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- 저자 : Yeongji JANG (검색결과 5 건)
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Relationship between personality and emotional responses for children
Eun-Hye Jang,Yeongji Eum,Byoung-Jun Park,Sang-Hyeob Kim,Jin-Hun Sohn 대한인간공학회 2013 대한인간공학회 학술대회논문집 Vol.2013 No.10
Objective: The aim of this study is to identify between personality and psychological responses induced by emotional stimuli (happiness, sadness, anger, boredom and stress) for children. Background: Many researches have interested in that there is close correlation between personality and emotion. The relationship between them has to study in the view of the integrations, not respective property, because personality is deeply ingrained and relatively enduring patterns of thought, feeling and behavior and emotion can take advantage of individual differences in sensitivities to situational cues and predispositions to emotional states. In particular, in that childhood is an important period for formation of their personality and emotion expression and regulation, studies on the personality and emotion for children is necessary. Method: Prior to the experiment, we made parents of 94 children rate personalities of children on Korean Personality Inventory for Children (K-PIC). Results of 64 children without missing answers to all questions were analyzed. 64 children were exposeed to five emotional stimuli and were asked to report the classification and intensity of their experienced emotion. Results: Children were classified into two groups of the lower 25% and higher 25% scores in twenty sub-scales of K-PIC and psychological responses to five emotional stimuli between two groups were compared. Accuracy of emotion experienced by emotional stimuli showed a significant difference between two groups, the lower and higher scores in Delinquency. Also, there was a significant difference in the intensity of experienced emotions between two groups in Intellectual Screening and Psychosis. Conclusion: Our result has showed that Delinquency, Intellectual Screening and Psychosis influence the accuracy and intensity of emotional responses. Application: This study can offer a guideline to overcome methodological limitation of emotion studies for children and help researcher basically understand and recognize human emotion in HCI.
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Discrimination of Three Emotions using Parameters of Autonomic Nervous System Response
Eun-Hye Jang,Byoung-Jun Park,Yeongji Eum,Sang-Hyeob Kim,Jin-Hun Sohn 대한인간공학회 2011 大韓人間工學會誌 Vol.30 No.6
Objective: The aim of this study is to compare results of emotion recognition by several algorithms which classify three different emotional states(happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. While three kinds of emotional stimuli were presented to participants, ANS responses(EDA, SKT, ECG, RESP, and PPG) as physiological signals were measured in twice first one for 60 seconds as the baseline and 60 to 90 seconds during emotional states. The obtained signals from the session of the baseline and of the emotional states were equally analyzed for 30 seconds. Participants rated their own feelings to emotional stimuli on emotional assessment scale after presentation of emotional stimuli. The emotion classification was analyzed by Linear Discriminant Analysis(LDA, SPSS 15.0), Support Vector Machine(SVM), and Multilayer perceptron(MLP) using difference value which subtracts baseline from emotional state. Results: The emotional stimuli had 96% validity and 5.8 point efficiency on average. There were significant differences of ANS responses among three emotions by statistical analysis. The result of LDA showed that an accuracy of classification in three different emotions was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study confirmed that the three emotions can be better classified by LDA using various physiological features than SVM and MLP. Further study may need to get this result to get more stability and reliability, as comparing with the accuracy of emotions classification by using other algorithms. Application: This could help get better chances to recognize various human emotions by using physiological signals as well as be applied on human-computer interaction system for recognizing human emotions.
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Eun-Hye Jang,Yeongji Eum,Sang-Hyeob Kim,Jin-Hun Sohn 대한인간공학회 2011 대한인간공학회 학술대회논문집 Vol.2011 No.5
Objective: The purpose of this study is to identify optimal algorithm for emotion recognition which classify three different emotional states (happiness, neutral, and surprise) using physiological features. Background: Recent emotion recognition studies have tried to detect human emotion by using physiological signals. It is important for emotion reconition to apply on human-computer interaction system for emotion detection. Method: 217 students participated in this experiment. During three different emotional stimuli are presented to participants, ANS responses(EDA, SKT, ECG, Respiration, and PPG) as physiological signals were measured for 1 minute as baseline and for 1-1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state. Participants assessed the induced emotion on emotional assessment scale after emotional stimuli presentation. Analysis for emotion classification were done by linear discriminant analysis (SPSS 15.0), Support Vector Machine (SVM), and Multilayer perceptron (MLP) using substracting baseline values from the emotional state. Results: The emotional stimuli had 96% validity and 5.8 effectiveness on average. The result of linear discriminant analysis using physiological signals showed that an accuracy of three different emotions classification was 83.4%. And an accuracy of three emotions classification by SVM was 75.5% and 55.6% by MLP. Conclusion: This study identified that three emotions were classified by linear discriminant analysis using various physiological features. Future study is needed to obtain stability and reliablity of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion reecognition.
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