「經驗模態分解（empirical mode decomposition；EMD）」已被證明可有效從高雜訊環境中成功分離呼吸訊號，然而呼吸訊號會隨機出現在不同的「本質模態函數（intrinsic mode functions；IMF）」，故篩選本質模態函數是採用經驗模態分解擷取呼吸訊號中最關鍵的技術。前人提出利用「多變量經驗模態分解（multivariate EMD；MEMD）」將三軸加速度器訊號拆解為三維本質模態函數組，並將傾角過大或過小之三維本質模態函數（也就是不可能由人類呼吸產生之訊號成分）剔除成功獲得呼吸訊號；但多變量經驗模態分解之運算量極大，不適合應用在嵌入式系統實作。本論文提出使用單軸加速度訊號之經驗模態分解搭配「趨勢波動分析（detrended fluctuation analysis；DFA）」選取單維本質模態函數，在同樣六個行車環境測試樣本下，皆可成功拆解出駕駛員之呼吸訊號，並大幅降低計算量。本論文同時提出「串流式趨勢波動分析計算（stream DFA）」，更可降低54%趨勢波動分析之計算量，使本論文所提出之單軸加速度器呼吸訊號擷取更適於嵌入式系統產品實作。 Empirical mode decomposition (EMD) is a proven technique to decompose breathing where from noisy signal recorded in severe environments. The breathing signal locates in arbitrary intrinsic mode functions (IMF) from EMD and selecting appropriate IMF to compose the breathing signal is critical for EMD-based breathing signal extraction. Multivariate EMD (MEMD) has been proposed to decompose 3-axis accelerometer signal into 3-dimension IMFs, the angle of which cannot be generated from human breathing is utilized as the feature to select IMF. However, MEMD needs huge computations and is not suitable for embedded implementation. This thesis proposes to use detrended fluctuation analysis (DFA) to select IMF from EMD results on 1-axis accelerometer signal. In our experiments, the accuracy on driver’s breathing extraction is comparable to that based on MEMD. In addition, stream DFA is proposed to reduce 54% computations, which makes the proposed breathing extraction with 1-axis accelerometer much more suitable for embedded systems.