Predicting mid-air gestural interaction with public displays based on audience behaviour

Gentile, V., Khamis, M. , Milazzo, F., Sorce, S., Malizia, A. and Alt, F. (2020) Predicting mid-air gestural interaction with public displays based on audience behaviour. International Journal of Human-Computer Studies, 144, 102497. (doi: 10.1016/j.ijhcs.2020.102497)

[img] Text
218340.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.



Knowledge about the expected interaction duration and expected distance from which users will interact with public displays can be useful in many ways. For example, knowing upfront that a certain setup will lead to shorter interactions can nudge space owners to alter the setup. If a system can predict that incoming users will interact at a long distance for a short amount of time, it can accordingly show shorter versions of content (e.g., videos/advertisements) and employ at-a-distance interaction modalities (e.g., mid-air gestures). In this work, we propose a method to build models for predicting users’ interaction duration and distance in public display environments, focusing on mid-air gestural interactive displays. First, we report our findings from a field study showing that multiple variables, such as audience size and behaviour, significantly influence interaction duration and distance. We then train predictor models using contextual data, based on the same variables. By applying our method to a mid-air gestural interactive public display deployment, we build a model that predicts interaction duration with an average error of about 8 s, and interaction distance with an average error of about 35 cm. We discuss how researchers and practitioners can use our work to build their own predictor models, and how they can use them to optimise their deployment.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Khamis, Dr Mohamed
Creator Roles:
Khamis, M.Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review and editing, Visualization
Authors: Gentile, V., Khamis, M., Milazzo, F., Sorce, S., Malizia, A., and Alt, F.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:International Journal of Human-Computer Studies
ISSN (Online):1095-9300
Published Online:13 June 2020
Copyright Holders:Copyright © 2020 Elsevier Ltd.
First Published:First published in International Journal of Human-Computer Studies 144: 102497
Publisher Policy:Reproduced in accordance with the publisher copyright policy

University Staff: Request a correction | Enlighten Editors: Update this record