Towards Richer Insights in Human-Centred Observation Studies: Combining Thematic Analysis and Computer Vision

Liu, Y; Caldwell, GA; Rittenbruch, M; Guertler, M; Teixeira, M; Burden, AG

Towards Richer Insights in Human-Centred Observation Studies: Combining Thematic Analysis and Computer Vision

Liu, Y Caldwell, GA Rittenbruch, M Guertler, M

Teixeira, M Burden, AG

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Publisher:: Association for Computing Machinery (ACM)
Publication Type:: Conference Proceeding
Citation:: Proceedings of the 37th Australian Conference on Human-Computer Interaction, 2025, pp. 792-800
Issue Date:: 2025-11-29

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Field	Value	Language
dc.contributor.author	Liu, Y
dc.contributor.author	Caldwell, GA
dc.contributor.author	Rittenbruch, M
dc.contributor.author	Guertler, M https://orcid.org/0000-0001-7062-6349
dc.contributor.author	Teixeira, M
dc.contributor.author	Burden, AG
dc.date.accessioned	2026-01-07T02:05:25Z
dc.date.available	2026-01-07T02:05:25Z
dc.date.issued	2025-11-29
dc.identifier.citation	Proceedings of the 37th Australian Conference on Human-Computer Interaction, 2025, pp. 792-800
dc.identifier.uri	http://hdl.handle.net/10453/191393
dc.description.abstract	Due to the precision and efficiency that collaborative robots (cobots) offer, they are becoming increasingly vital to advanced manufacturing, particularly for dynamic and complex tasks that depend on human intelligence, such as decision making. To support cobot adoption, this paper presents a novel method for analyzing human decision-making and task complexity using video observation. Moving beyond conventional video analysis, the proposed approach combines thematic analysis with computer vision-based motion recognition to reveal behavioral patterns and decision-making processes. Through the application in a real-world manufacturing gasket room task, we demonstrate how the integration of interpretive coding and computational motion data can uncover insights into task structure, decision points, and potential cobot intervention zones. This method contributes a practical tool for aligning cobot functions with human needs in complex settings. It demonstrated how generative intelligence can augment human-centered research, and inform the design of future collaborative system that aligned with planetary sustainability.
dc.language	en
dc.publisher	Association for Computing Machinery (ACM)
dc.relation.ispartof	Proceedings of the 37th Australian Conference on Human-Computer Interaction
dc.relation.ispartof	Proceedings of the 37th Australian Conference on Human-Computer Interaction
dc.relation.isbasedon	10.1145/3764687.3769930
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Towards Richer Insights in Human-Centred Observation Studies: Combining Thematic Analysis and Computer Vision
dc.type	Conference Proceeding
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Manufacturing (CAM)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Robotics Institute (RI)/Robotics Institute (RI) Associate Members
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-01-07T02:05:23Z
pubs.publication-status	Published

Abstract:

Due to the precision and efficiency that collaborative robots (cobots) offer, they are becoming increasingly vital to advanced manufacturing, particularly for dynamic and complex tasks that depend on human intelligence, such as decision making. To support cobot adoption, this paper presents a novel method for analyzing human decision-making and task complexity using video observation. Moving beyond conventional video analysis, the proposed approach combines thematic analysis with computer vision-based motion recognition to reveal behavioral patterns and decision-making processes. Through the application in a real-world manufacturing gasket room task, we demonstrate how the integration of interpretive coding and computational motion data can uncover insights into task structure, decision points, and potential cobot intervention zones. This method contributes a practical tool for aligning cobot functions with human needs in complex settings. It demonstrated how generative intelligence can augment human-centered research, and inform the design of future collaborative system that aligned with planetary sustainability.

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http://hdl.handle.net/10453/191393