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| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 許永真(Jan Yung-jen Hsu) | |
| dc.contributor.author | Yu-Hsin Chen | en |
| dc.contributor.author | 陳郁欣 | zh_TW |
| dc.date.accessioned | 2021-06-12T18:23:05Z | - |
| dc.date.available | 2007-08-28 | |
| dc.date.copyright | 2007-08-28 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-08-19 | |
| dc.identifier.citation | [1] L. F. Barrett. Discrete emotions or dimensions? the role of valence focus and arousal focus. Cognition and Emotion, 12:579 - 599, 1998.
[2] C. Busso, Z. Deng, S. Yildirim, M. Bulut, C. M. Lee, A. Kazemzadeh, S. Lee, U. Neumann, and S. Narayanan. Analysis of emotion recognition using facial expressions, speech and multimodal information. In Proceedings of the 6th International Conference on Multimodal Interfaces (ICMI 2004), pages 205-211, 2004. [3] C.-C. Chang and C.-J. Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/ cjlin/libsvm. [4] I. Cohen, N. Sebe, A. Gerg, L. S. Chen, and T. S. Huang. Facial expression recognition from video sequences: Temporal and static modeling. Computer Vision and Image Understanding, 91:160-187, July-August 2003. [5] R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor. Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18:32-80, 2001. [6] L. De Silva and P. C. Ng. Bimodal emotion recognition. In Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition, pages 332-335, 2000. [7] P. Ekman. Are there basic emotions? Psychological Review, 99(3):550-553, July 1992. [8] P. Ekman, R. W. Levenson, and W. V. Friesen. Autonomic nervous system activity distinguishes among emotions. Science, 221(4616):1208-1210, September 1983. [9] P. Gomez, W. A. Stahel, and B. Danuser. Respiratory response during affective picture viewing. Biological Psychology, 67:359-373, 2004. [10] J. A. Healey. Wearable and Automotive Systems for the Recognition of Affect from Physiology. PhD thesis, Massachusetts Institute of Techonology, May 2000. [11] C.-W. Hsu, C.-C. Chang, and C.-J. Lin. A practical guide to support vector classfication. Technical report, Department of Computer Science and Information Engineering, National Taiwan University, 2003. [12] C. E. Izard. Basic emotions, relations among emotions, and emotion-cognition relations. Psychological Review, 99(3):561-565, July 1992. [13] W. James. What is an emotion? Mind, 9(34):188-205, April 1884. [14] K. H. Kim, S. W. Bang, and S. R. Kim. Emotion recognition system using shortterm monitoring of physiological signals. Medical and Biological Engineering and Computing, 42:419-427, 2004. [15] J. Klein, Y. Moon, and R. Picard. This computer responds to user frustration: Theory, design, and results. Interacting with Computers, 14:119-140, February 2002. [16] S. M. Kosslyn. Psychology: the Brain, the Person, the World, chapter 10, pages 390-435. Person Education, Inc, 2 edition, 2004. [17] C.-J. Lin. A guide to support vector machines. January 2007. [18] C. L. Lisetti and F. Nasoz. Using noninvasive wearable computers to recognize human emotions from physiological signals. EURASIP Journal on Applied Signal Processing, 2004:1672-1687, 2004. [19] F. Nasoz, K. Alvarez, L. C.L., and N. Finkelstein. Emotion recognition from physiological signals using wireless sensors for presence technologies. Cognition, Technology & Work, 6(1):4-14, February 2004. [20] A. Ortony and T. J. Turner. What's basic about basic emotions? Psychological Review, 97(3):315-331, 1990. [21] R. Picard. Technology-sense and people-sensibility. presentation in h2.0 New Minds, New Bodies, New Identities, May 2007. [22] H. Prendinger, C. Becker, and M. Ishizuka. A study in users' physiological response to an empathic interface agent. International Journal of Humanoid Robotics, 3(3):371-391, September 2006. [23] H. Prendinger and M. Ishizuka. The empathic companion: A character-based interface that addresses users' a®ective states. Applied Arti‾cial Intelligence, 19(3-4):267-285, March-April 2005. [24] H. Prendinger, J. Mori, and M. Ishizuka. Using human physiology to evaluate subtle expressivity of a virtual quizmaster in a mathematical game. 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From physiological signals to emotion: Implementing and comparing selected methods for feature extraction and classification. In Proceedings of IEEE International Conference on Multimedia and Expo (ICME 2005), pages 940-943, 2005. [31] I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, June 2005. [32] P. G. Zimbardo and R. J. Gerrig. Psychology and Lift - A condensed Edition. Wunan, 1999. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27833 | - |
| dc.description.abstract | 情緒在人際溝通上與服務的提供上，是個常被遺忘的對象；但近年來由於溝通互動和個人化服務的迅速發展，情緒漸漸的被視為一個考慮、討論以及研究的對象。在心理學以及哲學的領域中，情緒算是較早被提出和被研究的，一些情緒的功能像是決策決定、學習上已證明有明確的影響與功能。
以前人之研究作為基石，本研究乃採用人體的胸腔擴張程度、肌膚導電度、肌膚表面溫度、以及血氧濃度等生理數值作為訊號來源，進而辨識喜、怒、哀、懼等四種情緒狀態，並透過較為隱蔽的表達方式展現結果，以利用這些資訊來幫助人際溝通與互動。在情緒的辨認部份，本文探討了情緒與生理訊號之關係，以及部分前人之研究情形，從而提出我的解決方式。除此之外，情緒資訊之應用亦為本文的另一個重點。藉由此珍貴的資訊，在此也提出了應用的雛形Cura，透過隱諱但隨手可得的方式，告知週遭的人自己的情緒狀態，以協助人際溝通與互動。 | zh_TW |
| dc.description.abstract | Emotion as a concept is usually forgotten in service providing and interactive communication. As content service and communication develop, emotion becomes more and more important in these research fields. In this research, I use bio-sensors to collect physiological signals from human subjects in different emotion states. The signals include sensor data from blood volume pulse, skin conductance, skin temperature, and respiration. I extract the features from these signals, and then use support vector machine to learn a classifier. The recognition rate of emotion is about 97%.
Furthermore, a prototype application Cura is made. Cura is an ambient cube which shows emotion states depending on the recognition of emotion. It is a media to tell one's closer the emotion with privacy-concern. Cura is simple and nature so that human transmit the emotion directly. Furthermore, Cura also keeps the private of the emotion information. It leaves the decision to user themselves with who he/she shares the emotion. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-12T18:23:05Z (GMT). No. of bitstreams: 1 ntu-96-R94922046-1.pdf: 2187832 bytes, checksum: 2fefe76fe7fd0f17c49a47f71f786259 (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | Acknowledgments ii
Abstract v List of Figures xii List of Tables xiv Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.2 Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Chapter 2 Related Work 5 2.1 View from Psychology . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.1 Emotion Models . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.2 Characteristics and Roles of emotion . . . . . . . . . . . . 9 2.2 Recognition in Artificial Intelligence . . . . . . . . . . . . . . . . . 9 2.2.1 Emotion Recognition . . . . . . . . . . . . . . . . . . . . . 10 2.2.2 Physiological Signals . . . . . . . . . . . . . . . . . . . . . 13 2.2.3 Learning Methods . . . . . . . . . . . . . . . . . . . . . . . 18 2.3 Relevance of Emotion Research for Affective Computing . . . . . 22 2.3.1 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.3.2 Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.3.3 Education and Tutor System . . . . . . . . . . . . . . . . . 24 Chapter 3 Problem Definition 26 3.1 System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 System Focus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.2.1 Sensor Data from Physiological Signals . . . . . . . . . . . 29 3.2.2 Emotion States . . . . . . . . . . . . . . . . . . . . . . . . 31 3.2.3 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Chapter 4 Experiment 36 4.1 Mood Induction and Data Collection . . . . . . . . . . . . . . . . 37 4.2 Feature Extraction and Learning . . . . . . . . . . . . . . . . . . 38 4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 4.3.1 The Result of Emotion Recognition . . . . . . . . . . . . . 41 4.3.2 The Result from the Decision Tree . . . . . . . . . . . . . 42 4.3.3 Importance of Sensor Data . . . . . . . . . . . . . . . . . . 43 4.3.4 Cross Person Recognition Result . . . . . . . . . . . . . . 44 4.3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Chapter 5 Application 47 5.1 The Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.2 Cura . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 5.2.1 Structure of Cura . . . . . . . . . . . . . . . . . . . . . . . 49 5.2.2 Patterns in Cura . . . . . . . . . . . . . . . . . . . . . . . 50 5.2.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 5.3 Music Service Providing . . . . . . . . . . . . . . . . . . . . . . . 51 Chapter 6 Conclusion 53 Bibliography 56 Appendix A Experiment Design Document 61 A.1 Preparing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 A.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 A.2.1 Equipment Set Up . . . . . . . . . . . . . . . . . . . . . . 62 A.2.2 Overview of the Whole Process . . . . . . . . . . . . . . . 62 A.2.3 Detail Description . . . . . . . . . . . . . . . . . . . . . . . 64 Appendix B Questionnaire for Mood Induction 70 B.1 Self Evaluation Form for Sad . . . . . . . . . . . . . . . . . . . . . 70 B.2 Self Evaluation Form for Anger . . . . . . . . . . . . . . . . . . . 71 B.3 Self Evaluation Form for Fear . . . . . . . . . . . . . . . . . . . . 71 B.4 Self Evaluation Form for Happy . . . . . . . . . . . . . . . . . . . 72 B.5 Self Evaluation Form for Personal Information . . . . . . . . . . . 72 | |
| dc.language.iso | en | |
| dc.subject | 情緒辨識 | zh_TW |
| dc.subject | 生理訊號 | zh_TW |
| dc.subject | emotion reconition | en |
| dc.subject | physiological signal | en |
| dc.title | 基於生理訊號之情緒辨識及應用 | zh_TW |
| dc.title | Emotion Recognition from Physiological Sensor Data - Learning and Applications | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 鄭士康,陳淑惠(Sue-Huei Chen),黃寶儀,張智星 | |
| dc.subject.keyword | 情緒辨識,生理訊號, | zh_TW |
| dc.subject.keyword | emotion reconition,physiological signal, | en |
| dc.relation.page | 74 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-08-20 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
| Appears in Collections: | 資訊工程學系 | |
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| ntu-96-1.pdf Restricted Access | 2.14 MB | Adobe PDF |
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