使用基於超寬頻雷達的二階層EEMD方法偵測動靜態者之呼吸與心率

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姓名	邱鸞嬌(Luan-Jiau Chiu) 查詢紙本館藏	畢業系所	電機工程學系
論文名稱	使用基於超寬頻雷達的二階層EEMD方法偵測動靜態者之呼吸與心率 (UWB Radar Based Static/Dynamic Human Breathing and Heart Rate Detections Using Two-layer EEMD Method)
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摘要(中)	近代，超寬頻雷達常被用來做為遠距感測生命工具或非接觸式生理信號監視器，如：對車輛駕駛員進行健康監護。透過從身體反射的雷達回波，經接收與信號處理後(降低身體運動和環境雜訊等干擾)，可分離出心臟跳動和肺呼吸的信號，達到健康監護目的。但因心跳信號極微小，容易被呼吸諧波和環境雜波掩蓋，心跳、呼吸頻率又很接近，使得一般頻率濾波器難以分離它們，一般偵測方法也不易偵測心跳信號。再者，身體隨時在靜/動態中，身體運動回波信號遠大於兩個生理信號，導致信號干擾和相互作用問題；因此，傳統固定特徵檢測法，不太可能找到移動者生理活動特徵；且身體運動信號干擾影響，也是值得研究的課題。本論文提出二階層EEMD方法，可有效地排除雷達回波信號中環境干擾與系統雜訊，分離出人體呼吸和微弱心跳信號。並針對靜態/動態目標分別提出第一谷峰法(FVPIEF method)、多特徵對準法(MFA method)，利用雷達回波強度衰減和時間延遲、人體不同組織層導電介質特性，以偵測時間興趣區(TROI)及萃取基於局部最佳化的生理活動單特徵/多特徵，並校準身體運動影響。透過模擬和實驗，證明提出方法在實驗室和汽車環境中，可有效、可靠地評估靜/動態志願者呼吸率和心跳率。
摘要(英)	Nowadays, Ultra-wideband (UWB) radar is an important remote sensing tool of life detection or a non-contact monitor of the physiological signals, such as health monitoring for a vehicle driver. By processing the received UWB pulse echoes reflected from the body, different signals corresponding to heart activity and breathing, corrupted by body motion and the environment noise etc., are wanted to be separated clearly for health-monitoring purposes. However, the heartbeat signal is so tiny that it is covered by breathing harmonics and clutters. At the same time, since the frequencies of the vital signals are very close, usually around 1 Hz, it is difficult to apply an ordinary frequency filter to separate them apart. This problem induces that the vital signal detection method, usually, only detects the large breath signal, not the heartbeat signal. Further, the driver body is in static/dynamic situation at any time. Usually, the body motion echo signal is much larger than other twos, which will cause signal interference and interaction problems. Thus, conventional fixed feature detection methods are not likely to find those movable physiological active features. Meanwhile, to reduce physiological feature bias from body motion, and efficiently obtain valuable physiological information are also the subjects worthy of study. This dissertation proposes a two-layer EEMD method, which effectively eliminates environmental interference and system noise in radar echo signals, and separates human breathing and weak heartbeat signals. Moreover, for static/dynamic human vital detection, the FVPIEF (first valley-peak of IMF energy function) method and the MFA (multi-feature alignment) method are proposed. Both methods utilize the characteristics of the strength attenuation and time delay of UWB radar echo, and the characteristics of conductive media in different tissue layers of the human body to detect the time-region-of-interest (TROI) and extract the single-/multi- feature of vital activities based on local optimization, and calibrate body motion effects. The simulation and experiment results show the proposed methods can effectively and reliably evaluate breathing and heart rates in static or/and dynamic volunteer situation, both in laboratory and car.
關鍵字(中)	★ 對準 ★ 呼吸率 ★ 總體經驗模態分解法 ★ 心跳率 ★ 本質模態函數 ★ 遠距感測 ★ 追蹤 ★ 超寬頻雷達	關鍵字(英)	★ alignment ★ breathing rate ★ nsemble empirical mode decomposition (EEMD) ★ heart rate ★ intrinsic mode function (IMF) ★ remote sensing ★ tracking ★ ultra-wideband radar (UWB)
論文目次	CHAPTER I INTRODUCTION 1 1.1 Background and Motivation 1 1.2 Objectives of Dissertation 2 1.3 Survey of Previous Work 4 1.4 Organization of Dissertation 6 CHAPTER II METHODS AND MATERIALS 9 2.1 UWB Radar and FCC Regulations 9 2.2 Human Tissue and Dielectric Permittivity Model 14 2.3 Radio Propagation Models 16 2.3.1 Transmitter model 16 2.3.2 Human chest movement model 17 2.3.3 Channel model 18 2.3.4 Receiver model 18 2.4 EEMD and PBDEEMD 19 2.4.1 Ensemble empirical mode decomposition (EEMD) 19 2.4.2 Pseudo-bi-dimensional ensemble empirical mode decomposition (PBDEEMD) 21 CHAPTER III FVPIEF-BASED TWO-LAYER EEMD METHOD, BR/HR DETECTIONS IN STATIC HUMAN 24 3.1 Static detection issue and previous studies 24 3.2 Proposed FVPIEF Based Two-layer EEMD Method 26 3.3 Detecting Simulations in Different Signal-to-noise Ratio 31 3.3.1 UWB Tx/Rx signal simulation model 32 3.3.2 Vital detection in echo with different SNRs 35 3.4 Subjects and Task 40 3.5 Experiments of Human Physiological Activity Detection 40 3.5.1 Heartbeat detecting in breathing state 41 3.5.2 Heartbeat detecting in breath-holding state 42 3.5.3 Both breathing and breath-holding states 44 3.6 Summary 45 CHAPTER IV MULTI-FEATURE ALIGNMENT TWO-LAYER EEMD METHOD, BR/HR DETECTIONS IN DYNAMIC/STATIC HUMAN 46 4.1 Dynamic detection issue and previous studies 46 4.2 Extending Materials and Methods 48 4.2.1 UWB radar, human tissue and dielectric permittivity model 48 4.2.2 Radio propagation model of swinging human 49 4.2.3 Clutter suppression by PBDEEMD 51 4.3 Proposed Multi-feature Alignment Two-layer EEMD Method 53 4.3.1 Multilayer echo delay time model 53 4.3.2 Multi-feature model 54 4.3.3 Multi-feature Alignment (MFA) 58 4.3.4 Algorithm 60 4.4 Simulations of Various Human Swinging Paths 63 4.5 Experiments of Dynamic/Static Human in Real Environments 71 4.6 Summary 79 CHAPTER V CONCLUSION and FUTURE WORKS 81 5.1 Conclusion 81 5.2 Future Works 85 BIBLIOLOGRAPHY 87 PUBLISHING LIST 93
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指導教授	徐國鎧李柏磊(Kuo-Kai Shyu Po-Lei Lee)	審核日期	2020-7-22
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