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Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction

Muneeb Ahmad Orcid Logo, Abdullah Alzahrani

ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction, Pages: 135 - 143

Swansea University Authors: Muneeb Ahmad Orcid Logo, Abdullah Alzahrani

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DOI (Published version): 10.1145/3577190.3614148

Abstract

Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human's trust in robots in real-time durin...

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Published in: ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction
ISBN: 979-8-4007-0055-2
Published: New York, NY, USA ACM 2023
URI: https://cronfa.swan.ac.uk/Record/cronfa64073
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spelling v2 64073 2023-08-14 Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction 9c42fd947397b1ad2bfa9107457974d5 0000-0001-8111-9967 Muneeb Ahmad Muneeb Ahmad true false d2f9f67e9bfd515f861a917fe1d00321 Abdullah Alzahrani Abdullah Alzahrani true false 2023-08-14 SCS Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human's trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants' electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings. Conference Paper/Proceeding/Abstract ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction 135 143 ACM New York, NY, USA 979-8-4007-0055-2 Trust, Measurement, Psychophysiological behaviours, Human-Robot Interaction, Real-time 9 10 2023 2023-10-09 10.1145/3577190.3614148 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Not Required 2024-04-11T16:27:26.3596443 2023-08-14T16:10:46.1968695 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Muneeb Ahmad 0000-0001-8111-9967 1 Abdullah Alzahrani 2 64073__28295__5175fff3bd524d41a4fd713b68c6606d.pdf ICMI_2023.pdf 2023-08-14T16:13:41.5887227 Output 3287736 application/pdf Accepted Manuscript true Permission to make digital or hard copies of all or part 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. © 2023 Copyright held by the owner/author(s). true eng
title Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
spellingShingle Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
Muneeb Ahmad
Abdullah Alzahrani
title_short Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
title_full Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
title_fullStr Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
title_full_unstemmed Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
title_sort Crucial Clues: Investigating Psychophysiological Behaviors for Measuring Trust in Human-Robot Interaction
author_id_str_mv 9c42fd947397b1ad2bfa9107457974d5
d2f9f67e9bfd515f861a917fe1d00321
author_id_fullname_str_mv 9c42fd947397b1ad2bfa9107457974d5_***_Muneeb Ahmad
d2f9f67e9bfd515f861a917fe1d00321_***_Abdullah Alzahrani
author Muneeb Ahmad
Abdullah Alzahrani
author2 Muneeb Ahmad
Abdullah Alzahrani
format Conference Paper/Proceeding/Abstract
container_title ICMI '23: Proceedings of the 25th International Conference on Multimodal Interaction
container_start_page 135
publishDate 2023
institution Swansea University
isbn 979-8-4007-0055-2
doi_str_mv 10.1145/3577190.3614148
publisher ACM
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department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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description Existing work on the measurements of trust during Human-Robot Interaction (HRI) indicates that psychophysiological behaviours (PBs) have the potential to measure trust. However, we see limited work on the use of multiple PBs in combination to calibrate human's trust in robots in real-time during HRI. Therefore, this study aims to estimate human trust in robots by examining the differences in PBs between trust and distrust states. It further investigates the changes in PBs across repeated HRI and also explores the potential of machine learning classifiers in predicting trust levels during HRI. We collected participants' electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) during repeated HRI. The results showed significant differences in HR and SKT between trust and distrust groups and no significant interaction effect of session and decision for all PBs. Random Forest classifier achieved the best accuracy of 68.6% to classify trust, while SKT, HR, BR, and BD were the important features. These findings highlight the value of PBs in measuring trust in real-time during HRI and encourage further investigation of trust measures with PBs in various HRI settings.
published_date 2023-10-09T16:27:22Z
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