Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots

Strathearn, Carl and Ma, Minhua ORCID logoORCID: https://orcid.org/0000-0001-7451-546X (2020) Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots. Modelling User Preference for Embodied Artificial Intelligence and Appearance in Realistic Humanoid Robots, 7, 28. ISSN 1816-0301

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Official URL: https://www.mdpi.com/2227-9709/7/3/28

Abstract / Summary

Realistic humanoid robots (RHRs) with embodied artificial intelligence (EAI) have numerous applications in society as the human face is the most natural interface for communication and the human body the most effective form for traversing the manmade areas of the planet. Thus, developing RHRs with high degrees of human-likeness provides a life-like vessel for humans to physically and naturally interact with technology in a manner insurmountable to any other form of non-biological human emulation. This study outlines a human–robot interaction (HRI) experiment employing two automated RHRs with a contrasting appearance and personality. The selective sample group employed in this study is composed of 20 individuals, categorised by age and gender for a diverse statistical analysis. Galvanic skin response, facial expression analysis, and AI analytics permitted cross-analysis of biometric and AI data with participant testimonies to reify the results. This study concludes that younger test subjects preferred HRI with a younger-looking RHR and the more senior age group with an older looking RHR. Moreover, the female test group preferred HRI with an RHR with a younger appearance and male subjects with an older looking RHR. This research is useful for modelling the appearance and personality of RHRs with EAI for specific jobs such as care for the elderly and social companions for the young, isolated, and vulnerable.

Item Type: Article
ISSN: 1816-0301
Subjects: Computer Science, Information & General Works
Courses by Department: The Games Academy > Computing for Games
Depositing User: Rebecca Takeda-Frost
Date Deposited: 08 Oct 2021 08:26
Last Modified: 08 Oct 2021 08:26
URI: http://repository.falmouth.ac.uk/id/eprint/4404

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