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研究生: 陳信杰
Xin-Jie Chen
論文名稱: 基於熱顯像儀的非接觸式多參數生理訊號量測系統在門診的應用
A Thermal Camera based Contactless Multi-Parameter Physiological Signal Measurement System for Outpatient Clinic
指導教授: 林淵翔
Yuan-Hsiang Lin
口試委員: 林昌鴻
Chang-Hong Lin
林敬舜
Ching-Shun Lin
周迺寬
Nai-Kuan Chou
學位類別: 碩士
Master
系所名稱: 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年: 2021
畢業學年度: 109
語文別: 中文
論文頁數: 96
中文關鍵詞: 非接觸式生理訊號量測熱顯像儀體表溫度心率呼吸率門診
外文關鍵詞: non-contact physiological signals measurement, thermal camera, surface temperature, pulse rate, respiration rate, outpatient clinic
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由於2019年新型冠狀病毒(COVID-19)的流行,世界衛生組織(WHO)建議人與人之間應保持至少1公尺的社交距離。然而,在醫院或診所內很難在保持社交距離情況下同時收集病患的生理參數。目前生理參數收集的傳統方法如:心電圖(ECG)、光體積變化描述法(PPG)與溫度計等接觸式的量測方法。近年來陸續有研究提出使用非接觸式的方式來量測生理訊號,目前主要使用都卜勒雷達、RGB攝影機或紅外線熱顯像儀。而基於熱顯像儀的非接觸式生理訊號量測技術為較新的研究領域,其不受環境光源、受測者膚色影響,且更有隱私性。使得它具有更廣的發展空間,也更符合門診、臨床、新生兒和長期照護的場景應用。
本論文建構一套適用於門診靜態情況下的多生理參數量測系統。以低解析度(80×60)熱顯像儀作為影像來源,並加入影像處理與訊號處理演算法即時推算出受測者當下的體表溫度、心率與呼吸率。實驗分為黑體爐溫度量測和生理信號量測。生理信號量測分成實驗室環境與醫院門診環境。在黑體爐表面溫度量測準確度實驗結果整體平均MAE/RMSE為0.1401 °C/0.1753 °C,實驗室環境的生理信號量測分為實驗一和實驗二,實驗一之未配戴口罩實驗中體表溫度、心率與呼吸率之平均MAE/RMSE分別為0.27 °C/0.32 °C、2.70 BPM/3.15 BPM與0.79 BPM/1.07 BPM;配戴口罩實驗中體表溫度、心率與呼吸率之平均MAE/RMSE分別為0.33 °C/0.37 °C、3.11 BPM/3.68 BPM與0.24 BPM/0.35 BPM。實驗二之未配戴口罩實驗中體表溫度、心率與呼吸率之平均MAE/RMSE分別為0.20 °C/0.26 °C、3.16 BPM/3.95 BPM與0.45 BPM/0.68 BPM;配戴口罩實驗中體表溫度、心率與呼吸率之平均MAE/RMSE分別為0.38 °C/0.42 °C、3.30 BPM/4.04 BPM與0.19 BPM/0.28 BPM。醫院門診環境量測平均體表溫度、心率與呼吸率之MAE/RMSE分別為0.36 °C/0.40 °C、2.71 BPM/3.27 BPM與0.41 BPM/0.58 BPM。


Due to the 2019 COVID-19 epidemic, the World Health Organization (WHO) recommends that people should maintain a social distance of at least one meter. However, it is difficult to collect the physiological parameters of patients while keeping social distancing in a hospital or clinic. The current methods of physiological parameter collection such as ECG, PPG, and thermometers might cause allergies, discomfort, and activity limitation in the long term. Researchers have proposed the use of non-contact methods to measure physiological signals in recent years, such as Doppler radar, RGB cameras, or thermal cameras. The non-contact measurement based on the thermal camera is not affected by the environmental light source and the skin color of the subject. It is more suitable for outpatient, clinical, neonatal, and long-term care with these advantages.
In this thesis, we built a multi-physiological parameter measurement system, which is suitable for static conditions in outpatient and clinics. A low-resolution (80×60) thermal camera is used as the image source, then the proposed image processing and signal processing algorithm are used to calculate the subject’s surface temperature, heart rate, and respiration rate. The experiment includes blackbody temperature measurement and physiological signal measurement. Physiological signal measurement is divided into laboratory environment and outpatient environment. The average MAE/RMSE of the blackbody temperature measurement was 0.1401 °C/0.1753 °C. The physiological signal measurement of the laboratory environment is divided into experiment 1 and experiment 2.
In experiment 1, the average MAE/RMSE of body surface temperature, heart rate, and respiration rate without a mask were 0.27 °C/0.32 °C, 2.70 BPM/3.15 BPM, and 0.79 BPM/1.07 BPM, respectively; and the average MAE/RMSE of the result with a mask were 0.33 °C/0.37 °C, 3.11 BPM/3.68 BPM and 0.24 BPM/0.35 BPM, respectively. In experiment 2, the average MAE/RMSE of the body surface temperature, heart rate and respiration rate without a mask were 0.20 °C/0.26 °C, 3.16 BPM/3.95 BPM and 0.45 BPM/0.68 BPM, respectively; and the average MAE/RMSE of the result with a mask were 0.38 °C/0.42 °C, 3.30 BPM/4.04 BPM and 0.19 BPM/0.28 BPM, respectively. The average MAE/RMSE of the body surface temperature, heart rate, and respiration rate measured in the outpatient environment were 0.36 °C/0.40 °C, 2.71 BPM/3.27 BPM, and 0.41 BPM/0.58 BPM, respectively.

摘要 I Abstract IV 致謝 VI 目錄 VII 圖目錄 IX 表目錄 XI 第一章、 緒論 1 1.1 動機與目的 1 1.2 文獻探討 3 1.2.1 基於熱顯像儀的連續體表溫度量測方法 3 1.2.2 基於熱顯像儀的非接觸式心率量測方法 4 1.2.3 基於熱顯像儀的非接觸式呼吸率量測方法 6 1.3 相關論文與本論文比較 7 1.4 論文架構 9 第二章、 研究背景 10 2.1 體溫的定義 10 2.2 BCG訊號的定義及原理 11 2.3 呼吸的定義與原理 12 2.4 物件偵測方法 13 第三章、 研究方法 15 3.1 系統介紹 15 3.2 熱顯像儀之溫度校正(Temperature Calibration) 16 3.3 人臉偵測模型訓練(Face Detection Model Training) 19 3.4 影像處理(Image Processing) 21 3.4.1 前處理 (Pre-processing) 21 3.4.2 人臉偵測(Face Detection) 22 3.4.3 感興趣區域選取(ROI Selection) 23 3.4.4 臉部邊緣顯化處理(Face Edge Visualization) 26 3.5 訊號處理(Signal Processing) 28 3.5.1 體表溫度量測(Surface Body Temperature Measurement) 28 3.5.2 心率量測(Pulse Rate Measurement) 29 3.5.3 呼吸率量測(Respiration Rate Measurement) 38 3.6 使用者介面(User Interface) 43 第四章、 實驗方法與結果討論 44 4.1 實驗設置與流程 44 4.1.1 黑體爐溫度量測 45 4.1.2 生理信號量測 47 4.2 驗證方式 49 4.2.1 體表溫度驗證 49 4.2.2 心率驗證 50 4.2.3 呼吸率驗證 51 4.3 實驗結果 52 4.3.1 黑體爐溫度量測結果 52 4.3.2 生理信號量測—實驗一 53 4.3.3 生理信號量測—實驗二 57 4.3.4 生理信號量測—實驗三 63 4.3.5 實驗結果分析 66 4.4 結果討論 70 4.4.1 與相關論文之結果比較 70 4.4.2 FEV與DWA演算法對於心率量測的影響 73 4.4.3 呼吸訊號來源對於呼吸率量測之影響 76 第五章、 結論與未來展望 78 參考文獻 79 《附錄一》 84

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