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Robot-Mediated Multi-Party Conversation Aimed at Affect Improvement for Psychiatric Patients
Author(s) -
Keiko Ochi,
Divesh Lala,
Koji Inoue,
Tatsuya Kawahara,
Hirokazu Kumazaki
Publication year - 2025
Publication title -
ieee transactions on affective computing
Language(s) - English
Resource type - Magazines
SCImago Journal Rank - 1.309
H-Index - 67
eISSN - 1949-3045
DOI - 10.1109/taffc.2025.3571427
Subject(s) - computing and processing , robotics and control systems , signal processing and analysis
This paper describes a multi-party attentive listening system that interacts with two persons to familiarize each other through conversation. We are mainly targeting social implementation in hospitals to contribute to the rehabilitation of people with psychiatric disorders to promote affect improvement in terms of pleasure and arousal. We conducted an experiment in a psychiatric outpatient-daycare program. Twenty daycare attendees participated in a three-party conversation session between a pair of two humans and a humanoid robot. One of the paired participants talked about his/her favorite topic and was attentively listened to by the other and the robot. In a subsequent session, the human pairs switched each other's roles. The subjective evaluations showed that both the pleasure and arousal of the participants were significantly improved after the conversation. The participants rated the impression of the robot as easier to talk with than strangers. They also rated that they could understand and feel familiar with significantly more their human talk partner after the conversational session. The multiple linear regression analysis showed that participants became more pleasant and verbal when stimulated by backchannels and questions from both the human listener and the robot. It suggests that those who expressed themself using more words received positive impressions.

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