Emotion Synchronization Method for Robot Facial Expression

Kajihara, Yushun; Sripian, Peeraya; Feng, Chen; Sugaya, Midori

doi:10.1007/978-3-030-49062-1_44

Cited by 4 publications

(6 citation statements)

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“…Kajihara et al suggested that real-time emotion estimation at an interval of 2.5 s produced the highest rating of the impression of a robot [ 18 , 19 ]. Therefore, in this research, we used the approach proposed by Kajihara et al to estimate the emotions using biological signals, and thus regularly determine the robot facial expressions under three conditions: synchronize, reverse synchronize, and funny facial expressions.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In this study, the emotions were estimated from biological signals based on the brain wave and heart rate measurement analysis of Ikeda et al [ 10 ]. Because the brain waves and heart rate can be measured continuously in real time [ 20 , 21 , 22 ], it is possible to change the robot facial expressions based on the estimated emotions by applying the methods from both Kajihara et al [ 18 , 19 ] and Ikeda et al [ 10 ]. If the robot is able to change and express emotions in real time during communication, the situation will more closely resemble human–human communication.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The emotion estimation methods in [ 10 , 18 , 19 ] correlate the values obtained by two physiological sensors, i.e., the brain waves (EEG) and heart rate variability (HRV), to the Arousal and Valence axes, respectively, in Russell’s circumplex model of affect [ 23 ]. The EEG can be used to measure the state of concentration and is reported to be negatively correlated with the subjectively evaluated level of arousal [ 24 , 25 ].…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The study of Kajihara et al examined four types of robot facial expression based on the four basic emotions [ 18 ]. In addition to the four basic emotions, we added another type of expression to clarify the individual differences in the impression of facial expressions.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…They found that the emotion estimated from biological signals, which was derived from uncontrollable expressions, resulted in a high concordance rate. Subsequently, Kajihara et al [ 18 ] expanded the work to determine a suitable synchronization method for the robot expression and the emotion estimation approach of uncontrollable expressions. In Kajihara et al’s later work [ 19 ], they compared the participant impression rating of the robot facial expression under two conditions: synchronized and inversely synchronized with the estimated emotion.…”

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: Proposed Methodsmentioning

confidence: 99%