本研究提出一種模板匹配奇異譜分析(Template-matching Singular Spectrum Analysis, TM-SSA)演算法，用以實時(Real-time)地追蹤人體之心率。透過雷達非接觸式地量測人體生理訊號是被廣泛討論的研究議題，然而量測期間的隨機身體移動(Random body movements, RBM)引入的巨大干擾將阻礙心率辨識，而間歇性的晃動干擾(Intermittent interference)是其中一種常見於實際情境中發生的類型。它們會隨機地出現，持續數秒後又突然消失，降低晃動期間的心率追蹤可靠性。本研究提出之演算法使用三個階段的訊號處理，試圖於如此巨大的干擾中準確追蹤人體的心率，首先使用奇異譜分析消除大部分晃動的能量，並藉由重建成分(Reconstructed components,RC)的奇異值留下干擾存在與否的標示；第二階段為匹配濾波器(Matched filter)，使用未干擾時前一階段提取的心跳波形作為模板，於干擾片段凸顯心率成分；最後利用心率於正常狀態下不劇烈偏移的特性，使用卡爾曼濾波器(Kalman filter)追蹤心率值，使雷達相位解調出的心率可以隨時間準確繪出其變化軌跡。實驗情境設為在30秒內存在5秒的身體晃動，使用8秒的窗口進行滑動資料擷取，以每秒為單位進行心率更新，可達成3%以下的錯誤率和0.02 Hz的誤差標準差，驗證了本論文提出之演算法的有效性與可靠性。

This study proposed a Template-Matching Singular Spectrum Analysis (TM-SSA) algorithm to track human heart rate in real-time. Non-contact measurement for physiological signals through radar has been widely discussed for decades. However, significant interference caused by random body movements (RBM) during measurement can hinder the identification of heart rate. Intermittent interference is a common type of RBM in practice. It occurs randomly, lasts for several seconds, and disappears suddenly, reducing the reliability of heart rate tracking during the interference.
The algorithm proposed uses a three-stage signal processing approach to accurately track human heart rate under the intermittent interference. First, Singular Spectrum Analysis (SSA) is employed to eliminate most of the energy caused by body movements. The singular values of the reconstructed components (RCs) are utilized to tell whether the interference occurs or not. Subsequently, template matching is used to extract the heart rate during interfered, where the clean heartbeat signals in previous segment serves as the template. Finally, Kalman filter is applied to further enhance robustness of the heart rate estimation, leveraging the characteristic that human heart rate won’t drastically deviate.
The experimental scenario is set with 5 seconds of body movement within a 30-second period, using an 8-second window for sliding data capture, with heart rate updates occurring every second. This approach achieves an error rate of less than 3% and a standard deviation of 0.02 Hz, validating the effectiveness and reliability of the algorithm proposed in this study.

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