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題名 應用虛擬實境探索睡眠輔助機制
Exploring Sleep Assistance Mechanisms through Virtual Reality作者 陳恩誠
Chen, En-Chen貢獻者 李蔡彥
Li, Tsai-Yen
陳恩誠
Chen, En-Chen關鍵詞 虛擬實境
腦波
失眠
心率變異數
內隱記憶
機器學習
線性回歸
Virtual Reality (VR)
Insomnia
Electroencephalogram (EEG)
Heart Rate Variability (HRV)
Implicit Memory
Machine Learning (ML)
Linear Regression日期 2025 上傳時間 4-Aug-2025 13:57:08 (UTC+8) 摘要 近年研究顯示，自然環境對於緩解壓力和提高睡眠品質有顯著的影響。本研究將參考生物心理社會模式，採用跨領域整合的方法，結合電腦科學、生物科學與行為科學，探討虛擬自然環境在睡眠改善上的潛力與機制。我們的研究有三個主要的目標，第一個目標是運用虛擬實境（VR）技術建構不同的自然場景，探討在不同情境中的情緒反應與機制。第二個研究目標將收集有睡眠困擾與健康睡眠者在不同情境的生理數據，融合沉浸式VR的體驗和機器學習對睡眠品質進行分類，分析兩者在調節機制上的差異特徵。最終，我們設計了一個可客製化的VR環境(VRE)，以協助提升失眠者的睡眠品質，進而提供臨床應用的有效輔助工具。 睡眠主要是受到三個系統的影響，包括恆定穩態系統、晝夜節律系統和喚醒系統。我們研究的焦點在於虛擬技術中擬真的大自然環境對身心的影響效果，特別是睡眠行為發生在晝夜節律的夜間階段，啟發我們建構黃昏到深夜的虛擬場景，探討對放鬆與促發睡意的有效性。因此，我們開發了三個虛擬場景，分別為日落、月光和星空。我們分析參與者在各場景中的情緒反應，特別針對喚醒系統與自律神經系統相關的情緒變化，包括孤獨、悲傷、焦慮、放鬆、平靜和快樂。結果顯示，「營火下的星空場景」最有助於誘導放鬆、平靜與睡意。我們進一步比較星空營火、閉眼冥想與白天海邊三種情境下，參與者從壓力到放鬆的身心反應。雖然，所有情境皆可促進放鬆，但星空營火場景在降低心率與增加睡意方面效果最顯著。 我們進一步針對星空和營火的虛擬場景來研究涉及睡眠之調節機制的生理特徵。針對調節機制，在研究中假設四個腦波對環境刺激的反應調節參數：α比率（AR）、α-β比率（ABR）、θ比率（TR）和highβ比率（hBR）。結果顯示，在使用沉浸式VR放鬆模式，睡眠品質良好的人和睡眠品質不佳的人在AR、TR 和hBR方面有顯著差異。另外，考量臨床研究中常見的小樣本限制，本研究導入支援向量機(SVM)進行分類模型訓練，並透過主成分分析（PCA）方法降維以進一步分析。結果顯示，沉浸式VR放鬆模型可以有效地區分睡眠品質良好和不佳的個體，且分類的準確率(accuracy)與F1分數穩定一致，說明了其評估睡眠品質的可靠性。此階段研究顯示，沉浸式體驗能揭示睡眠困難者在生理調節上的困境，並為未來運用VR技術識別與改善睡眠問題提供實證基礎。 在研究的最後階段，我們考慮到大腦中的腦島對情境故事的敏感性與內隱記憶對情境的投射效應，設計了可客製化與產生時間變化的虛擬場景，試圖誘發個案對於睡眠行為的動機與感受。研究結果驗證了我們的假設，客製化且含有時間變化的VRE能顯著增強放鬆、改善睡眠品質，並有效降低負面情緒，避免喚醒系統的活化。透過線性迴歸分析，我們進一步確認了與睡眠品質改善相關的關鍵生理調節參數，包括AR、hBR、訓練期間的θ比率（trainTR）。在這三項指標中，實驗組與對照組間出現明顯的變化趨勢，進一步強化了沉浸式虛擬環境對於身心調節與睡眠改善的科學依據。 總結而言，我們整合心理學、生理監測與電腦科學的跨域研究架構，證實沉浸式虛擬自然環境能有效誘發情緒與生理反應，促進身心放鬆並改善睡眠品質。在未來的研究，我們期望能進一步發展基於人工智慧自動化的客製化互動虛擬環境系統，為智慧健康與實體人工智慧（physical AI）的實踐與應用貢獻創新動能。
Recent studies have shown that natural environments have a significant impact on stress reduction and improve sleep quality. This research adopts a biopsychosocial model to employ a cross-disciplinary approach that integrates computer science, biological sciences, and behavioral sciences to explore the potential and mechanisms of virtual natural environments in improving sleep. Our study has three primary objectives. The first is to utilize virtual reality (VR) technology to create natural scenes at different times and examine emotional responses and regulatory mechanisms in various scenarios. The second objective is to collect physiological data from both individuals with sleep disturbances and healthy sleepers in different VR settings, integrating immersive VR experiences with machine learning techniques to classify sleep quality and analyze differences in their regulatory mechanisms. Ultimately, our goal is to develop a customizable VR environment (VRE) that improves the sleep quality of individuals with insomnia, thus providing an effective tool for clinical application. Sleep is mainly influenced by three systems: the homeostatic system, the circadian rhythm, and the arousal system. Our research investigates how realistic natural environments, created through virtual technology, impact the mind and body, particularly during the nighttime phase of the circadian rhythm when sleep typically occurs. This exploration prompted us to develop virtual scenes that transition from sunset to late night, allowing us to test their effectiveness in promoting relaxation and inducing sleepiness. We created three virtual scenarios: sunset, moonlight, and starry night. We analyze emotional responses to each scene, focusing on changes in emotions related to arousal and the autonomic nervous system, including feelings of loneliness, sadness, anxiety, relaxation, calmness, and happiness. The results indicated that the "starry sky with a campfire" scene was the most effective in promoting relaxation, calmness, and sleepiness. Additionally, we compared the physical and psychological responses of the participants to stress and relaxation in three conditions: a starry campfire scene, eye-closed meditation, and a daytime beach scene. Although all conditions promoted relaxation, the starry campfire scene proved to be the most effective in lowering heart rate and improving sleepiness. In addition, we investigated the physiological features associated with sleep regulation in virtual environments. The study proposed four EEG-based indicators as potential markers of regulatory responses to environmental stimuli: the alpha ratio (AR), the alpha-beta ratio (ABR), the theta ratio (TR), and the high-beta ratio (hBR). The findings revealed significant differences in AR, TR, and hBR between people with good and poor sleep quality during immersive VR relaxation experiences. Considering the common limitation of small sample sizes in clinical research, we applied a support vector machine (SVM) to develop a classification model and utilized principal component analysis (PCA) for dimensionality reduction. The results indicated that the immersive VR relaxation model effectively differentiated between good and poor sleepers, demonstrating consistent accuracy and F1 scores, supporting the model's reliability in assessing sleep quality. This phase of the study emphasizes how immersive experiences can uncover physiological regulators of people with sleep difficulties. It also provides empirical support for the use of VR technologies in identifying and potentially alleviating sleep-related issues. Recognizing the insula's sensitivity to contextual storytelling and the projective nature of implicit memory in VR settings, we designed customizable and time-evolving virtual environments for the study's final stage. These environments were intended to evoke the motivation and emotional involvement of participants in sleep-related behaviors. The results validated our hypothesis: personalized and temporally dynamic VRE significantly enhanced relaxation, improved sleep quality, reduced negative emotions, and suppressed activation of the arousal system. Using linear regression analysis, we identified key physiological parameters associated with improved sleep quality, including AR, hBR, and the theta ratio during training (trainTR). These three indicators exhibited significant trends of change between the experimental and control groups, further strengthening the scientific foundation for immersive VREs in psychophysiological regulation and sleep enhancement. In conclusion, our research demonstrates that immersive virtual natural environments, through a cross-interdisciplinary framework that integrates psychology, physiological monitoring, and computer science, can effectively induce emotional and physiological responses, promote mind-body relaxation, and improve sleep quality. As this field continues to advance, we anticipate the emergence of AI-driven, automated, and personalized interactive virtual environments, which will provide innovative contributions to smart health technologies and the practical application of physical artificial intelligence (physical AI).參考文獻 [1] R. H. Adler, "Engel's biopsychosocial model is still relevant today," Journal of psychosomatic research, vol. 67, no. 6, pp. 607-611, 2009. 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Gradisar, "A bidirectional model of sleep and technology use: a theoretical review of how much, for whom, and which mechanisms," Sleep Medicine Reviews, p. 101933, 2024. 描述 博士
國立政治大學
資訊科學系
107753502資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107753502 資料類型 thesis dc.contributor.advisor 李蔡彥 zh_TW dc.contributor.advisor Li, Tsai-Yen en_US dc.contributor.author (Authors) 陳恩誠 zh_TW dc.contributor.author (Authors) Chen, En-Chen en_US dc.creator (作者) 陳恩誠 zh_TW dc.creator (作者) Chen, En-Chen en_US dc.date (日期) 2025 en_US dc.date.accessioned 4-Aug-2025 13:57:08 (UTC+8) - dc.date.available 4-Aug-2025 13:57:08 (UTC+8) - dc.date.issued (上傳時間) 4-Aug-2025 13:57:08 (UTC+8) - dc.identifier (Other Identifiers) G0107753502 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/158472 - dc.description (描述) 博士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 資訊科學系 zh_TW dc.description (描述) 107753502 zh_TW dc.description.abstract (摘要) 近年研究顯示，自然環境對於緩解壓力和提高睡眠品質有顯著的影響。本研究將參考生物心理社會模式，採用跨領域整合的方法，結合電腦科學、生物科學與行為科學，探討虛擬自然環境在睡眠改善上的潛力與機制。我們的研究有三個主要的目標，第一個目標是運用虛擬實境（VR）技術建構不同的自然場景，探討在不同情境中的情緒反應與機制。第二個研究目標將收集有睡眠困擾與健康睡眠者在不同情境的生理數據，融合沉浸式VR的體驗和機器學習對睡眠品質進行分類，分析兩者在調節機制上的差異特徵。最終，我們設計了一個可客製化的VR環境(VRE)，以協助提升失眠者的睡眠品質，進而提供臨床應用的有效輔助工具。 睡眠主要是受到三個系統的影響，包括恆定穩態系統、晝夜節律系統和喚醒系統。我們研究的焦點在於虛擬技術中擬真的大自然環境對身心的影響效果，特別是睡眠行為發生在晝夜節律的夜間階段，啟發我們建構黃昏到深夜的虛擬場景，探討對放鬆與促發睡意的有效性。因此，我們開發了三個虛擬場景，分別為日落、月光和星空。我們分析參與者在各場景中的情緒反應，特別針對喚醒系統與自律神經系統相關的情緒變化，包括孤獨、悲傷、焦慮、放鬆、平靜和快樂。結果顯示，「營火下的星空場景」最有助於誘導放鬆、平靜與睡意。我們進一步比較星空營火、閉眼冥想與白天海邊三種情境下，參與者從壓力到放鬆的身心反應。雖然，所有情境皆可促進放鬆，但星空營火場景在降低心率與增加睡意方面效果最顯著。 我們進一步針對星空和營火的虛擬場景來研究涉及睡眠之調節機制的生理特徵。針對調節機制，在研究中假設四個腦波對環境刺激的反應調節參數：α比率（AR）、α-β比率（ABR）、θ比率（TR）和highβ比率（hBR）。結果顯示，在使用沉浸式VR放鬆模式，睡眠品質良好的人和睡眠品質不佳的人在AR、TR 和hBR方面有顯著差異。另外，考量臨床研究中常見的小樣本限制，本研究導入支援向量機(SVM)進行分類模型訓練，並透過主成分分析（PCA）方法降維以進一步分析。結果顯示，沉浸式VR放鬆模型可以有效地區分睡眠品質良好和不佳的個體，且分類的準確率(accuracy)與F1分數穩定一致，說明了其評估睡眠品質的可靠性。此階段研究顯示，沉浸式體驗能揭示睡眠困難者在生理調節上的困境，並為未來運用VR技術識別與改善睡眠問題提供實證基礎。 在研究的最後階段，我們考慮到大腦中的腦島對情境故事的敏感性與內隱記憶對情境的投射效應，設計了可客製化與產生時間變化的虛擬場景，試圖誘發個案對於睡眠行為的動機與感受。研究結果驗證了我們的假設，客製化且含有時間變化的VRE能顯著增強放鬆、改善睡眠品質，並有效降低負面情緒，避免喚醒系統的活化。透過線性迴歸分析，我們進一步確認了與睡眠品質改善相關的關鍵生理調節參數，包括AR、hBR、訓練期間的θ比率（trainTR）。在這三項指標中，實驗組與對照組間出現明顯的變化趨勢，進一步強化了沉浸式虛擬環境對於身心調節與睡眠改善的科學依據。 總結而言，我們整合心理學、生理監測與電腦科學的跨域研究架構，證實沉浸式虛擬自然環境能有效誘發情緒與生理反應，促進身心放鬆並改善睡眠品質。在未來的研究，我們期望能進一步發展基於人工智慧自動化的客製化互動虛擬環境系統，為智慧健康與實體人工智慧（physical AI）的實踐與應用貢獻創新動能。 zh_TW dc.description.abstract (摘要) Recent studies have shown that natural environments have a significant impact on stress reduction and improve sleep quality. This research adopts a biopsychosocial model to employ a cross-disciplinary approach that integrates computer science, biological sciences, and behavioral sciences to explore the potential and mechanisms of virtual natural environments in improving sleep. Our study has three primary objectives. The first is to utilize virtual reality (VR) technology to create natural scenes at different times and examine emotional responses and regulatory mechanisms in various scenarios. The second objective is to collect physiological data from both individuals with sleep disturbances and healthy sleepers in different VR settings, integrating immersive VR experiences with machine learning techniques to classify sleep quality and analyze differences in their regulatory mechanisms. Ultimately, our goal is to develop a customizable VR environment (VRE) that improves the sleep quality of individuals with insomnia, thus providing an effective tool for clinical application. Sleep is mainly influenced by three systems: the homeostatic system, the circadian rhythm, and the arousal system. Our research investigates how realistic natural environments, created through virtual technology, impact the mind and body, particularly during the nighttime phase of the circadian rhythm when sleep typically occurs. This exploration prompted us to develop virtual scenes that transition from sunset to late night, allowing us to test their effectiveness in promoting relaxation and inducing sleepiness. We created three virtual scenarios: sunset, moonlight, and starry night. We analyze emotional responses to each scene, focusing on changes in emotions related to arousal and the autonomic nervous system, including feelings of loneliness, sadness, anxiety, relaxation, calmness, and happiness. The results indicated that the "starry sky with a campfire" scene was the most effective in promoting relaxation, calmness, and sleepiness. Additionally, we compared the physical and psychological responses of the participants to stress and relaxation in three conditions: a starry campfire scene, eye-closed meditation, and a daytime beach scene. Although all conditions promoted relaxation, the starry campfire scene proved to be the most effective in lowering heart rate and improving sleepiness. In addition, we investigated the physiological features associated with sleep regulation in virtual environments. The study proposed four EEG-based indicators as potential markers of regulatory responses to environmental stimuli: the alpha ratio (AR), the alpha-beta ratio (ABR), the theta ratio (TR), and the high-beta ratio (hBR). The findings revealed significant differences in AR, TR, and hBR between people with good and poor sleep quality during immersive VR relaxation experiences. Considering the common limitation of small sample sizes in clinical research, we applied a support vector machine (SVM) to develop a classification model and utilized principal component analysis (PCA) for dimensionality reduction. The results indicated that the immersive VR relaxation model effectively differentiated between good and poor sleepers, demonstrating consistent accuracy and F1 scores, supporting the model's reliability in assessing sleep quality. This phase of the study emphasizes how immersive experiences can uncover physiological regulators of people with sleep difficulties. It also provides empirical support for the use of VR technologies in identifying and potentially alleviating sleep-related issues. Recognizing the insula's sensitivity to contextual storytelling and the projective nature of implicit memory in VR settings, we designed customizable and time-evolving virtual environments for the study's final stage. These environments were intended to evoke the motivation and emotional involvement of participants in sleep-related behaviors. The results validated our hypothesis: personalized and temporally dynamic VRE significantly enhanced relaxation, improved sleep quality, reduced negative emotions, and suppressed activation of the arousal system. Using linear regression analysis, we identified key physiological parameters associated with improved sleep quality, including AR, hBR, and the theta ratio during training (trainTR). These three indicators exhibited significant trends of change between the experimental and control groups, further strengthening the scientific foundation for immersive VREs in psychophysiological regulation and sleep enhancement. In conclusion, our research demonstrates that immersive virtual natural environments, through a cross-interdisciplinary framework that integrates psychology, physiological monitoring, and computer science, can effectively induce emotional and physiological responses, promote mind-body relaxation, and improve sleep quality. As this field continues to advance, we anticipate the emergence of AI-driven, automated, and personalized interactive virtual environments, which will provide innovative contributions to smart health technologies and the practical application of physical artificial intelligence (physical AI). en_US dc.description.tableofcontents 致謝 I 摘要 II Abstract IV Contents VII List of Figures IX List of Tables XII Chapter 1 Introduction 1 1.1 Motivation and Issues 2 1.2 The Objectives of the Study 5 1.3 Main Contributions 6 Chapter 2 Literature Review 7 2.1 The Arousal System of The Sleep Mechanism 7 2.2 The Features of Biomedical Signals of Sleep Behavior 11 2.3 The Application Strategies of Virtual Reality in Treating Insomnia 15 Chapter 3 Study of the Relaxation Effects of Virtual Reality: A Pilot Study 20 3.1 Objectives and Hypothesis 20 3.2 Study Design and Procedure 21 3.3 Results and Discussion 23 3.4 Conclusions 27 Chapter 4 Study of Different Virtual Reality Environments for Psychological Perception: Virtual Scenes Design 29 4.1 Related Work 29 4.2 Objective and Hypothesis 32 4.3 Study Design and Procedure 34 4.3.1 The Initial Study 34 4.3.2 The Final Study 37 4.4 Results and Discussion 40 4.4.1 Investigation of Virtual Scenes on Psychological Perception 40 4.4.2 Effectiveness of Virtual Scenes for Relaxation and Sleepiness 53 4.5 Conclusions 63 Chapter 5 Analysis of EEG and ANS Features Related to Sleep Quality through Virtual Reality Environments: Characterization of Neurophysiological Patterns As-sociated with Sleep Quality in Digital Intervention 66 5.1 Related Work 66 5.2 Objectives and Hypothesis 70 5.3 Study Design and Procedure 76 5.4 Results and Discussion 78 5.4.1 Exploring the Recognition of Physiological Characteristics of Good and Poor Sleepers 78 5.4.2 Using Machine Learning Techniques to Classify Good and Poor Sleepers 84 5.5 Conclusions 93 Chapter 6 Study on the Applicability of Virtual Scenarios in Improving Sleep Quality: Digital Sleep Improvement Intervention 97 6.1 Related Work 97 6.2 Objectives and Hypothesis 101 6.3 Study Design and Procedure 102 6.4 Results and Discussion 104 6.5 Conclusion 120 Chapter 7 Conclusions 124 7.1 Summary of Findings 124 7.2 Theoretical and Practical Contributions 125 7.3 Limitations and Future Work 126 7.4 Conclusion 127 Reference 128 zh_TW dc.format.extent 3976020 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107753502 en_US dc.subject (關鍵詞) 虛擬實境 zh_TW dc.subject (關鍵詞) 腦波 zh_TW dc.subject (關鍵詞) 失眠 zh_TW dc.subject (關鍵詞) 心率變異數 zh_TW dc.subject (關鍵詞) 內隱記憶 zh_TW dc.subject (關鍵詞) 機器學習 zh_TW dc.subject (關鍵詞) 線性回歸 zh_TW dc.subject (關鍵詞) Virtual Reality (VR) en_US dc.subject (關鍵詞) Insomnia en_US dc.subject (關鍵詞) Electroencephalogram (EEG) en_US dc.subject (關鍵詞) Heart Rate Variability (HRV) en_US dc.subject (關鍵詞) Implicit Memory en_US dc.subject (關鍵詞) Machine Learning (ML) en_US dc.subject (關鍵詞) Linear Regression en_US dc.title (題名) 應用虛擬實境探索睡眠輔助機制 zh_TW dc.title (題名) Exploring Sleep Assistance Mechanisms through Virtual Reality en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) [1] R. 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