近年來運動風氣的提升，帶動相關產業蓬勃發展，而運動過程的心跳變化常被視為一項評估運動強度和訓練成果的指標。因此，倘若能將新興的非接觸式心率量測技術與運動場域加以結合，透過其所具備的高舒適、高便利和低風險等優勢，不僅能讓使用者於運動過程中避免因配戴穿戴式裝置而造成的身體不適感或皮膚過敏等現象，亦能降低病毒接觸感染的風險，進而提升整體的運動成效。
目前非接觸式心率量測技術應用於運動情境仍存在部分問題需要改善，主要重點整理如下：(1)大部分研究專注於如何消除運動過程所產生的雜訊，卻忽略分析了量測範圍內的所有像素點是否皆如期具有心率訊號。(2)當心率發生快速變化時，傅立葉轉換之取樣數量會使量測結果呈現延遲情形。(3)當進行具有週期性活動的運動項目(例如：跑步機)時，由於運動過程會產生週期性晃動雜訊，若此雜訊剛好介於心率頻段時，便將可能造成頻譜上難以明確辨別何者為真實的心跳脈動頻率，進而導致錯誤的心率量測結果。(4)由於非接觸式心率量測技術的演算法較為複雜，相關研究皆選擇將運動過程錄製成影片後，再以離線的方式進行量測分析，如此將無法於實際場域中即時提供量測資訊給予使用者參考。
因此，本論文基於影像感測器實現一套即時的非接觸式心率量測系統，並對於上述相關問題進行改善，以期能實際應用於運動場域。本系統經由腳踏車、踏步機和跑步機等三項實驗項目所得到的平均絕對誤差(Mean Absolute Error，MAE)/均方根誤差(Root-Mean-Square Error，RMSE)分別為2.05/2.98、3.04/4.20和2.65/4.05 BPM，而Success Rate-5/ Success Rate-10分別為0.88/0.93、0.83/0.89和0.85/0.94。經由實驗結果，證明本論文所提出的方法有助於提升心率量測準確度。

In recent years, increased participation in physical activity has led to the vigorous development of related industries. The change in heart rate during exercise is often regarded as an indicator to evaluate the training intensity and effect. Therefore, taking the advantages of the novel non-contact pulse rate measurement technology, such as high comfort, high convenience, and low-risk, to the fitness industry will not only help users to avoid discomfort or skin allergies caused by the wearable devices, but also reduce the risk of infectious diseases, and thereby improves the overall exercise effectiveness.
At present, there are still some problems need to be improved in the application of non-contact pulse rate measurement technology in fitness fields. The main issues are as follows: 1) Most of the research focuses on how to eliminate the noise caused by motion but ignores to analyze and identify the pixels effective to the pulse rate measurement in the region of interest (ROI). 2) The number of samples in the Fourier transform will cause a delay in the results of the measurement if the heart rate changes rapidly. 3) When conducting the periodic physical activities, for example, working out on a treadmill, the periodic swaying noise will be generated during the process. If the noise frequency is close to the heart rate, it may be difficult to identify the real heartbeat frequency on the spectrum, which will lead to erroneous results of heart rate measurement. 4) Since algorithms of non-contact pulse rate measurement are of greater complexity, current relevant researches tend to record the experimental process and then analyze the videos offline. However, the measurement information cannot be provided to the users in real-time in this way.
Therefore, this thesis proposes an instantly non-contact pulse rate measurement system based on image sensors to improve the above-mentioned problems. The results reveal that the mean absolute error (MAE) / root-mean-square error (RMSE) of pulse rate measurement are 2.05/2.98, 3.04/4.20, and 2.65/4.05 BPM for biking, stepper, and treadmill, respectively. In addition, the success rate-5 / success rate-10 are 0.88/0.93, 0.83/0.89, and 0.85/0.94, respectively. The experimental results show that the proposed methods can improve the accuracy of pulse rate measurement.

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