DSpace collection: 博碩士論文

環境友善摩擦奈米發電機之設計與開發;Sustainable Triboelectric Nanogenerators Enabled by Environmentally Friendly Materials and Structural Design

Fri, 06 Mar 2026 10:26:13 GMT

title: 環境友善摩擦奈米發電機之設計與開發;Sustainable Triboelectric Nanogenerators Enabled by Environmentally Friendly Materials and Structural Design abstract: 鈣鈦礦材料近年來在能源轉換與儲存領域引發關注，特別是在光電檢測被廣泛應用。另一方面，值得注意的是鈣鈦礦材料亦具備優秀摩擦起電特性，對於自供電感測系統設計方面具備高度潛力。然而，傳統含鉛鈣鈦礦材料的潛在毒性問題，制約其進階應用。因此，本研究致力於開發一種無金屬鈣鈦礦材料，其不僅具備生物相容性、化學穩定性與摩擦起電特性。階段性研究結果顯示，此新材料能提供穩定的輸出能力，並可整合於穿戴式系統。我們相信此研究成果為未來綠色能源與感測技術帶來新的機會。 ;In recent years, perovskite materials have attracted significant attention in the fields of energy conversion and storage, particularly due to their widespread applications in optoelectronic devices. Beyond their optoelectronic performance, it is noteworthy that perovskite materials also exhibit excellent triboelectric properties, highlighting their strong potential for the development of self-powered sensing systems. However, the practical advancement of conventional lead-based perovskites is severely constrained by concerns regarding their inherent toxicity and environmental risks. To address these limitations, this study focuses on the development of a metal-free perovskite material that simultaneously offers biocompatibility, chemical stability, and favorable triboelectric characteristics. The preliminary results demonstrate that the proposed material is capable of delivering stable electrical output and can be effectively integrated into wearable systems. We believe that these findings provide new opportunities for the advancement of environmentally friendly energy harvesting technologies and next-generation self-powered sensors.

腦波儀整合穿戴式裝置於睡眠評估之研究;The research on Integrating EEG Devices with Wearable Technology for Sleep Assessment

Fri, 06 Mar 2026 10:25:53 GMT

title: 腦波儀整合穿戴式裝置於睡眠評估之研究;The research on Integrating EEG Devices with Wearable Technology for Sleep Assessment abstract: 睡眠品質對於個人健康與生活品質至關重要。然而，現有的臨床睡眠監測方法（如多導睡眠圖，Polysomnography, PSG）往往受限於高成本、環境侷限性及使用不便性，難以應用於日常長期監測。本研究旨在評估整合可穿戴式乾式腦波儀（BrainLink Lite）與智慧型穿戴裝置（Apple Watch）於睡眠與腦功能監測的可行性。研究透過非侵入性方式，同步記錄多頻段腦波波形（Delta、Theta、Low Alpha、High Alpha、Low Beta、High Beta、Low Gamma、High Gamma）與生理數據（心率 HR、心率變異性 HRV、活動能量、睡眠階段），分析兩者間的關聯性。研究對象為一名健康成年受試者，於每日定時進行腦波記錄與生理數據同步蒐集。採用Pearson相關性分析、Spearman秩相關分析及隨機森林回歸等統計方法，探討前一晚睡眠生理數據與早晨腦波活動的關聯性。本研究期望證實了整合可穿戴式腦波儀與智慧型裝置進行睡眠與腦功能監測交互搭配的可行性，為發展基於消費級設備的個人化睡眠健康評估系統提供了實證基礎與分析方法框架。 ;Sleep quality is crucial for personal health and quality of life. However, existing clinical sleep monitoring methods, such as Polysomnography (PSG), are often limited by high costs, environmental constraints, and practical inconvenience, making them unsuitable for routine long-term monitoring. This study aims to evaluate the feasibility of integrating a wearable dry-electrode EEG device (BrainLink Lite) with a smartwatch (Apple Watch) for sleep and brain function monitoring. Through non-invasive methods, the study simultaneously records multi-band EEG waveforms (Delta, Theta, Low Alpha, High Alpha, Low Beta, High Beta, Low Gamma, High Gamma) and physiological data (heart rate HR, heart rate variability HRV, active energy, sleep stages) to analyze the correlations between them. The study involves one healthy adult participant who undergoes daily EEG recordings and synchronized physiological data collection. Statistical methods including Pearson correlation analysis, Spearman rank correlation, and Random Forest regression are employed to explore the associations between previous night′s sleep physiological data and morning EEG activity. This study aims to confirm the feasibility of integrating wearable EEG devices with smartwatches for combined sleep and brain function monitoring, providing empirical evidence and an analytical framework for developing personalized sleep health assessment systems based on consumer-grade devices.

結合心電圖與聽診雙模態之數位聽診器與深度學習模型開發：實現心肺疾病之遠距監測系統;Development of a Dual-Modal ECG–Auscultation Digital Stethoscope and Deep Learning Model: Enabling a Remote Monitoring System for Cardiopulmonary Diseas

Fri, 06 Mar 2026 10:25:35 GMT

title: 結合心電圖與聽診雙模態之數位聽診器與深度學習模型開發：實現心肺疾病之遠距監測系統;Development of a Dual-Modal ECG–Auscultation Digital Stethoscope and Deep Learning Model: Enabling a Remote Monitoring System for Cardiopulmonary Diseas abstract: 本研究在開發一套整合心電圖(Electrocardiogram, ECG)與心音(Phonocardiogram, PCG)的雙模態數位聽診系統，並結合深度學習模型以支援心臟衰竭(Heart Failure)之早期偵測與遠距監測。心臟衰竭為全球高度盛行的慢性疾病，其臨床表現常包含呼吸困難、下肢水腫與活動耐受度下降等，但早期症狀多屬非特異性，臨床評估高度仰賴醫師經驗。ECG 可反映心律、傳導路徑與心肌負荷變化；PCG 則提供瓣膜運動、心室壓力動態與異常血流聲響等機械資訊。若能同步量測 ECG 與 PCG，將有助於更精確地掌握心臟電氣—機械整合狀態，對心衰竭的即時判讀與長期追蹤具有重要價值。本研究設計一套可攜式、高整合度之硬體平台，包含心音／心電類比前端電路、濾波與偏壓設計、低雜訊電源管理、ADC 數位化架構，以及以 STM32L562 為核心的資料擷取系統。系統採用 SPI 介面搭配雙緩衝技術，成功達成即時資料寫入 SD 卡與 ECG–PCG 時序同步擷取。在心音分析方面，本研究以 YOLOV8 建構心雜音(murmur)偵測模型，並使用 CirCor DigiScope Dataset 進行訓練；同時導入連續與間斷白噪音的資料擴增策略，以增強模型在真實錄音環境下之魯棒性。實驗結果顯示，本系統可穩定取得高品質之 ECG 與 PCG 訊號，並能透過深度學習模型自動標定心雜音區段，於乾淨與高雜訊情境中皆展現良好辨識能力。綜合以上成果，本研究所提出的雙模態智慧聽診系統兼具便攜性、低功耗與自動化分析能力，可作為心臟衰竭之早期偵測工具，亦適合作為居家監測與遠距醫療的重要基礎平台，具有進一步發展為智慧醫療終端裝置的高度潛力。 ;This study presents the development of a dual-modal digital stethoscope that integrates electrocardiogram (ECG) and phonocardiogram (PCG) acquisition, combined with a deep learning–based analysis model to support early detection and remote monitoring of heart failure. Heart failure is a highly prevalent chronic disease worldwide, characterized by symptoms such as dyspnea, peripheral edema, and reduced exercise tolerance. However, its early manifestations are often nonspecific, making clinical assessment highly dependent on physician experience. ECG provides essential information on cardiac rhythm, conduction pathways, and myocardial loading conditions, whereas PCG reflects valvular function, ventricular pressure dynamics, and abnormal blood flow sounds. Simultaneous acquisition of both signals enables a more comprehensive assessment of the heart’s electromechanical behavior, which is crucial for timely interpretation and long-term management of heart failure. In this work, a portable and highly integrated measurement platform is developed, incorporating analog front-end circuits for PCG and ECG, band-pass filtering and biasing, low-noise power management, analog-to-digital conversion, and an STM32L562-based data acquisition system. The system employs an SPI interface with a double-buffering mechanism to achieve real-time SD-card data logging and reliable time-aligned ECG–PCG acquisition. For heart sound analysis, a YOLOV8-based murmur detection model is constructed using the CirCor DigiScope Dataset, with additional continuous and intermittent white-noise augmentation to improve robustness in realistic acoustic environments. Experimental results demonstrate that the proposed system successfully captures high-quality ECG and PCG signals synchronously and, with the aid of the deep learning model, can automatically identify murmur segments with stable performance under both clean and noisy conditions. Overall, the proposed dual-modal stethoscope system offers portability, low power consumption, and automated analysis capability. It serves as a promising foundation for early screening of heart failure, home-based physiological monitoring, and remote healthcare applications, with strong potential for future development into an intelligent medical device.

以 Wi-Fi 通道相位資訊建構即時非接觸式動態生理訊號系統：理論模型與臨床驗證;Development of a Real-Time, Non-Contact Dynamic Physiological Signal System Using Wi Fi Channel State Information Phase: Theoretical Modeling and Clinical Validation

Fri, 06 Mar 2026 10:25:24 GMT

title: 以 Wi-Fi 通道相位資訊建構即時非接觸式動態生理訊號系統：理論模型與臨床驗證;Development of a Real-Time, Non-Contact Dynamic Physiological Signal System Using Wi Fi Channel State Information Phase: Theoretical Modeling and Clinical Validation abstract: 本研究旨在建立一套以 Wi-Fi 通道狀態資訊（Channel State Information, CSI）為核心的非接觸式動態生理訊號監測系統，透過深入的理論推導與臨床級驗證，實現對呼吸與心跳等微小人體運動的即時估測。隨著睡眠障礙與慢性呼吸疾病在全球日益普遍，現有的穿戴式或接觸式監測裝置常因侵入性高、不適合長期使用而受限。因此，本研究提出一種利用環境中即可取得的 Wi-Fi 訊號進行生理監測的方法，期望提供高隱私、低成本且可長期部署的健康監測解決方案。研究核心基於 Wi-Fi 的 OFDM 與 MIMO 架構，從實體層(Physical Layer)提取 CSI，再以 Ray Tracing 模型描述訊號多路徑與動態反射特性。為使 CSI 相位資訊可用於解析胸腔與腹腔因呼吸造成的毫米級位移，本研究提出一套包含 Ratio Model 相位誤差消除、主成分分析（PCA）訊號強化、DC compensation 圓擬合校正及 Gabor Transform 時頻分析的完整演算法流程。此流程能有效從 CSI 中分離動態成分，估測瞬時頻率，再轉換為實際位移與速度資訊，並具備在低速、小振幅運動下仍保持高靈敏度的能力。為驗證系統效能，本研究設計三類實驗：線性滑軌週期運動模擬呼吸、極低速位移測試以評估演算法極限，以及人體實驗以觀察六類典型呼吸波型（Eupnea、Biot、Bradypnea、Sighing、Tachypnea、Kussmaul）。所有實驗均以高精度雷射測距模組作為對照組。結果顯示，本研究提出的方法在毫米至公分尺度範圍內皆能準確重建呼吸波型，並於頻率軌跡上呈現良好的一致性與抗雜訊能力。 ;This study aims to develop a non-contact dynamic physiological monitoring system based on Wi-Fi Channel State Information (CSI), capable of estimating subtle human motions—such as respiration and heartbeat—in real time through rigorous theoretical modeling and clinical-grade validation. As sleep disorders and chronic respiratory diseases continue to rise globally, existing wearable or contact-based monitoring devices often suffer from intrusiveness and poor suitability for long-term use. Therefore, this work proposes a Wi-Fi–based physiological sensing method that leverages ambient wireless signals, offering a high-privacy, low-cost, and long-term deployable solution for continuous health monitoring. The core of this research is built upon Wi-Fi OFDM and MIMO architectures, extracting CSI from the physical layer and modeling multipath propagation and dynamic reflections using a Ray Tracing framework. To enable CSI phase information to resolve millimeter-scale thoracic and abdominal motions induced by respiration, this study introduces a complete signal-processing pipeline that includes a Ratio Model for phase error elimination, Principal Component Analysis (PCA) for dynamic component enhancement, DC compensation via circular fitting, and Gabor Transform–based time-frequency analysis. This pipeline effectively isolates motion-induced variations within CSI, estimates instantaneous frequency, and converts it into displacement and velocity with high sensitivity, even under low-speed and small-amplitude movements. To validate system performance, three categories of experiments were conducted: linear-guide periodic motion to simulate breathing, ultra-low-speed displacement tests to evaluate algorithmic limits, and human-subject experiments featuring six representative respiratory patterns (Eupnea, Biot, Bradypnea, Sighing, Tachypnea, and Kussmaul). All measurements were benchmarked against a high-precision laser distance sensor. The results demonstrate that the proposed method can accurately reconstruct respiratory waveforms across millimeter-to-centimeter scales, maintaining strong consistency in frequency tracking while exhibiting robust noise resilience.