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This research proposes a comprehensive multimodal dataset integrating physiological signals, behavioral data, and self-reports to better analyze user engagement with humanoid robots. By moving beyond simple observable cues, the study aims to refine human-robot interaction frameworks under varying task complexities. This work is significant for advancing the understanding of HRI dynamics through richer data collection protocols.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

Establishing a Dynamic Multimodal HRI Dataset for Engagement Analysis with a Humanoid Robot

arXiv1 min read

arXiv:2609.03255v1 Announce Type: new Abstract: This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human-robot interaction (HRI). Prior studies have mainly relied on observable behavioral cues, with limited frameworks integrating physiological signals. We therefore propose a structured data-collection protocol to build a multimodal dataset that includes wearable physiological signals, behavioral data, and self-report measures under different levels of task complexity defined in this experiment.

Abstract:This paper presents an experimental design for constructing a multimodal dataset to analyze user engagement in human-robot interaction (HRI). Prior studies have mainly relied on observable behavioral cues, with limited frameworks integrating physiological signals. We therefore propose a structured data-collection protocol to build a multimodal dataset that includes wearable physiological signals, behavioral data, and self-report measures under different levels of task complexity defined in this experiment.

Robotics (cs.RO)

arXiv:2609.03255 [cs.RO]

(or arXiv:2609.03255v1 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2609.03255 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 159 words

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