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http://dx.doi.org/10.13088/jiis.2013.19.1.079

A Study of the Reactive Movement Synchronization for Analysis of Group Flow  

Ryu, Joon Mo (School of Management at Kyung Hee University)
Park, Seung-Bo (School of Management at Kyung Hee University)
Kim, Jae Kyeong (School of Management at Kyung Hee University)
Publication Information
Journal of Intelligence and Information Systems / v.19, no.1, 2013 , pp. 79-94 More about this Journal
Abstract
Recently, the high value added business is steadily growing in the culture and art area. To generated high value from a performance, the satisfaction of audience is necessary. The flow in a critical factor for satisfaction, and it should be induced from audience and measures. To evaluate interest and emotion of audience on contents, producers or investors need a kind of index for the measurement of the flow. But it is neither easy to define the flow quantitatively, nor to collect audience's reaction immediately. The previous studies of the group flow were evaluated by the sum of the average value of each person's reaction. The flow or "good feeling" from each audience was extracted from his face, especially, the change of his (or her) expression and body movement. But it was not easy to handle the large amount of real-time data from each sensor signals. And also it was difficult to set experimental devices, in terms of economic and environmental problems. Because, all participants should have their own personal sensor to check their physical signal. Also each camera should be located in front of their head to catch their looks. Therefore we need more simple system to analyze group flow. This study provides the method for measurement of audiences flow with group synchronization at same time and place. To measure the synchronization, we made real-time processing system using the Differential Image and Group Emotion Analysis (GEA) system. Differential Image was obtained from camera and by the previous frame was subtracted from present frame. So the movement variation on audience's reaction was obtained. And then we developed a program, GEX(Group Emotion Analysis), for flow judgment model. After the measurement of the audience's reaction, the synchronization is divided as Dynamic State Synchronization and Static State Synchronization. The Dynamic State Synchronization accompanies audience's active reaction, while the Static State Synchronization means to movement of audience. The Dynamic State Synchronization can be caused by the audience's surprise action such as scary, creepy or reversal scene. And the Static State Synchronization was triggered by impressed or sad scene. Therefore we showed them several short movies containing various scenes mentioned previously. And these kind of scenes made them sad, clap, and creepy, etc. To check the movement of audience, we defined the critical point, ${\alpha}$and ${\beta}$. Dynamic State Synchronization was meaningful when the movement value was over critical point ${\beta}$, while Static State Synchronization was effective under critical point ${\alpha}$. ${\beta}$ is made by audience' clapping movement of 10 teams in stead of using average number of movement. After checking the reactive movement of audience, the percentage(%) ratio was calculated from the division of "people having reaction" by "total people". Total 37 teams were made in "2012 Seoul DMC Culture Open" and they involved the experiments. First, they followed induction to clap by staff. Second, basic scene for neutralize emotion of audience. Third, flow scene was displayed to audience. Forth, the reversal scene was introduced. And then 24 teams of them were provided with amuse and creepy scenes. And the other 10 teams were exposed with the sad scene. There were clapping and laughing action of audience on the amuse scene with shaking their head or hid with closing eyes. And also the sad or touching scene made them silent. If the results were over about 80%, the group could be judged as the synchronization and the flow were achieved. As a result, the audience showed similar reactions about similar stimulation at same time and place. Once we get an additional normalization and experiment, we can obtain find the flow factor through the synchronization on a much bigger group and this should be useful for planning contents.
Keywords
Group Audience; Synchronization; Flow; Differential Image; Group Flow;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
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1 Haag, A., S. Goronzy, P. Schaich, and J. Williams, "Emotion Recognition Using Bio-sensors : First Step Towards an Automatic System", Affective Dialogue Systems, Tutorial and Research Workshop, Germany, 2004.
2 Clarke, S. G. and J. T. Haworth, "Flow Experience in the Daily Lives of Sixth-Form Collect Students", British Journal of Psychology, Vol.85(1994), 511-523.   DOI   ScienceOn
3 Lee, J. S. and Lee, M. G., "A Study on Pattern Recognition using DCT and Neural Network", The Korean Institute of Communications and Information Sciences, Vol.22(1997), 481-492.
4 Ministry of Culture, Sports and Tourism, "2008 Survey on the Performing Arts", 2009.
5 Oliveira, A. M., M. P. Teixeira, l. B. Fonseca, and M. Oliveira, "Joint Model-Parameter Validation of Self-Estimates of Valence and Arousal : Probing a Differential-Weighting Model of Affective Intensity", Proceedings of the 22nd Annual Meeting of the International Society for Psychophysics, (2006), 245-250.
6 Roh, J. S., "A Study on the Leisure Satisfaction of the Audience by Uses and Flow Experience of Media-Focused on the TV and Internet", Ph.D. diss., Dept. of Mass Communication, Chung-Ang Univ, 2003.
7 Park, W. K., I. Y. Choi, H. C. Ahn, and J. K. Kim, "A Study on Intelligent Interactive System Considering Audience's Response for Providing Personalized Exhibition Service", Journal of Intelligence and Information Systems, Vol.11, No.2(2012), 229-242.
8 Picard, R. W., E. Vyzas, and J. Healy, "Toward Machine Emotional Intelligence : Analysis of Affective Physiological State", Pattern Analysis and Machine Intelligence, IEEE Transaction, Vol.23(2001), 1175-1191.   DOI   ScienceOn
9 Ryu, J. M., S.-B. Park, and J. K. Kim, "Judgement Model for Group Flow based on the Synchronization of Group Behavior", Proceedings of the Korea Intelligent Information System Society Conference, (2012), 135-141.
10 TTAword Dictionary, Telecommunications technology Association, http://word.tta.or.kr/terms/ terms.jsp?search=%B5%BF%B1%E2&how=like (Accessed 15 December, 2012).
11 Wagner, J., J. Kim, and E. Andre, "From Physiological Signals to Emotions : Implementing and Comparing Selected Methods for Feature Extraction and Classification", Multimedia and Expo, ICME 2005, IEEE International Conference on, (2005), 940-943.
12 Yoo, J. K., "A study on the Effects of the Flow Experience on Satisfaction Level : The Case of Tourist Visiting TV Drama Location", The Tourism Research Association, Vol.15 (2007), 389-400.
13 Hatfield, E. and C. Hsee, "The impact of vocal feedback on emotional experience and expression", Journal of Social Behavior and Personality, Vol.10(1995), 293-313.
14 Hietanen, J. K., V. Surakka, and I. Linnankoski, "Facial electro myographic responses to vocal affect expressions", Psychophysiology, Vol.35 (2003), 530-536.
15 Hoffman, D. L. and T. P. Novak, "Marketing in Hypermedia Computer Mediated Environments : Conceptual Foundations", Journal of Marketing, Vol.60(1996), 50-68.   DOI   ScienceOn
16 Jeong, J. S., "Science Concert", eastasiabook. 2003.
17 Jung, M. K. and J. K. Kim, "The Intelligent Determination Model of Audience Emotion for Implementing Personalized Exhibition", Journal of Intelligence and Information Systems, Vol.18, No.1(2012), 39-67.
18 Kim, D. A., "The Relationships among Aerobics Exercise Participation, and Flow Experience, Quality of Life", Korean Alliance for Health, Physical Education, Recreation, and Dance, Vol.43(2004), 111-120.
19 Kim, M. J., "On Relevance of Mean as a Representative value of Data shown in Secondary Math Textbooks", M. A. diss., Dept. of education, Kon-Kuk Univ, 2009.
20 Kim, S. J., E. J. Ryoo, M. K. Jung, J. K. Kim, and H. C. Ahn, "Application of Support Vector Regression for Improving the Performance of the Emotion Prediction Model", Journal of Intelligence and Information Systems, Vol.18, No.3(2012), 185-202.
21 Lane, R. D. and L. Nadel, "Cognitive Neuroscience of Emotion", Oxford Univ. Press, 2000.
22 Lee, J. Y., "Research on the Emotion Recognition Agent based on Biometrics", M.A. diss., Dept. of Human Computer Interaction, Se- Jong Univ, 2009.
23 Leung, M. K. and Y. H. Yang, "Human Body Motion Segmentation in A Complex Scene", Pattern Recognition, Vol.20(1987), 55-64.   DOI   ScienceOn
24 Lewis, P. A., H. D. Critchley, P. Rotshtein, and Dolan, R. J., "Neural Correlates of Processing Valence and Arousal in Affective Words", Cerebral Cortex, Vol.17(2007), 742-748.
25 Lundqvist, L. O. and Dimberg, U., "Facial expressions are contagious", Journal of Psychophysiology, Vol.9(1995), 203-211.
26 McCraty, R., M. Atkinson, W. A. Tiller, G. Rein, and A. D. Watkins, "The Effects of Emotions on Short-Term Power Spectrum Analysis of Heart Rate Variability", The American Journal of Cardiology, Vol.76(1995), 1089-1093.   DOI   ScienceOn
27 Ministry of Culture, Sports and Tourism, "2011 Survey on the Performing Arts", 2012.
28 Neumann, R. and Strack, F., "Mood contagion : The automatic transfer of mood between persons", Journal of Personality and Social Psychology, Vol.79(2000), 211-223.   DOI   ScienceOn
29 Alvarado, N., "Arousal and Valence in the Direct Scaling of Emotional Response to Film Clips", Motivation and Emotion, Vol.21, Issue 4(1997), 323-348.   DOI   ScienceOn
30 Anders, S., Heinzle, J., Weiskopf, N., Ethofer, T. and Haynes, J. D., "Flow of affective information between communicating brains", Neuro Image, Vol.54, No.1(2011), 439-446.
31 Barsade, S. G. and D. E. Gibson, "Group emotion : A view from top and bottom", Research on Managing Groups and Teams, Stanford, CT : JAI Press, Vol.1(1998), 81-102.
32 Bernieri, F. J., "Coordinated movement and rapport in student-teacher interactions", Journal of Nonverbal Behavior, Vol.12(1988), 120-138.   DOI
33 Chartrand, T. L. and J. A. Bargh, "The chameleon effect : The perception-behavior link and social interaction", Journal of Personality and Social Psychology, Vol.76(1999), 893-910.   DOI
34 Chen, L. S., T. S. Huang, T. Miyasato, and R. Nakatsu, "Multimodal human emotion/expression recognition", Conf. on Automatic Face and Gesture Recognition, Third IEEE International Conf., (1998), 366-371.
35 Csikszentmihalyi, M., "Flow : The Psychology of Optimal Experience", New York, Harper and Row Publisher, 1990.
36 Davis, B., Bull, R., Roscoe, J., Roscoe, D., Saiz, R. and Curran, R., "Physical education and the study of sport", 4th ed. Spain : Harcourt, 312, 2000.
37 Dimberg, U., "Facial reactions to facial expressions", Psychophysiology, Vol.19(1982), 643-647.   DOI   ScienceOn
38 Ekman, P., W. V. Friesen, and K. R. Scherer,. "Body movement and voice pitch in deceptive interaction", Semiotica, Vol.16(1976), 23-28.