Regular paper
Journal of information and communication convergence engineering 2024; 22(4): 322-329
Published online December 31, 2024
https://doi.org/10.56977/jicce.2024.22.4.322
© Korea Institute of Information and Communication Engineering
The Impact of Backchannel in Medical-Questionnaire Robot on User Perception
Da-Young Kim 
1, Min-Gyu Kim 1, and Sang-Seok Yun2* , Member, KICCE
Correspondence to : Sang-Seok Yun (E-mail: ssyun@silla.ac.kr)
Department of Aeronautical & Mechanical Engineering, Silla University, Busan 46958, Republic of Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Robot backchanneling is implemented using verbal expressions, such as reiterating after remembering the response of the user or chiming in with the user. We performed experiments to investigate how the backchannel behavior of the robot affects the user's perception of the robot's empathy and attentiveness toward their reported pain and sleep concerns. The results indicate that users prefer the robot that provides backchannel and perceive it as more intelligent. Where the robot backchanneled, the number of conversational turns between the user and the robot decreased compared to situations where the robot did not backchannel. The refusal rate of the robot repeating the same question to the user decreased. When a robot is conducting a medical questionnaire and offers backchannels to the user, the overall efficiency of the conversation improves. Users also perceive the robot as understanding and empathetic towards their pain and discomfort, leading to a positive evaluation of the interaction.
Keywords Human-robot interaction, Backchannel, Chatbot, Medical questionnaire
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Regular paper
Journal of information and communication convergence engineering 2024; 22(4): 322-329
Published online December 31, 2024 https://doi.org/10.56977/jicce.2024.22.4.322
Copyright © Korea Institute of Information and Communication Engineering.
The Impact of Backchannel in Medical-Questionnaire Robot on User Perception
Da-Young Kim 1, Min-Gyu Kim 1, and Sang-Seok Yun2* , Member, KICCE
1Human-Robot Interaction Center, Korea Institute of Robotics & Technology Convergence (KIRO), Pohang 37553, Republic of Korea
2Department of Aeronautical & Mechanical Engineering, Silla University, Busan 46958, Republic of Korea
Correspondence to:Sang-Seok Yun (E-mail: ssyun@silla.ac.kr)
Department of Aeronautical & Mechanical Engineering, Silla University, Busan 46958, Republic of Korea
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Robot backchanneling is implemented using verbal expressions, such as reiterating after remembering the response of the user or chiming in with the user. We performed experiments to investigate how the backchannel behavior of the robot affects the user's perception of the robot's empathy and attentiveness toward their reported pain and sleep concerns. The results indicate that users prefer the robot that provides backchannel and perceive it as more intelligent. Where the robot backchanneled, the number of conversational turns between the user and the robot decreased compared to situations where the robot did not backchannel. The refusal rate of the robot repeating the same question to the user decreased. When a robot is conducting a medical questionnaire and offers backchannels to the user, the overall efficiency of the conversation improves. Users also perceive the robot as understanding and empathetic towards their pain and discomfort, leading to a positive evaluation of the interaction.
Keywords: Human-robot interaction, Backchannel, Chatbot, Medical questionnaire
