HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRI

2020
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Authors: Austin Kothig; John Muñoz; Hamza Mahdi; Alexander M. Aroyo; Kerstin Dautenhahn
Published in: Social Robotics
Publisher: Springer International Publishing

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Abstract

The rise of available physiological sensors in recent years can be attributed to the widespread popularity of commercial sensing technologies equipped with health monitoring technology. To make more engaging human-robot interaction (HRI), social robots should have some ability to infer their social partner’s affective state. Measurements of the autonomic nervous system via non-invasive physiological sensors provide a convenient window into a person’s affective state, namely their emotions, behavior, stress, and engagement. HRI research has included physiological sensors in-the-loop, however implementations are often specific to studies, and do not lend well for reusability. To address this gap, we propose a modular, flexible and extensible framework designed to work with popular robot platforms. Our framework will be compatible with both lab and consumer grade sensors, and includes essential tools and processing algorithms for affective state estimation geared towards real-time HRI applications.

Notes

This research was undertaken, in part, thanks to funding from the Canada 150 Research Chairs Program and thanks to Alexander Graham Bell Canada Graduate Scholarships.

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Metadata
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Title: HRI Physio Lib: A Software Framework to Support the Integration of Physiological Adaptation in HRI
Authors: Austin Kothig
John Muñoz
Hamza Mahdi
Alexander M. Aroyo
Kerstin Dautenhahn
Publisher: Springer International Publishing
Book: Social Robotics
Print ISBN: 978-3-030-62055-4

Electronic ISBN: 978-3-030-62056-1

Copyright Year: 2020
DOI: https://doi.org/10.1007/978-3-030-62056-1_4

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