April 2023
    3914 pages
    ISBN:9781450394222
    DOI:10.1145/3544549
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    Published: 19 April 2023

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    Author Tags

    1. Ambient displays
    2. agglomerative hierarchical clustering
    3. dynamic time warping
    4. mobility behavior
    5. walking trajectories

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