A Preliminary Experiment on the Estimation of Emotion Using Facial Expression and Biological Signals

Kurono, Yuya; Sripian, Peeraya; Feng, Chen; Sugaya, Midori

doi:10.1007/978-3-030-22643-5_10

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Face expression and voice intonation can be controlled, and may thus conceal the real emotion. Therefore, Kurono et al [ 16 , 17 ] compared the impression of the robot based on the emotion estimated from controllable and uncontrollable expressions. The controllable expressions were derived from facial expression recognition, whereas the uncontrollable expressions were analyzed from brain waves and heart rate measurement.…”

Section: Introductionmentioning

confidence: 99%

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The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

Sripian

Anuardi

et al. 2021

Sensors

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Recently, robot services have been widely applied in many fields. To provide optimum service, it is essential to maintain good acceptance of the robot for more effective interaction with users. Previously, we attempted to implement facial expressions by synchronizing an estimated human emotion on the face of a robot. The results revealed that the robot could present different perceptions according to individual preferences. In this study, we considered individual differences to improve the acceptance of the robot by changing the robot’s expression according to the emotion of its interacting partner. The emotion was estimated using biological signals, and the robot changed its expression according to three conditions: synchronized with the estimated emotion, inversely synchronized, and a funny expression. During the experiment, the participants provided feedback regarding the robot’s expression by choosing whether they “like” or “dislike” the expression. We investigated individual differences in the acceptance of the robot expression using the Semantic Differential scale method. In addition, logistic regression was used to create a classification model by considering individual differences based on the biological data and feedback from each participant. We found that the robot expression based on inverse synchronization when the participants felt a negative emotion could result in impression differences among individuals. Then, the robot’s expression was determined based on the classification model, and the Semantic Differential scale on the impression of the robot was compared with the three conditions. Overall, we found that the participants were most accepting when the robot expression was calculated using the proposed personalized method.

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Section: Introductionmentioning

confidence: 99%

“…In studies by Kurono et al [ 16 , 17 ], the authors compared the impression rating of robot facial expression, which was synchronized from the emotion of the participant. In their work, the emotion was estimated from facial expression and biological signals (heart rate and brain waves).…”

Section: Introductionmentioning

confidence: 99%

The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

Sripian

Anuardi

et al. 2021

Sensors

Self Cite

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“…Besides, this tool already has Noise Cancellation Technologies that can eliminate noise frequencies from other sources such as muscle movements and electrical devices. This filter removes electrical interference that varies from 50 Hz to 60 Hz depending on geographical location [10].…”

Section: Introductionmentioning

confidence: 99%

Recognizing Human Emotion patterns by applying Fast Fourier Transform based on Brainwave Features

Yudhana

Mukhopadhyay²,

Karaş

et al. 2019

2019 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

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The natural ability of humans to receive messages from the surrounding environment can be obtained through the senses. The senses will respond to stimuli received in various conditions including emotional conditions. Psychologically, recognizing human emotions directly can be assessed from several criteria, such as facial expressions, sounds, or body movements. This research aims to analyze human emotions from the biomedical side through brainwave signals using EEG sensors. The EEG signal obtained will be extracted using Fast Fourier Transform and first-order statistical features. four emotional conditions (normal, focus, sadness and shock emotions). The results of this research are expected to help improve users in knowing their mental state accurately. The development of this kind of emotional analysis has the potential to create wide applications in the future environment. Research results have shown and compared frequency stimuli from normal emotions, sadness, focus and shock in a variety of situations. Monitoring of EEG Signals is obtained by grouping based on

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Emotion Synchronization Method for Robot Facial Expression

Kajihara

Sripian

Feng

et al. 2020

Lecture Notes in Computer Science

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A Preliminary Experiment on the Estimation of Emotion Using Facial Expression and Biological Signals

Cited by 6 publications

References 8 publications

The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

The Implementation and Evaluation of Individual Preference in Robot Facial Expression Based on Emotion Estimation Using Biological Signals

Recognizing Human Emotion patterns by applying Fast Fourier Transform based on Brainwave Features

Emotion Synchronization Method for Robot Facial Expression

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