本論文開發一套基於PPG和加速度頻譜之能量分析的運動心律偵測演算法，並將此演算法實現於Android手機以作為心律監測平台，結合方便攜帶之耳道式耳機型感測器，形成一套耳道式運動心律監測系統。本論文所開發之耳道式耳機型感測器利用PPG感測模組與加速度感測模組收集PPG和加速訊號，再藉由藍芽將訊號傳輸至Android心律監測平台，透過訊號運算過後，監測平台會呈現即時心律分析相關結果。其中心律之結果乃基於本論文開發之運動心律偵測演算法，此演算法的目的在於解決活動時動作雜訊對於PPG訊號的影響。於現有文獻中，大多使用不同數學工具或濾波器來消除PPG訊號裡的動作雜訊，有別於消除動作雜訊的方法，本論文開發新的一套PPG和加速度頻譜之能量分析的運動心律偵測演算法，探討PPG與加速度訊號於頻域能量本質與其變化的意義，應用於即時偵測人體長時間活動的心律。並透過實驗收案，驗證本論文之運動心律偵測演算法的準確度；本論文以判定係數R2及Bland-Altman ratio評估演算法偵測心律與實際結果之線性相關及一致性，其中當視窗長度10秒且視窗位移時間3~5秒時，R2大於0.85且Bland-Altman ratio小於0.1，有高度的線性相關與一致性，驗證此運動心律偵測演算法應用於偵測人體活動時的心律之可行性與有效性。

This thesis presents an in-ear motion heart rate monitoring system based on photoplethysmography (PPG) and acceleration signals. The system is composed of an in-ear headset sensor module and a heart rate monitoring application running on Android devices. The in-ear headset sensor module collects PPG and acceleration signals and transmits them to the heart rate monitoring application via Bluetooth transmission for motion heart rate detection. The heart rate monitoring application executes a motion heart rate detection algorithm that solves the problem that PPG signal is easily corrupted by motion artifacts (MA) when users are in motion. In existing research studies, most researchers used mathematical tools or filters to reduce MA in order to recover PPG signal for heart rate detection. In this paper, the proposed method for heart rate detection from MA corrupted PPG signals is to analyze the power change in the spectra of PPG and acceleration signals. This method can detect heart rate directly without conducting MA cancellation in corrupted PPG signals. Several experiments have been conducted to validate feasibility and effectiveness of the proposed method. The result indicates that the heart rate obtained by the proposed method and Electrocardiogram (ECG) signal are linearly correlated and in a high degree of consistency. Moreover, the error is also within 5% to achieve the minimum requirement of the system applied to heart rate detection of daily activities.

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