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Affective Evaluation of Virtual Kawaii Robotic Gadgets Using Biological Signals in a Remote Collaboration of American and Japanese Students

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Human-Computer Interaction. Theoretical Approaches and Design Methods (HCII 2022)

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Abstract

This paper describes our remote collaboration project related to the design and implementation of virtual kawaii robots by Japanese and American university students, and affective evaluation of the robots. Because of the COVID-19 pandemic, we had to change our planned 7-week collaboration from in-person to virtual with a resultant change in the target product of our collaboration from real robots to virtual robots. Based on our new plan for 2021, students designed robots in virtual spaces aiming that each robot elicited a different Electroencephalogram (EEG) and/or Heart Rate (HR) reaction from humans. Based on the persona and scenario for the companion robot authored by each student team, each student designed four robots with the goal that one robot would be most kawaii, a Japanese adjective representing cute and adorable, and others would be less kawaii due to variations in shapes and colors. The affective evaluation of robots was performed both by biological signals (EEG and HR) and by kawaii rating.

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Acknowledgement

This material is based upon work supported by the National Science Foundation under Grant No. OISE-1854255. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We thank all the participating students: G. Todd, H. Schwipps, N. Jadram, S. Imura, L. Guinee, R. Martinez, N. S. Fong and S. Ohtsuka.

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Correspondence to Michiko Ohkura .

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Ohkura, M., Laohakangvalvit, T., Sripian, P., Sugaya, M., Chiba, H., Berque, D. (2022). Affective Evaluation of Virtual Kawaii Robotic Gadgets Using Biological Signals in a Remote Collaboration of American and Japanese Students. In: Kurosu, M. (eds) Human-Computer Interaction. Theoretical Approaches and Design Methods. HCII 2022. Lecture Notes in Computer Science, vol 13302. Springer, Cham. https://doi.org/10.1007/978-3-031-05311-5_33

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  • DOI: https://doi.org/10.1007/978-3-031-05311-5_33

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