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ID 117312
Author
Akiyama, Toshiya Tokushima University
Tanioka, Ryuichi Tokushima University
Betriana, Feni Tokushima University
Zhao, Yueren Fujita Health University
Kai, Yoshihiro Tokai University
Miyagawa, Misao Tokushima Bunri University
Keywords
schizophrenia
human–robot interaction
multimodal data
multimodal emotion recognition
Content Type
Journal Article
Description
Rapid progress in humanoid robot investigations offers possibilities for improving the competencies of people with social disorders, although this improvement of humanoid robots remains unexplored for schizophrenic people. Methods for creating future multimodal emotional data for robot interactions were studied in this case study of a 40-year-old male patient with disorganized schizophrenia without comorbidities. The qualitative data included heart rate variability (HRV), video-audio recordings, and field notes. HRV, Haar cascade classifier (HCC), and Empath API© were evaluated during conversations between the patient and robot. Two expert nurses and one psychiatrist evaluated facial expressions. The research hypothesis questioned whether HRV, HCC, and Empath API© are useful for creating future multimodal emotional data about robot–patient interactions. The HRV analysis showed persistent sympathetic dominance, matching the human–robot conversational situation. The result of HCC was in agreement with that of human observation, in the case of rough consensus. In the case of observed results disagreed upon by experts, the HCC result was also different. However, emotional assessments by experts using Empath API© were also found to be inconsistent. We believe that with further investigation, a clearer identification of methods for multimodal emotional data for robot interactions can be achieved for patients with schizophrenia.
Journal Title
Healthcare
ISSN
22279032
Publisher
MDPI
Volume
10
Issue
5
Start Page
848
Published Date
2022-05-05
Rights
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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DOI (Published Version)
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language
eng
TextVersion
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departments
Medical Sciences
Science and Technology