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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 吳安宇(An-Yeu Wu) | |
| dc.contributor.author | Yi-Hsuan Wu | en |
| dc.contributor.author | 吳翊玄 | zh_TW |
| dc.date.accessioned | 2022-11-25T06:33:34Z | - |
| dc.date.copyright | 2021-08-18 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-07-22 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/82200 | - |
| dc.description.abstract | 隨著人類社會不斷進步，我們的生活品質獲得了提升，但同時心理健康問題如 憂鬱症、焦慮症等心理疾病相繼產生，並成為全世界需要面對的問題，而心理健康 相關問題的一大來源為工作壓力。心智負荷 (MentalWorkload) 被認為是個體可用 的資源量與作業情況所要求資源量之間的差異，較高的心智負荷意味在處理工作 時需消耗更大量的訊息處理能力，因此透過生理訊號評估心智負荷可即時反映工 作時的狀態，避免工作量超出自身負荷，並藉由生理訊號的長期監控維持心理健康。 然而，需達成長時間的監控必須透過穿戴式裝置，由於穿戴式裝置的便利性使 得活動不會受到限制，造成收錄到的訊號產生不穩定的情形，進而降低分類表現。 因此，我們在此論文提出了一個穩健的心理負荷偵測系統，透過基線飄移的去除及 非線性的特徵抽取提高對噪聲的容忍度。接著基於原本的系統，開發了針對光體積 描述訊號的輔助前處理系統，藉由心電訊號的標記及機器學習的輔助，能夠準確的 識別穿戴式中常見的運動偽影並進行去除，大幅降低穿戴式裝置不穩定所產生的 誤差。接著，將針對此誤差進行評估，在特徵值產生誤差的情況下，勢必會使分類 器產生不確定性，本文針對此不確定性提出了一個評估系統，將此不確定性量化為 可能分類錯誤的機率，藉此找出一些高機率分類錯誤的資料並將其去除，進而減少 誤判的情形。在此架構下，用於穿戴式裝置的光體積描述訊號的分類準確度可以大 幅提升，並能逼近心電訊號的分類表現。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-25T06:33:34Z (GMT). No. of bitstreams: 1 U0001-1007202120384500.pdf: 5011364 bytes, checksum: e36919837d227c64ee076a51d52ff0d6 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "Chapter 1 Introduction 1 1.1 Mental Workload Assessment 1 1.1.1 Mental Health Issue 1 1.1.2 Long-term Monitoring of Mental Workload 2 1.1.3 HRV-based Mental Workload Assessment Using PPG 5 1.2 Challenges of Mental Workload Detection on Smartwatch 6 1.3 Motivation and Main Contributions 7 1.4 Thesis Organization 11 Chapter 2 Review of Mental Workload Detection System 13 2.1 Recent Databases for Mental Workload Assessment 13 2.1.1 Stimuli 14 2.1.2 Physiological Modalities 15 2.1.3 Reference of Mental Workload Level 15 2.2 Experiment Setup for Our Database 17 2.2.1 Participants 17 2.2.2 Data Acquisition 17 2.2.3 Experimental Procedure 18 2.3 General Framework for HRV-based Mental Workload Detection 21 2.3.1 Peak Detection 21 2.3.2 HRV Feature Extraction 22 2.3.3 Machine Learning Classification 23 2.3.4 Results 24 2.4 Recent Works of Mental Workload Detection System Using Wearable Devices 25 2.5 Summary 26 Chapter 3 Enhanced HRV-based Mental Workload Detection System for PPG Watch 27 3.1 Proposed HRV-based Mental Workload Detection System 27 3.1.1 Traditional HRV Feature Extraction 28 3.1.2 Limitation of Traditional HRV Features under Noise 32 3.2 Baseline Wandering Removal 33 3.3 Nonlinear Time-delay Embedding Features 35 3.3.1 Poincaré Plot 36 3.3.2 Correlation Dimension (CD) 37 3.3.3 Effectiveness of Nonlinear Features 39 3.4 Simulation Results 42 3.4.1 Statistical Analysis 42 3.4.2 Classification Results 42 3.5 Summary 45 Chapter 4 ML-assisted Inter-beat Interval (IBI) Outlier Removal for HRV Analysis 47 4.1 IBI Outlier Removal 47 4.1.1 Effect of IBI Outliers on HRV Analysis 47 4.1.2 Traditional Methods for IBI Outlier Detection 49 4.2 PPG Pulse-based Quality Aware Outlier Detection Mechanism 51 4.3 ECG-assisted Outlier Labeling 52 4.4 Proposed ML-assisted Outlier Removal System 55 4.4.1 PPG Pulse Signal Quality Metrics 55 4.4.2 Outlier Detection Model - Extreme Gradient Boosting (XGBoost) 58 4.4.3 The Framework of the Proposed ML-assisted Outlier Removal System 59 4.5 Simulation Results 60 4.5.1 Evaluation Metrics 60 4.5.2 Classification Results 62 4.6 Summary 63 Chapter5 Uncertainty Quantification of SVM Classification for Mental Workload Detection 65 5.1 The Effect of Removed Beats on Mental Workload Detection 65 5.2 Influence of Removed Beats on HRV Features 66 5.2.1 Probability Distribution between Removed IBIs and Feature Error 66 5.2.2 Regression of Feature Error Estimation 68 5.3 Influence of Feature Error on SVM Classification 70 5.3.1 Linear SVM Model with Input Feature Errors 70 5.3.2 SVM Classification under Different Feature Errors 72 5.3.3 Uncertainty Estimation for Quantifying the Probability of Wrong Classification 73 5.3.4 Validation of Uncertainty Estimation Model 74 5.4 Proposed Framework of Robust Mental Workload Detection with Auxiliary Preprocessing System 76 5.4.1 Overview of the Proposed Framework 76 5.4.2 Determine Threshold for Rejecting Data with High Probability of Misclassification 77 5.5 Simulation Results 78 5.5.1 Experiment Setup 78 5.5.2 Classification Results 79 5.6 Summary 80 Chapter 6 Validation of Proposed Framework on Open Dataset, Conclusion, and Future Work 81 6.1 Validation of Proposed Robust Mental Workload Detection System 81 6.1.1 Dataset Candidates 81 6.1.2 Classification Results 83 6.2 Main Contributions 84 6.3 Future Directions 85 REFERENCE 86 " | |
| dc.language.iso | en | |
| dc.subject | 分類不確定性評估 | zh_TW |
| dc.subject | 心智負荷 | zh_TW |
| dc.subject | 光體積描述訊號 | zh_TW |
| dc.subject | 心率變異度 | zh_TW |
| dc.subject | 非線性映射方法 | zh_TW |
| dc.subject | 運動偽影去除 | zh_TW |
| dc.subject | Heart Rate Variability | en |
| dc.subject | Uncertainty Estimation of Classifier | en |
| dc.subject | Motion Artifact Correction | en |
| dc.subject | Nonlinear Method | en |
| dc.subject | Mental Workload | en |
| dc.subject | Photoplethysmogram | en |
| dc.title | 基於光體積描述訊號之長期監控心智負荷偵測系統 | zh_TW |
| dc.title | PPG-based Long-term Mental Workload Detection System | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 盧奕璋(Hsin-Tsai Liu),蔡佩芸(Chih-Yang Tseng),呂適任 | |
| dc.subject.keyword | 心智負荷,光體積描述訊號,心率變異度,非線性映射方法,運動偽影去除,分類不確定性評估, | zh_TW |
| dc.subject.keyword | Mental Workload,Photoplethysmogram,Heart Rate Variability,Nonlinear Method,Motion Artifact Correction,Uncertainty Estimation of Classifier, | en |
| dc.relation.page | 92 | |
| dc.identifier.doi | 10.6342/NTU202101382 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2021-07-22 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
| dc.date.embargo-lift | 2026-06-30 | - |
| 顯示於系所單位： | 電子工程學研究所 | |
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