TY - JOUR
T1 - Predicting mid-air gestural interaction with public displays based on audience behaviour
AU - Gentile, Vito
AU - Khamis, Mohamed
AU - Milazzo, Fabrizio
AU - Sorce, Salvatore
AU - Malizia, Alessio
AU - Alt, Florian
N1 - © 2020 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/.
PY - 2020/12
Y1 - 2020/12
N2 - 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.
AB - 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.
KW - Audience behaviour
KW - Pervasive displays
KW - Users behaviour
UR - http://www.scopus.com/inward/record.url?scp=85086713284&partnerID=8YFLogxK
U2 - 10.1016/j.ijhcs.2020.102497
DO - 10.1016/j.ijhcs.2020.102497
M3 - Article
AN - SCOPUS:85086713284
SN - 1071-5819
VL - 144
JO - International Journal of Human Computer Studies
JF - International Journal of Human Computer Studies
M1 - 102497
ER -