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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 洪一平(Yi-Ping Hung) | |
dc.contributor.author | Chau-Che Yeh | en |
dc.contributor.author | 葉兆哲 | zh_TW |
dc.date.accessioned | 2021-06-08T02:04:45Z | - |
dc.date.copyright | 2016-03-08 | |
dc.date.issued | 2016 | |
dc.date.submitted | 2016-02-15 | |
dc.identifier.citation | 1. Lazarus, Richard S. 'Psychological stress and the coping process.' (1966).
2. http://www.apa.org/news/press/releases/stress/ 3. Lazarus, Richard S., and Susan Folkman. 'Stress.' Appraisal, and coping 725 (1984). 4. Lazarus, Richard S. 'Progress on a cognitive-motivational-relational theory of emotion.' American psychologist 46.8 (1991): 819. 5. Lazarus, Richard S. 'Coping theory and research: past, present, and future.' Psychosomatic medicine 55.3 (1993): 234-247. 6. Smith, Craig A., and Richard S. Lazarus. 'Appraisal components, core relational themes, and the emotions.' Cognition & Emotion 7.3-4 (1993): 233-269. 7. Lazarus, Richard S., and Susan Folkman. 'Transactional theory and research on emotions and coping.' European Journal of personality 1.3 (1987): 141-169. 8. Kirschbaum, Clemens, K-M. Pirke, and Dirk H. Hellhammer. 'The ‘Trier Social Stress Test’–a tool for investigating psychobiological stress responses in a laboratory setting.' Neuropsychobiology 28.1-2 (1993): 76-81. Frisch, Johanna U., Jan A. Häusser, and Andreas Mojzisch. 'The Trier Social Stress Test as a paradigm to study how people respond to threat in social interactions.' Frontiers in psychology 6 (2015). 9. Frisch, Johanna U., Jan A. Häusser, and Andreas Mojzisch. 'The Trier Social Stress Test as a paradigm to study how people respond to threat in social interactions.' Frontiers in psychology 6 (2015). 10. Dedovic, Katarina, et al. 'The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain.' Journal of Psychiatry and Neuroscience 30.5 (2005): 319. 11. Holmes, Thomas H., and Richard H. Rahe. 'The social readjustment rating scale.' Journal of psychosomatic research 11.2 (1967): 213-218. 12. Hosseini, Seyyed Abed, and Mohammad Ali Khalilzadeh. 'Emotional stress recognition system using EEG and psychophysiological signals: Using new labelling process of EEG signals in emotional stress state.' Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on. IEEE, 2010. 13. Healey, Jennifer, and Rosalind W. Picard. 'Detecting stress during real-world driving tasks using physiological sensors.' Intelligent Transportation Systems, IEEE Transactions on 6.2 (2005): 156-166. 14. Zhai, Jing, and Armando Barreto. 'Stress detection in computer users based on digital signal processing of noninvasive physiological variables.' Engineering in Medicine and Biology Society, 2006. EMBS'06. 28th Annual International Conference of the IEEE. IEEE, 2006. 15. Kumar, Manoj, et al. 'Fuzzy evaluation of heart rate signals for mental stress assessment.' Fuzzy Systems, IEEE Transactions on 15.5 (2007): 791-808. 16. Picard, Rosalind W., and Roalind Picard. Affective computing. Vol. 252. Cambridge: MIT press, 1997. 17. Russell, James A. 'A circumplex model of affect.' Journal of personality and social psychology 39.6 (1980): 1161. 18. Mehrabian, Albert. 'Basic Dimensions for a General Psychological Theory Implications for Personality, Social, Environmental, and Developmental Studies.' (1980). 19. Bao, Sheng Hua, et al. 'Pushing specific content to a predetermined webpage.' U.S. Patent Application No. 14/012,085. 20. Mehrabian, Albert. Nonverbal communication. Transaction Publishers, 1977. 21. Lang, Peter J., Margaret M. Bradley, and Bruce N. Cuthbert. 'International affective picture system (IAPS): Affective ratings of pictures and instruction manual.' Technical report A-8 (2008). 22. Bradley, Margaret M., and Peter J. Lang. 'Measuring emotion: the self-assessment manikin and the semantic differential.' Journal of behavior therapy and experimental psychiatry 25.1 (1994): 49-59. 23. Sharma, Nandita, and Tom Gedeon. 'Objective measures, sensors and computational techniques for stress recognition and classification: A survey.' Computer methods and programs in biomedicine 108.3 (2012): 1287-1301. 24. Picard, Rosalind W., Elias Vyzas, and Jennifer Healey. 'Toward machine emotional intelligence: Analysis of affective physiological state.' Pattern Analysis and Machine Intelligence, IEEE Transactions on 23.10 (2001): 1175-1191. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19547 | - |
dc.description.abstract | 心理壓力是現代人許多身心疾病的因素,也是許多社會、經濟問題和個人問題的主因,到醫院就診的病患有超過70%是與心理壓力直接或間接造成的疾病,在美國每年與壓力相關的經濟損失更高達 2000 億美元以上,因此我們希望透過對壓力偵測的研究,可以在未來的日常生活中即時偵測心理壓力狀況,幫助人們及時排解壓力恢復身心健康,從而有好的身心健康狀態以及人生幸福。
在這個研究中我們做了兩個實驗。第一個實驗確立了情境刺激壓力的順序,因此我們得以提供一個較好的刺激方式,可以在實驗室控制壓力刺激的程度。實驗結果建議壓力由小到大:第四象限第一象限第三象限第二象限,這樣的刺激順序較能有效激起壓力反應。同時我們也建立了一套挑選情境內容的方法,用來選擇適當的情境刺激內容當作壓力源。第二個實驗我們用來驗證第一個實驗的結果並找尋較好的壓力分類方式以及適合用來做壓力分類的特徵值。我們使用 SFFS 來進行特徵值選擇,並使用選擇出來較佳的特徵值進行壓力分類,確實經過特徵值選擇後,我們使用較少的特徵值來進行分類就有較佳的正確率。同時我們也發現使用自我評量的方式來給予壓力值,這樣的做法由於人本身的不確定性無法提供太精確的分類,我們把壓力狀態整合成為有壓力/沒壓力兩種分類後,準確率最高可以提高到78%左右,以各種生理訊號的組合平均來說約為68.48%,整體來說提昇了34%。 在本研究中,我們建立了一套完整的系統機制用來進行心理壓力研究,包括如何系統化地挑選進行壓力刺激、進行壓力刺激、收集生理訊號、分析生理訊號以及使用生理訊號辨識和預測心理壓力。由於呼吸是我們唯一可控制的生理訊號,可以用來協助調節壓力和情緒,因此我們建議穿戴式裝置上較好的壓力偵測訊號是皮膚導電度和心跳。 | zh_TW |
dc.description.abstract | Psychological stress is one of the major causes of physical and psychological diseases in the modern world that more than 70% hospital visitors are linked to it. Stress also results more than 200 billion economic loss in U.S. Therefore, we hope the stress research can detect stress in daily life in the future and improve health and well-beings of humankind.
We did 2 experiments in this research. Experiment 1 figures out the affective stimuli from relaxed to stressful is Quadrant 4 Quadrant 1 Quadrant 3 Quadrant 2. Experiment 2 proves that the stimuli sequence works. We proposed a method to choose appropriate affective multimedia contents to be stressors of the stress stimuli system. And we also figure out some good features and classification process for stress classification. Feature selection does help in reduce feature numbers of each physiological signals and improve the classification accuracies. Self-assessment is subjective label that can’t provide precise stress labels for classification. After we merge into 2 classes of stress, the maximum accuracy of classification is approximately 78%, improved about 34% in general. In this research, we proposed a completed process to research psychological stress, includes stress stimuli selection, stress stimulation, physiological signal collection, physiological signal analysis, and psychological stress recognition and prediction via physiological signals. Because of respiration is the only physiological signal which we can control by our will to reduce stress and adjust emotion. We recommend GSR and BVP is the best physiological signal to measure stress in wearable devices. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:04:45Z (GMT). No. of bitstreams: 1 ntu-105-P00922001-1.pdf: 2743310 bytes, checksum: 4e2086396b89c55e0c490a8dca1b0103 (MD5) Previous issue date: 2016 | en |
dc.description.tableofcontents | Chapter 1. Introduction 1
1.1. Motivation 1 1.2. Stress 2 1.3. Stress Theory 3 1.4. Problem and Proposed Solution 4 1.5. Thesis Organization 4 Chapter 2. Related Works 5 2.1. Stress Stimuli Systems 5 2.1.1. The Trier Social Stress Test 5 2.1.2. The Montreal Imaging Stress Task 6 2.1.3. Multimedia Stimuli 6 2.2. Stress Scales 7 2.2.1. Psychological Stress Scale 7 2.2.2. Computer-based stress scales 8 Chapter 3. Stress Stimuli System for Stress Detection 9 3.1. System Overview 9 3.2. The Properties of Affective System 9 3.3. Dimensional Model of Affect 10 3.4. Stress Stimuli 12 3.4.1. International Affective Picture System 12 3.4.2. Stress Stimuli Selection Method 14 3.4.3. Picture Stimuli Selection 15 3.4.4. Sound Stimuli Selection 15 3.4.5. Video Stimuli Selection 16 3.5. Biological Stress Measurements 17 Chapter 4. Experiments 19 4.1. Experiment I 20 4.1.1. Experiment Flow 20 4.1.2. Result & Interview 23 4.2. Experiment II 25 4.2.1. Experiment Flow 25 4.2.2. Result & Interview 27 Chapter 5. Conclusion 40 5.1. Conclusion 40 5.2. Future Work 40 Bibliographic 41 Figure 1. Motivation to objective roadmap 7 Figure 2. The transactional model of stress and coping system 9 Figure 3. The Trier Social Stress Test set-up and front view of the committee members. 11 Figure 4. The Montreal Imaging Stress Task. 12 Figure 5. Social Readjustment Rating Scale 13 Figure 15. The functionalities of the affective stimuli system. 15 Figure 6. Response decay. Top: the pattern and strikes applied to a bell. Middle: the bell's responses. Bottom: Sum of responses, presents the net intensity of the sound the bell products. 16 Figure 7. Left: Circumplex model of affect. Right: PAD emotional state model. 17 Figure 8. IAPS. Left: IAPS distribution on Circumplex Model of Affect. Right: Demo of IAPS. 18 Figure 9. The Self-assessment Manikin 19 Figure 10. The Stress Stimuli Selection Method. 20 Figure 11. The process of dubbing the chosen affective pictures. 21 Figure 12. The Affective stimuli video process. 22 Figure 13. The scoring table of the affective stimuli videos 22 Figure 14. Biological measures of stress. 24 Figure 16. Left: NeuroSky MindSet. Middle: IOM Hardware. Right: ProComp Infinite. 25 Figure 17. The overview of Experiment 1. 26 Figure 18. The basic information form for the participant. 26 Figure 19. The introduction of the experiment. 27 Figure 20. The process of Baseline. 27 Figure 21. The Stress Self-Assessment Manikin with 5 point test. 28 Figure 22. The process in a block. 28 Figure 23. The stress score of each block. 29 Figure 24. The classification accuracy of physiological signals 30 Figure 25. The stress stimuli sequence 30 Figure 26. The overview of Experiment 2 31 Figure 27. The introduction of Experiment 2. 31 Figure 28. The process of the Baseline block. 32 Figure 29. Left: The process of the Affective Stimuli block. Right: The process of a video part. 32 Figure 30. The process of Recovery block. 33 Figure 31. The Stress Scores of each blocks in Experiment 2. 33 Figure 32. The SVM accuracy of signal fusion. 43 Figure 33. The SVM accuracies of NFS and SFFS 44 Figure 34. The SVM accuracies of 2 classes 45 | |
dc.language.iso | en | |
dc.title | 基於生理訊號之心理壓力偵測 | zh_TW |
dc.title | Psychological Stress Detection Based on Physiological Signals | en |
dc.type | Thesis | |
dc.date.schoolyear | 104-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 李明穗(Ming-Sui (Amy),陳永昇(Yong-Sheng Chen) | |
dc.subject.keyword | 心理壓力,生理訊號,腦波,皮膚導電度,心跳,呼吸,穿戴式, | zh_TW |
dc.subject.keyword | Psychological stress,Physiological signal,EEG,GSR,Heart rate,Respiration,Wearable, | en |
dc.relation.page | 43 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2016-02-15 | |
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 |
顯示於系所單位: | 資訊工程學系 |
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