遠距光體積變化描述術(remote Photoplethysmography，rPPG)技術可以在健身運動期間使用，去量測運動者的即時脈搏律，但是脈搏訊號很容易因為運動中身體晃動或光照變化而導致錯誤的脈搏律估計。雖然先前有學者針對運動偽影與光線變化進行研究，但是當運動雜訊過大時，脈搏訊號仍然很容易失真導致估計出錯誤的心律。此外，先前的研究大多使用個人電腦做為開發環境去實現他們的方法。
為了更好地解決這些問題，本論文希望在一低價的SoC平台上進行了這項非接觸式的脈搏律量測研究，透過攝影機的影像當輸入，並透過影像處理與數位訊號處理分析出rPPG訊號，進而推算出脈搏律。除此之外，我們也使用單指令多數據(Single Instruction Multiple Data, SIMD)技術去優化演算法，以達到即時的運算。三項運動器材的實驗中，本論文所提出的非接觸式量測方法所得到的平均絕對誤差/均方跟誤差於健身腳踏車、踏步機與跑步機分別為3.64/4.95 BPM、3.39/4.94 BPM與4.77/7.01 BPM，而Success Rate-5/10分別為0.66/0.81、0.73/0.89與0.73/0.87。在執行時間上，本論文的方法在Raspberry Pi 3 Model B plus平台上的整體執行時間約為26ms左右，即相機的幀率可以穩定在30FPS (Frame Per Second)。

The remote photoplethysmography (rPPG) technique can be used during fitness exercise to optimize the effectiveness of a workout. But the pulse signals may lead to erroneous estimates because body motion and illumination variations in practice. Therefore, many studies have been proposed different methods to reduce motion artifacts and tackle the illumination variation challenge. However, stronger movements created in an exercise will still cause rPPG signal distortion easily. Moreover, most of the previous studies implemented their algorithm using language C or MATLAB on personal computer.
In order to solve these problems, we use a SoC platform to construct a contactless pulse rate measurement system. A webcam is as an input and the output is the estimated pulse rate. The rPPG signal is analyzed using image processing and digital signal processing. We also optimize the algorithm using single instruction multiple data (SIMD) technology with a system on chip (SOC) Platform. In the experiment, the mean absolute error (MAE) / root mean square error (RMSE) of pulse rate measurement are 3.64/4.95 BPM in Biking; 3.39/4.94 BPM in Stepper and 4.77/7.01 BPM in treadmill running. The results also reveal that our method can run at 30 FPS (Frame Per Second) on Raspberry Pi 3 Model B plus.

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