政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32703

政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/32703

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政大機構典藏 > 資訊學院 > 資訊科學系 > 學位論文 > Item 140.119/32703

請使用永久網址來引用或連結此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/32703

題名:	利用生理感測資料之線上情緒辨識系統 On-line Emotion Recognition System by Physiological Signals
作者:	陳建家 Chen, Jian Jia
貢獻者:	蔡子傑 Tsai, Tzu Chieh 陳建家 Chen, Jian Jia
關鍵詞:	情緒生理感測器演算法即時辨識智慧型生活環境 emotion physiological sensors algorithm on-line recognition smart environment
日期:	2008
上傳時間:	2009-09-17 14:05:43 (UTC+8)
摘要:	貼心的智慧型生活環境，必須能在不同的情緒狀態提供適當服務，因此我們希望能開發出一個情緒辨識系統，透過對於形於外的生理感測資料的變化來觀察形於內的情緒狀態。首先我們採用國際情緒圖庫系統(IAPS: International Affective Picture System) 及維度式分析方法，透過心理實驗的操弄，收集了20位的受測者生理數值與主觀評定情緒的強度與正負向。我們提出了一個情緒辨識學習演算法，經由交叉驗證訓練出每個情緒的特徵，並藉由即時測試資料來修正情緒特徵的個人化，經由學習趨勢的評估，準確率有明顯提升。其次，我們更進一步引用了維度式與類別式情緒的轉換概念來驗證受測者主觀評定的結果。相較於相關研究實驗結果，我們在維度式上的強度與正負向辨識率有較高的表現，在類別式上的驗證我們也達到明顯區分效果。更重要的是，我們所實作出的系統，是搭載了無線生理感測器，使用時更具行動性，而且可即時反映情緒，提供線上智慧型服務。 A living smart environment should be able to provide thoughtful services by considering different states of emotions. The goal of our research is to develop an emotion recognition system which can detect the internal emotion states from external varieties of physiological data. First we applied the dimensional analysis approach and adopted IAPS (International Affective Picture System) to manipulate psychological experiments. We collected physiological data and subjective ratings for arousal and valence from 20 subjects. We proposed an emotion recognition learning algorithm. It would extract each pattern of emotions from cross validation training and can further learn adaptively by feeding personalized testing data. We measured the learning trend of each subject. The recognition rate reveals incremental enhancement. Furthermore, we adopted a dimensional to discrete emotion transforming concept for validating the subjective rating. Compared to the experiment results of related works, our system outperforms both in dimensional and discrete analyses. Most importantly, the system is implemented based on wireless physiological sensors for mobile usage. This system can reflect the image of emotion states in order to provide on-line smart services.
參考文獻:	[1] J. LeDoux, The Emotional Brain. New York: Simon & Schuster, 1996. [2] P. Salovery and J. D. Mayer, “Emotional intelligence”, Imagination, Cognition and Personality, vol. 9, no. 3, pp. 185-211, 1990. [3] D. Goleman, Emotional Intelligence. New York: Bantam Books, 1995. [4] K. R. Scherer. Ch. 10: Speech and emotional states. In J. K. Darby, editor, Speech Evaluation in psychiatry, pages 189-220. Grune and Stratton, Inc., 1981. [5] Y. Yacoob and L.S. Davis. Recognizing human facial expressions from log image sequences using optical flow. Transactions on Pattern Recognition and Machine Intelligence, 18(6):636-642, June 1996. [6] I. A. Essa and A. Pentland. Facial expression recognition using a dynamic model and motion enerty. In International Conference on Computer Vision, pates 360-367, Cambridge MA, 1995, IEEE Computer Society. [7] R. W. Picard. Affective Computing. The MIT Press, Cambridge, MA, 1997. [8] Christian Martyn Jones and Tommy Troen, Biometric Valence and Arousal Recognition , ACM International Conference Proceeding Series; Vol. 251, Proceedings of the 2007 conference of the computer-human interaction special interest group (CHISIG) of Australia on Computer-human interaction. [9] Corinna Cortes and V. Vapnik, "Support-Vector Networks, Machine Learning, 20, 1995 [10] Peng, H.C., Long, F., and Ding, C., "Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy," IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 8, pp.1226-1238, 2005. [11] R.O. Duda and P.E. Hart. Pattern Classification and Scene Analysis. Wiley-Interscience, 1978. [12] Kim, K.H., Bang, S.W., Kim, S.R.: Emotion recognition system using short-term. monitoring of physiological signals. Med Biol Eng Compute 42 (2004). [13] Lang, P.J., Bradley, M. M. and Cuthbert, B. N.. International affective picture system (IAPS): Technical Manual and Affective Ratings, Center for Research in Psychophysiology, University of Florida (1999). [14] Kohavi, Ron (1995). "A study of cross-validation and bootstrap for accuracy estimation and model selection". Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence 2 (12): 1137–1143.(Morgan Kaufmann, San Mateo). [15] Chang, J., Luo, Y., and Su, K. 1992. GPSM: a Generalized Probabilistic Semantic Model for ambiguity resolution. In Proceedings of the 30th Annual Meeting on Association For Computational Linguistics (Newark, Delaware, June 28 - July 02, 1992). Annual Meeting of the ACL. Association for Computational Linguistics, Morristown, NJ, 177-184 [16] Devijver, P. A., and J. Kittler, Pattern Recognition: A Statistical Approach, Prentice-Hall, London, 1982. [17] 徐世平 Master thesis,“Application of Physiological Signal Monitoring in Smart Living Space”, Jun,2008.
描述:	碩士國立政治大學資訊科學學系 95753038 97
資料來源:	http://thesis.lib.nccu.edu.tw/record/#G0095753038
資料類型:	thesis
顯示於類別:	[資訊科學系] 學位論文

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