目前市面上所販售的手環或手錶大多都有量測心率的功能，一般是使用光體積變化描述術(Photoplethysmography, PPG)的光學心率感測器。可是由於運動時的手部晃動容易產生移動雜訊(Motion Artifact)，造成心率量測的準確度下降。為了改善此問題，本論文的主要目的是開發一款可消除移動雜訊的演算法，本研究主要是藉由一個穿戴式手環，其裝置內包含PPG和加速度感測器，此PPG與加速度訊號經取樣並轉換為數位訊號後經由藍牙無線傳輸傳送到一手機的APP內錄製，錄製完畢後將其訊號存取成.csv檔，之後再藉由PC端的Matlab開發環境做分析與演算法開發。
為了準確的偵測心率，此研究我們提出一個利用自適應濾波器來消除移動雜訊的方法，透過加速度計來量測移動雜訊。本研究之心率偵測演算法的性能經過實驗驗證並與市售的Polar心率帶做比較。其設計的演算法在靜止時的心率誤差結果為2.7 %，在從事飛輪動作時的心率誤差結果為6.6 %，而在跑步機運動時的心率誤差結果為5.6 %。
本研究使用的自適應濾波器可用來降低PPG訊號裡的移動雜訊，搭配心率偵測演算法可提高心率偵測的準確性。

Nowadays most of the wristbands or watches sold on the market feature with the function of measuring pulse rate, which generally equip with optical pulse rate sensors using Photoplethysmography. However, motion artifacts will decrease the PPG accuracy of pulse rate measurement. The main purpose of this thesis is to improve this problem by developing an algorithm that can reduce motion artifacts. In this study, we use a wristband with a PPG and acceleration sensor, The PPG and the an acceleration signals are sampled and converted to digital signal, and then transmitted to the APP of a mobile phone via bluetooth wireless transmission. After recording, the signal is recorded and saved as a .csv file. Then the algorithm will be analyzed and developed through the PC-based MATLAB development platform.
In order to detect the pulse rate more accurately, in this study we propose a method using an adaptive filter to eliminate motion artifacts, while motion artifacts are measured through triaxial accelerometer. The accuracy of the pulse rate detection algorithm in this study has been tested experimentally and compared with the polar pulse rate band sold on the market. The pulse rate error resulting from the algorithm designed in this study is 2.7% while sitting on a chair, 6.6% while riding a spinning bike, and 5.6% while running on a treadmill.
In this study, it is verified that the proposed algorithm can be used to reduce the motion artifacts in PPG signal, and that the accuracy of pulse rate detection can be improved.

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