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Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction

ABDULLAH ALZAHRANI, Muneeb Ahmad Orcid Logo

Human-Computer Interaction – INTERACT 2025: 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8–12, 2025, Proceedings, Part III, Volume: 16110, Pages: 224 - 245

Swansea University Authors: ABDULLAH ALZAHRANI, Muneeb Ahmad Orcid Logo

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DOI (Published version): 10.1007/978-3-032-05005-2_12

Abstract

Existing work on estimating user trust in robotic systems has primarily utilised datasets that monitored variations in physiological behaviours (PBs), evolving from one context of interaction. Consequently,in this paper, we created two datasets from two different human-robot interaction (HRI) contex...

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Published in: Human-Computer Interaction – INTERACT 2025: 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8–12, 2025, Proceedings, Part III
ISBN: 9783032050045 9783032050052
ISSN: 0302-9743 1611-3349
Published: Cham Springer 2026
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69923
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last_indexed 2025-11-05T09:57:53Z
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spelling 2025-11-04T11:26:25.9877658 v2 69923 2025-07-09 Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction fc85729b42b753b90537dd1efb84d3cc ABDULLAH ALZAHRANI ABDULLAH ALZAHRANI true false 9c42fd947397b1ad2bfa9107457974d5 0000-0001-8111-9967 Muneeb Ahmad Muneeb Ahmad true false 2025-07-09 Existing work on estimating user trust in robotic systems has primarily utilised datasets that monitored variations in physiological behaviours (PBs), evolving from one context of interaction. Consequently,in this paper, we created two datasets from two different human-robot interaction (HRI) contexts, namely competitive and collaborative, to explore trust dynamics comprehensively. The datasets consisted of participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) across multiple sessions of collaborative HRI during trust and distrust states. We investigated the differences in PBs between trustand distrust states and explored the potential of incremental transfer learning methods in predicting trust levels during HRI using the two datasets. The findings showed significant differences in HR between trust and distrust groups. It further showed that the Decision Tree classifier achieved the best accuracy of 89% in classifying trust, outperforming the previous work, while HR, BVP, and BR were the important features. Overall, the findings indicate the potential for applying incremental transfer learning to real-time datasets collected from different HRI contexts to estimate trust during HRI. Conference Paper/Proceeding/Abstract Human-Computer Interaction – INTERACT 2025: 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8–12, 2025, Proceedings, Part III 16110 224 245 Springer Cham 9783032050045 9783032050052 0302-9743 1611-3349 Trust; Measurement; Physiological Behaviour; Human-Robot Interaction; Real-time 1 1 2026 2026-01-01 10.1007/978-3-032-05005-2_12 Lecture Notes in Computer Science (LNCS, volume 16110) COLLEGE NANME COLLEGE CODE Swansea University Not Required 2025-11-04T11:26:25.9877658 2025-07-09T08:13:41.2662628 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science ABDULLAH ALZAHRANI 1 Muneeb Ahmad 0000-0001-8111-9967 2 69923__34709__75fa2450545640db88e6c907a794c025.pdf 69923.pdf 2025-07-09T08:16:50.7269932 Output 1256994 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
spellingShingle Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
ABDULLAH ALZAHRANI
Muneeb Ahmad
title_short Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
title_full Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
title_fullStr Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
title_full_unstemmed Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
title_sort Multi-contextual Analysis for Physiological Behaviour for Estimating Trust in Human-Robot Interaction
author_id_str_mv fc85729b42b753b90537dd1efb84d3cc
9c42fd947397b1ad2bfa9107457974d5
author_id_fullname_str_mv fc85729b42b753b90537dd1efb84d3cc_***_ABDULLAH ALZAHRANI
9c42fd947397b1ad2bfa9107457974d5_***_Muneeb Ahmad
author ABDULLAH ALZAHRANI
Muneeb Ahmad
author2 ABDULLAH ALZAHRANI
Muneeb Ahmad
format Conference Paper/Proceeding/Abstract
container_title Human-Computer Interaction – INTERACT 2025: 20th IFIP TC 13 International Conference, Belo Horizonte, Brazil, September 8–12, 2025, Proceedings, Part III
container_volume 16110
container_start_page 224
publishDate 2026
institution Swansea University
isbn 9783032050045
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doi_str_mv 10.1007/978-3-032-05005-2_12
publisher Springer
college_str Faculty of Science and Engineering
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hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
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 estimating user trust in robotic systems has primarily utilised datasets that monitored variations in physiological behaviours (PBs), evolving from one context of interaction. Consequently,in this paper, we created two datasets from two different human-robot interaction (HRI) contexts, namely competitive and collaborative, to explore trust dynamics comprehensively. The datasets consisted of participants’ electrodermal activity (EDA), blood volume pulse (BVP), heart rate (HR), skin temperature (SKT), blinking rate (BR), and blinking duration (BD) across multiple sessions of collaborative HRI during trust and distrust states. We investigated the differences in PBs between trustand distrust states and explored the potential of incremental transfer learning methods in predicting trust levels during HRI using the two datasets. The findings showed significant differences in HR between trust and distrust groups. It further showed that the Decision Tree classifier achieved the best accuracy of 89% in classifying trust, outperforming the previous work, while HR, BVP, and BR were the important features. Overall, the findings indicate the potential for applying incremental transfer learning to real-time datasets collected from different HRI contexts to estimate trust during HRI.
published_date 2026-01-01T05:29:28Z
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