Preliminary Reaction Analysis of Audience with Bio-Emotion Estimation Method

Sugaya, Midori; Hiramatsu, Takuya; Yoshida, Reiji; Feng, Chen

doi:10.1109/compsac.2018.10303

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…In this model the x-axis with positive and negative end-points is called the valence axis, and the y-axis is called the arousal axis. Given that emotions are plotted on an x-y axis, it is possible to quantify each emotion as a vector beginning at the centre of the model (Sugaya et al, 2018).…”

Section: Discussionmentioning

confidence: 99%

“…Subsequently, we report and discuss the results of an empirical user study examining the meanings and emotions conveyed by FB, and emotions experienced by participants in response to FB. This includes a comparison between emotions intended by the designer and the conveyed emotions of FB identified by participants using a vector analysis in Russell's circumplex model of emotions as suggest by (Sugaya et al, 2018) as one way of quantifying comparisons. The comparison offers insights into the possibility of using the framework predictively for design as well as a possible methodology for future comparative analysis.…”

Section: Introductionmentioning

confidence: 99%

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Does bouncy equal happy? Comparing user's interpretations of emotions conveyed by one designed moving object based on the soma-semiotic framework

2021

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Does bouncy equal happy? Comparing user's interpretations of emotions conveyed by one designed moving object based on the soma-semiotic framework

2021

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“…Numerous studies have shown that the emotions estimated using these methods are closely related to the subjective evaluation results obtained in the experiments. These approaches have been used in numerous applications, such as social network feedback analysis, audience reaction analysis, and robot hospitality analysis [ 27 , 28 , 29 , 30 ].…”

Section: Proposed Methodsmentioning

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

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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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“…Hiramatsu et al made a stage support system based on such a method. (17) To determine the stage composition, they proposed that the response of the audience can be understood by estimating their emotions using biological sensors. They used the vector decomposition of the emotion classification method based on biological information.…”

Section: Unconscious Emotion Detection Methodsmentioning

confidence: 99%

Social Networking Services Support Application Based on Biological Information

Feng¹,

Sripian²,

Sugaya³

2020

Sensors and Materials

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These days, Internet services realize computer-mediated social communication as social networking services (SNSs) such as WhatsApp, Facebook, Twitter, and so on, which expand worldwide. Through these SNSs, people express emotions by selecting phrases and characters. These emotional expressions are selected generally on the basis of the user's subjective idea. However, when users lose self-control, they might send inappropriate expressions to others without proper consideration. To avoid such situations, some observable mechanisms that can detect and communicate unconscious emotions to users before they send the message are desired. Recently, some approaches to detecting unconscious emotions using biological sensors have been reported. These approaches provide procedures to make unconscious emotions observable and communicable to users in real time. We combine a biofeedback method with an unconscious emotion estimation method for detecting mismatch between an unconscious emotion and an outward expression before sending the message. Using this idea, we design and implement a system that detects such mismatch and gives feedback to an SNS user. We carry out an experiment using the proposed system. The results show that the system is helpful for the intended purpose but needs improvement.

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Preliminary Reaction Analysis of Audience with Bio-Emotion Estimation Method

Cited by 5 publications

References 4 publications

Does bouncy equal happy? Comparing user's interpretations of emotions conveyed by one designed moving object based on the soma-semiotic framework

Does bouncy equal happy? Comparing user's interpretations of emotions conveyed by one designed moving object based on the soma-semiotic framework

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

Social Networking Services Support Application Based on Biological Information

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