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| Title | 生理訊號監控應用於智慧生活環境之研究 Application of physiological signal monitoring in smart living space |
| Creator | 徐世平 Shu, Shih Ping |
| Contributor | 廖文宏 Liao, Wen Hung 徐世平 Shu, Shih Ping |
| Key Words | 短時間情緒識別 生理訊號 情意運算 short-term emotion recognition Physiological Signal Affective Computing IAPS,International Affective Picture System |
| Date | 2007 |
| Date Issued | 19-Sep-2009 12:09:42 (UTC+8) |
| Summary | 在心理與認知科學領域中常使用生理訊號來測量受試者的反應，並反映出人們的心理狀態起伏。本研究探討應用生理訊號識別情緒之可能性，以及將生理訊號與其他情緒辨識結果整合之方法。 在過去的研究中，生理與心理的對應關係，並無太多著墨，可稱為一黑盒子(black-box)的方式。並因上述類別式實驗長時間收集的生理訊號，對於誘發特定情緒反應之因果(cause-effect)並未進行深入的討論。本研究由於實驗的設計與選用材料之故，可一探純粹由刺激引發的情緒下情緒在生理與心理之因果關係，在輸入輸出對應間能有較明確的解釋。 本研究中嘗試監測較短時間(<10sec)的生理資訊，期望以一近乎即時的方式判讀並回饋使用者適當的資訊，對於生理訊號與情緒狀態的關聯性研究，將以IAPS(International Affective Picture System) 素材為來源，進行較過去嚴謹的實驗設計與程序，以探究生理訊號特徵如何應用於情緒分類。 雖然本研究以維度式情緒學說為理論基礎，然考慮到實際應用情境，若有其他以類別式的理論為基礎之系統，如何整合維度式與類別式兩類的資訊，提出可行的轉換方式，亦是本研究的主要課題。 Physiological signals can be used to measure a subject’s response to a particular stimulus, and infer the emotional status accordingly. This research investigates the feasibility of emotion recognition using physiological measurements in a smart living space. It also addresses important issues regarding the integration of classification results from multiple modalities. Most past research regarded the recognition of emotion from physiological data as a mapping mechanism which can be learned from training data. These data were collected over a long period of time, and can not model the immediate cause-effect relationship effectively. Our research employs a more rigorous experiment design to study the relationship between a specific physiological signal and the emotion status. The newly designed procedure will enable us to identify and validate the discriminating power of each type of physiological signal in recognizing emotion. Our research monitors short term (< 10s) physiological signals. We use the IAPS (International Affective Picture System) as our experiment material. Physiological data were collected during the presentation of various genres of pictures. With such controlled experiments, we expect the cause-effect relation to be better explained than previous black-box approaches. Our research employs dimensional approach for emotion modeling. However, emotion recognition based on audio and/or visual clues mostly adopt categorical method (or basic emotion types). It becomes necessary to integrate results from these different modalities. Toward this end, we have also developed a mapping process to convert the result encoded in dimensional format into categorical data. |
| 參考文獻 | [1] Rosalind W. Picard, Affective Computing, The MIT Press, 1997. [2] R.W Picard., E. Vyzas; J. Healey, Toward machine emotional intelligence: analysis of affective physiological state, Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 1175-1191 ,2001 [3] K.H. Kim, S.W. Bang, S.R. Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical and Biological Engineering and Computing, Vol. 42., pp. 419–427,42, 2004. [4] Wendy S. Ark et al. ,The Emotion Mouse ,Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction, Ergonomics and User Interfaces, Vol. 1 ,pp. 818-823 , 1999 [5] Dong-Wan Ryoo, Jeun-Woo Lee, Feature Extraction and Emotion Classification Using Bio-Signal, Transactions on Engineering, Computing and Technology, Vol. 2 ,2004 [6] Peter J. Lang, The emotion probe. Studies of motivation and attention., American Psychologist, Vol. 50, No. 5, pp.372-185,1995 [7] MM. Bradley, M. Codispoti, BN. Cuthbert, and PJ Lang, Emotion and motivation I: Defensive and appetitive reactions in picture processing, American Psychological Association, Vol. 1, No. 3, pp. 276–298,2001 [8] N. Sebe , I. Cohen, T.S. Huang, Human-Computer Interaction, ICCV 2005 Wprkshop on HCI , pp 1-15 , 2005 [9] Paul Ekman, Friesen WV. Facial action coding system. Consulting Psychologists Press, 1978 [10] Paul Ekman, Robert W. Levenson, and Wallace V. Friesen. Autonomic nervous system activity distinguishes among emotions., Science, 221.,pp. 1208-1210, 1983. [11] K.T. Strongman, The Psychology of Emotion(Chinese Editon), WU-NAN Book CO.LTD, pp. 92-93,1993 [12] Tom Mitchell, Machine Learning. McGraw-Hill, 1997 [13] Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm [14] J.Wagner, J. Kim and E.André., From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification., IEEE International Conference on Multimedia & Expo (ICME 2005), 2005. [15] R.O.Duda, P.E.Hart, D.G. Stork , Pattern Classification (2nd Edition), Wiley & Sons , 2001 |
| Description | 碩士 國立政治大學 資訊科學學系 94753010 96 |
| 資料來源 | http://thesis.lib.nccu.edu.tw/record/#G0094753010 |
| Type | thesis |
| dc.contributor.advisor | 廖文宏 | zh_TW |
| dc.contributor.advisor | Liao, Wen Hung | en_US |
| dc.contributor.author (Authors) | 徐世平 | zh_TW |
| dc.contributor.author (Authors) | Shu, Shih Ping | en_US |
| dc.creator (作者) | 徐世平 | zh_TW |
| dc.creator (作者) | Shu, Shih Ping | en_US |
| dc.date (日期) | 2007 | en_US |
| dc.date.accessioned | 19-Sep-2009 12:09:42 (UTC+8) | - |
| dc.date.available | 19-Sep-2009 12:09:42 (UTC+8) | - |
| dc.date.issued (上傳時間) | 19-Sep-2009 12:09:42 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0094753010 | en_US |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/37103 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 資訊科學學系 | zh_TW |
| dc.description (描述) | 94753010 | zh_TW |
| dc.description (描述) | 96 | zh_TW |
| dc.description.abstract (摘要) | 在心理與認知科學領域中常使用生理訊號來測量受試者的反應，並反映出人們的心理狀態起伏。本研究探討應用生理訊號識別情緒之可能性，以及將生理訊號與其他情緒辨識結果整合之方法。 在過去的研究中，生理與心理的對應關係，並無太多著墨，可稱為一黑盒子(black-box)的方式。並因上述類別式實驗長時間收集的生理訊號，對於誘發特定情緒反應之因果(cause-effect)並未進行深入的討論。本研究由於實驗的設計與選用材料之故，可一探純粹由刺激引發的情緒下情緒在生理與心理之因果關係，在輸入輸出對應間能有較明確的解釋。 本研究中嘗試監測較短時間(<10sec)的生理資訊，期望以一近乎即時的方式判讀並回饋使用者適當的資訊，對於生理訊號與情緒狀態的關聯性研究，將以IAPS(International Affective Picture System) 素材為來源，進行較過去嚴謹的實驗設計與程序，以探究生理訊號特徵如何應用於情緒分類。 雖然本研究以維度式情緒學說為理論基礎，然考慮到實際應用情境，若有其他以類別式的理論為基礎之系統，如何整合維度式與類別式兩類的資訊，提出可行的轉換方式，亦是本研究的主要課題。 | zh_TW |
| dc.description.abstract (摘要) | Physiological signals can be used to measure a subject’s response to a particular stimulus, and infer the emotional status accordingly. This research investigates the feasibility of emotion recognition using physiological measurements in a smart living space. It also addresses important issues regarding the integration of classification results from multiple modalities. Most past research regarded the recognition of emotion from physiological data as a mapping mechanism which can be learned from training data. These data were collected over a long period of time, and can not model the immediate cause-effect relationship effectively. Our research employs a more rigorous experiment design to study the relationship between a specific physiological signal and the emotion status. The newly designed procedure will enable us to identify and validate the discriminating power of each type of physiological signal in recognizing emotion. Our research monitors short term (< 10s) physiological signals. We use the IAPS (International Affective Picture System) as our experiment material. Physiological data were collected during the presentation of various genres of pictures. With such controlled experiments, we expect the cause-effect relation to be better explained than previous black-box approaches. Our research employs dimensional approach for emotion modeling. However, emotion recognition based on audio and/or visual clues mostly adopt categorical method (or basic emotion types). It becomes necessary to integrate results from these different modalities. Toward this end, we have also developed a mapping process to convert the result encoded in dimensional format into categorical data. | en_US |
| dc.description.tableofcontents | 第一章 簡介 1 1.1 研究背景 1 1.2 研究目的 2 1.3 預期成果與應用情境 2 1.4 章節總覽 3 1.5 本研究之貢獻 3 第二章 相關研究 5 2.1資訊科學中的情意運算 5 2.2心理學領域的情緒相關研究 7 2.3 IAPS測驗 9 2.4情緒研究之比較與整理 11 2.5生理訊號特性簡述 12 同步記錄生理訊號記錄儀 16 2.6資料收集儀器Biofeedback 2000 Xpert 16 2.7資料收集儀器NeuroScan 17 2.8資料收集儀器ProComp Infiniti 18 第三章 研究架構 19 3.1 研究方法 19 3.2應用於居家生活之生理監控系統 20 第四章 實驗設計說明與資料收集 22 4.1 IAPS情緒圖片前測實驗 22 4.1.1實驗數據收集 23 4.1.2訊號來源與穩定度比較 23 4.1.3 SCR與情緒反應間的關係 23 4.1.4 Heart Rate與情緒反應間的關係 28 4.2情境之實驗設計與數據收集 36 第五章 生理訊號分析與情緒識別 39 5.1基於生理訊號之情緒感知 39 5.2維度情緒與類別式情緒間的轉換 59 第六章 結論 70 第七章 參考文獻 72 附錄A 前測實驗 74 A.1.2 Sensor附著方式 74 A.1.3訊號的連動與干擾程度 75 A.1.4訊號來源的選用建議 77 附錄B SCR Data vs. Behavior Data 79 附錄C 受測者SAM量表之評量 81 | zh_TW |
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| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0094753010 | en_US |
| dc.subject (關鍵詞) | 短時間情緒識別 | zh_TW |
| dc.subject (關鍵詞) | 生理訊號 | zh_TW |
| dc.subject (關鍵詞) | 情意運算 | zh_TW |
| dc.subject (關鍵詞) | short-term emotion recognition | en_US |
| dc.subject (關鍵詞) | Physiological Signal | en_US |
| dc.subject (關鍵詞) | Affective Computing | en_US |
| dc.subject (關鍵詞) | IAPS,International Affective Picture System | en_US |
| dc.title (題名) | 生理訊號監控應用於智慧生活環境之研究 | zh_TW |
| dc.title (題名) | Application of physiological signal monitoring in smart living space | en_US |
| dc.type (資料類型) | thesis | en |
| dc.relation.reference (參考文獻) | [1] | zh_TW |
| dc.relation.reference (參考文獻) | Rosalind W. Picard, Affective Computing, The MIT Press, 1997. | zh_TW |
| dc.relation.reference (參考文獻) | [2] | zh_TW |
| dc.relation.reference (參考文獻) | R.W Picard., E. Vyzas; J. Healey, Toward machine emotional intelligence: analysis of affective physiological state, Transactions on Pattern Analysis and Machine Intelligence, Vol. 23, pp. 1175-1191 ,2001 | zh_TW |
| dc.relation.reference (參考文獻) | [3] | zh_TW |
| dc.relation.reference (參考文獻) | K.H. Kim, S.W. Bang, S.R. Kim, Emotion recognition system using short-term monitoring of physiological signals, Medical and Biological Engineering and Computing, Vol. 42., pp. 419–427,42, 2004. | zh_TW |
| dc.relation.reference (參考文獻) | [4] | zh_TW |
| dc.relation.reference (參考文獻) | Wendy S. Ark et al. ,The Emotion Mouse ,Proceedings of HCI International (the 8th International Conference on Human-Computer Interaction) on Human-Computer Interaction, Ergonomics and User Interfaces, Vol. 1 ,pp. 818-823 , 1999 | zh_TW |
| dc.relation.reference (參考文獻) | [5] | zh_TW |
| dc.relation.reference (參考文獻) | Dong-Wan Ryoo, Jeun-Woo Lee, Feature Extraction and Emotion Classification Using Bio-Signal, Transactions on Engineering, Computing and Technology, Vol. 2 ,2004 | zh_TW |
| dc.relation.reference (參考文獻) | [6] | zh_TW |
| dc.relation.reference (參考文獻) | Peter J. Lang, The emotion probe. Studies of motivation and attention., American Psychologist, Vol. 50, No. 5, pp.372-185,1995 | zh_TW |
| dc.relation.reference (參考文獻) | [7] | zh_TW |
| dc.relation.reference (參考文獻) | MM. Bradley, M. Codispoti, BN. Cuthbert, and PJ Lang, Emotion and motivation I: Defensive and appetitive reactions in picture processing, American Psychological Association, Vol. 1, No. 3, pp. 276–298,2001 | zh_TW |
| dc.relation.reference (參考文獻) | [8] | zh_TW |
| dc.relation.reference (參考文獻) | N. Sebe , I. Cohen, T.S. Huang, Human-Computer Interaction, ICCV 2005 Wprkshop on HCI , pp 1-15 , 2005 | zh_TW |
| dc.relation.reference (參考文獻) | [9] | zh_TW |
| dc.relation.reference (參考文獻) | Paul Ekman, Friesen WV. Facial action coding system. Consulting Psychologists Press, 1978 | zh_TW |
| dc.relation.reference (參考文獻) | [10] | zh_TW |
| dc.relation.reference (參考文獻) | Paul Ekman, Robert W. Levenson, and Wallace V. Friesen. Autonomic nervous system activity distinguishes among emotions., Science, 221.,pp. 1208-1210, 1983. | zh_TW |
| dc.relation.reference (參考文獻) | [11] | zh_TW |
| dc.relation.reference (參考文獻) | K.T. Strongman, The Psychology of Emotion(Chinese Editon), WU-NAN Book CO.LTD, pp. 92-93,1993 | zh_TW |
| dc.relation.reference (參考文獻) | [12] | zh_TW |
| dc.relation.reference (參考文獻) | Tom Mitchell, Machine Learning. McGraw-Hill, 1997 | zh_TW |
| dc.relation.reference (參考文獻) | [13] | zh_TW |
| dc.relation.reference (參考文獻) | Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm | zh_TW |
| dc.relation.reference (參考文獻) | [14] | zh_TW |
| dc.relation.reference (參考文獻) | J.Wagner, J. Kim and E.André., From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification., IEEE International Conference on Multimedia & Expo (ICME 2005), 2005. | zh_TW |
| dc.relation.reference (參考文獻) | [15] | zh_TW |
| dc.relation.reference (參考文獻) | R.O.Duda, P.E.Hart, D.G. Stork , Pattern Classification (2nd Edition), Wiley & Sons , 2001 | zh_TW |