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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16710

Title: Understanding Nonverbal Communication Cues of Human Personality Traits in Human-Robot Interaction
Authors: Shen, Zhihao
Elibol, Armagan
Chong, Nak Young
Keywords: human-robot interaction
nonverbal communication cues
personality traits
machine learning
Issue Date: 2020-06-02
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: IEEE/CAA Journal of Automatica Sinica
DOI: 10.1109/JAS.2020.1003201
Abstract: With the increasing presence of robots in our daily life, there is a strong need and demand for the strategies to acquire a high quality interaction between robots and users by enabling robots to understand usersʼ mood, intention, and other aspects. During human-human interaction, personality traits have an important influence on human behavior, decision, mood, and many others. Therefore, we propose an efficient computational framework to endow the robot with the capability of understanding the userʼ s personality traits based on the userʼ s nonverbal communication cues represented by three visual features including the head motion, gaze, and body motion energy, and three vocal features including voice pitch, voice energy, and mel-frequency cepstral coefficient ( MFCC ) . We used the Pepper robot in this study as a communication robot to interact with each participant by asking questions, and meanwhile, the robot extracts the nonverbal features from each participantʼ s habitual behavior using its on-board sensors. On the other hand, each participantʼ s personality traits are evaluated with a questionnaire. We then train the ridge regression and linear support vector machine ( SVM ) classifiers using the nonverbal features and personality trait labels from a questionnaire and evaluate the performance of the classifiers. We have verified the validity of the proposed models that showed promising binary classification performance on recognizing each of the Big Five personality traits of the participants based on individual differences in nonverbal communication cues.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. IEEE/CAA Journal of Automatica Sinica, 2020, DOI:10.1109/JAS.2020.1003201. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/16710
Material Type: author
Appears in Collections:b10-1. 雑誌掲載論文 (Journal Articles)

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