常見的監測生命徵象，例如監測呼吸（RR）和心跳（HR），通常需要穿戴接觸式的感應器，如生理監測儀和光體積變化描記圖法（PPG），然而接觸式感應器可能會造成皮膚刺激，並且對嬰兒和燒傷患者不適用。因此使用非接觸式感應器來監測生命徵象非常重要，一種常見的非接觸式監測生命徵象的方法為透過分析反射電磁波的相位差來推算生命徵象。
連續波（CW）雷達是一種傳統雷達基於電磁波的非接觸式監測生命徵象的方法，然而連續波雷達經常容易受到各種干擾源的干擾，包括隨機身體移動（RBM）和呼吸諧波。隨機身體移動和呼吸諧波是監測過程的重要干擾源，測量過程中，受測者可能會不知不覺晃動，而且隨機身體移動的晃動幅度遠大於生命徵象的幅度，這種差異導致監測結果不準確，監測到的訊號主要為身體移動而不是實際的生命徵象。此外呼吸訊號的振幅大於心跳訊號的振幅，並且第二或第三的呼吸諧波可能落在正常心率範圍內，因此在分析心跳時訊號的最大能量可能是呼吸訊號而不是心跳訊號。
本篇論文提出了應用於生命特徵偵測之干擾消除演算法基於連續波雷達，該演算法目的在於消除兩個主要干擾源的影響，並分別預測呼吸和心跳。演算法的第一部分稱為隨機身體移動消除（RBMC），它利用多項式擬合，多項式擬合是一種適用於非線性訊號擬合的有效方法，特別適合用在隨機身體移動引起的非規則變化的干擾，在執行隨機身體移動消除後接著預測呼吸。算法的第二部分稱為呼吸諧波消除（RHC），它利用連續迭代的四段線性波形（S-IFSLW），四段線性波形（FSLW）[1]可以提取額外的呼吸特徵，包括吐氣速度、吸氣速度和保持時間，通過對呼吸波形的更準確逼近的方法，四段線性波形有助於消除呼吸諧波，在執行呼吸諧波消除後接著預測心跳。
實驗使用了24GHz的連續波雷達和液晶（LC）發射天線來監測受測者的呼吸和心跳。本篇論文將接收到的信號劃分為四種類型：類型I表示理想的生命徵象，類型II表示較強的呼吸，類型III表示短時間的隨機身體移動，類型IV表示長時間的隨機身體移動。評估演算法的性能指標包括閾值內的準確率和平均誤差，用於評估隨機身體移動消除和呼吸諧波消除的改善程度。隨機身體移動消除的呼吸改善不顯著，因為隨機身體移動消除的主要目的是消除隨機身體移動引起的直流偏移，此外在隨機身體移動期間仍然可以測量呼吸訊號，這進一步限制了通過隨機身體移動消除的改進程度。呼吸諧波消除可以提高心跳的準確率並減小心跳的平均誤差，因為消除了呼吸諧波使得心跳訊號更明顯，特別是對於類型II的強呼吸，四種頻譜分析方法的平均準確率從3.25%提高到18.5%。

Conventional monitoring of vital signs, such as detecting respiration rate (RR) and heart rate (HR), usually requires wearing contact sensors like physiological monitors and photoplethysmography (PPG). However, contact sensors can cause skin irritation and are not suitable for infants and burn victims. Therefore, using non-contact sensors to detect vital signs is crucial. A popular non-contact method for detecting vital signs involves estimating the phase difference between received signals by analyzing the reflected electromagnetic (EM) wave.
Continuous wave (CW) radar is a conventional EM-based non-contact method for detecting vital signs. However, it is often susceptible to interference from various sources, including random body movement (RBM) and respiration harmonic. RBM and respiration harmonics are significant sources of interference for accurate detection. The object may unconsciously shake during the measurement, and the shaking amplitudes of random body movement are much greater than the vital signs. This discrepancy leads to inaccurate results, where the detected signals are attributed to movement rather than the actual vital signs. In addition, the amplitudes of respiration signals are greater than the amplitudes of heartbeat signals, and the second or third respiration harmonic may fall within the normal heart rate range. Consequently, when analyzing the heartbeat, the maximum energy of the signal may correspond to the respiration signal rather than the heartbeat signal.
The thesis proposes the interference cancellation algorithm for vital signs detection. The algorithm aims to eliminate two main sources of interference and predict the RR and HR respectively. The first part of the algorithm is called random body movement cancellation (RBMC) and utilizes polynomial fitting. Polynomial fitting is an effective method for fitting non-linear signals, which is particularly useful for irregular interference caused by random body movements. After performing RBMC, the algorithm proceeds to predict the RR. The second part of the algorithm is referred to as respiration harmonic cancellation (RHC) and utilizes a successive iterative four-segment linear waveform (S-IFSLW). The four-segment linear waveform (FSLW) [1] can extract additional respiration characteristics, including expiration rate, inspiration rate, and holding time. By providing a more accurate approximation of the respiration waveform, the FSLW aids in the elimination of respiration harmonics. After performing RHC, the algorithm moves on to predict the HR.
The experiments utilized a 24GHz continuous wave radar and a liquid crystal (LC) transmitting antenna to detect the RR and HR of the object. The received signals were classified into four types: Type I representing ideal vital signs, Type II indicating strong respiration, Type III representing short-time random body movement, and Type IV denoting long-time random body movement. The performance metrics of accuracy within the threshold and mean error were employed to evaluate the degree of improvement in RBMC and RHC. The RR improvement of RBMC is not significant because the main purpose of RBMC is to eliminate DC offset caused by random body movement. Additionally, respiration signals can still be measured during random body movement, which further restricts the extent of improvement achievable through RBMC. In general, RHC can improve the HR accuracy and reduce the HR mean error, because the cancellation of the respiration harmonics makes the heartbeat signal more obvious. Especially for Type II strong respiration, the average accuracy of the four spectrum analysis methods increased from 3.25% to 18.5%.

Abstract
Contents
1 Introduction 1
1.1 Measurement Technique of Vital Signs . . . . . . . . . . . . . . . . . . . 1
1.2 Continuous Wave Radar Vital Signs Analysis . . . . . . . . . . . . . . . . 2
1.2.1 Continuous Wave Radar Principle . . . . . . . . . . . . . . . . . . 2
1.2.2 CW Radar Detecting Vital Signs Processing Flow . . . . . . . . . 4
1.3 General Interference Sources . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.3.1 Random Body Movement . . . . . . . . . . . . . . . . . . . . . . 14
1.3.2 Respiration Harmonic . . . . . . . . . . . . . . . . . . . . . . . . 15
1.4 Research Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Interference Cancellation for Vital Signs Detection 17
2.1 Random Body Movement Cancellation . . . . . . . . . . . . . . . . . . . 17
2.2 Respiration Harmonic Cancellation . . . . . . . . . . . . . . . . . . . . . 21
3 Proposed Interference Cancellation Algorithm for Vital Signs Detection
Based on Continuous Wave Radar 29
4 Experiment Results of Vital Signs Detection 33
4.1 Experiment Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
4.2 Performance Evaluation Metrics of Vital Signs Detection . . . . . . . . . 37
4.3 Vital Signs Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.1 Type I: Ideal Vital Signs . . . . . . . . . . . . . . . . . . . . . . . 38
4.3.2 Type II: Strong Respiration . . . . . . . . . . . . . . . . . . . . . 39
4.3.3 Type III: Short-time Random Body Movement . . . . . . . . . . . 39
4.3.4 Type IV: Long-time Random Body Movement . . . . . . . . . . . 40
4.4 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4.1 Constellation of Raw Baseband Signal and Circle Fitting Compensation
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4.2 Random Body Movement Cancellation . . . . . . . . . . . . . . . 46
4.4.3 Respiration Harmonic Cancellation . . . . . . . . . . . . . . . . . 49
4.4.4 Spectrum Analysis Result . . . . . . . . . . . . . . . . . . . . . . 52
5 Conclusion and Future Work 57
References 59

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