本研究探討了FMCW(Frequency-Modulated Continuous Wave)毫米波雷達在非接觸式生理信號測量中的應用。本研究開發了一套信號處理演算法，用於從雷達數據中提取呼吸和心跳信息。通過實驗，實現了對呼吸、呼吸中止、心率的測量，並探索了胸腔位移預測血壓的可能性。
研究結果顯示，FMCW毫米波雷達技術在非接觸式生理監測領域具有可行性，包含在呼吸、呼吸中止檢測、心率與血壓估計方面。本研究提出了一個新的呼吸中止檢測演算法，可根據個體特徵進行參數調整，且不須經過機器學習。在血壓預測方面，本研究比較了支援向量機、決策樹和隨機森林等機器學習模型的可行性，最終成果可以達到英國高血壓學會(British Hypertension Society, BHS)定義之Grade C標準。心率估計方面，最終結果之平均絕對百分比誤差為2.88%，根據美國國家標準協會(American National Standards Institute, ANSI)制定標準以及與其他文獻相比，顯示本研究具有準確性。

This thesis investigates the application of FMCW (Frequency-Modulated Continuous Wave) millimeter-wave radar in non-contact physiological signal measurement. A series of signal processing algorithms was applied to extract respiration and heartbeat information from radar data. Through experiments, the thesis successfully measured respiration, apnea, and heart rate, and explored the potential of predicting blood pressure based on chest displacement.
The results indicate that FMCW millimeter-wave radar technology is feasible for non-contact physiological monitoring, particularly in the detection of respiration, apnea, heart rate, and blood pressure estimation. Additionally, a novel apnea detection algorithm was proposed, which allows for parameter adjustment based on individual characteristics without requiring machine learning. In terms of blood pressure prediction, the thesis compared the feasibility of machine learning models such as Support Vector Machines, Decision Trees, and Random Forests, ultimately achieving the Grade C standard as defined by the British Hypertension Society (BHS). In terms of heart rate estimation, the results yielded a mean absolute percentage error of 2.88%, which demonstrates accuracy according to the standards set by the American National Standards Institute (ANSI).

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