University of Hertfordshire

From the same journal

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

Research output: Contribution to journalArticlepeer-review

Standard

Predicting mid-air gestural interaction with public displays based on audience behaviour. / Gentile, Vito; Khamis, Mohamed; Milazzo, Fabrizio; Sorce, Salvatore; Malizia, Alessio; Alt, Florian.

In: International Journal of Human Computer Studies, Vol. 144, 102497, 12.2020.

Research output: Contribution to journalArticlepeer-review

Harvard

APA

Vancouver

Author

Gentile, Vito ; Khamis, Mohamed ; Milazzo, Fabrizio ; Sorce, Salvatore ; Malizia, Alessio ; Alt, Florian. / Predicting mid-air gestural interaction with public displays based on audience behaviour. In: International Journal of Human Computer Studies. 2020 ; Vol. 144.

Bibtex

@article{ca9d0d7a5c244d40bb67a80792a7d145,
title = "Predicting mid-air gestural interaction with public displays based on audience behaviour",
abstract = "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{\textquoteright} 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.",
keywords = "Audience behaviour, Pervasive displays, Users behaviour",
author = "Vito Gentile and Mohamed Khamis and Fabrizio Milazzo and Salvatore Sorce and Alessio Malizia and Florian Alt",
note = "{\textcopyright} 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/.",
year = "2020",
month = dec,
doi = "10.1016/j.ijhcs.2020.102497",
language = "English",
volume = "144",
journal = "International Journal of Human-Computer Studies",
issn = "1071-5819",
publisher = "Academic Press Inc.",

}

RIS

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

VL - 144

JO - International Journal of Human-Computer Studies

JF - International Journal of Human-Computer Studies

SN - 1071-5819

M1 - 102497

ER -