心率、呼吸率和體溫是人體重要的生理參數，監控上述幾項數值能幫助醫生診斷病患的生理狀態。近年來陸續有研究提出使用都卜勒雷達或熱顯像儀來進行非接觸式生理訊號量測，這兩個感測器不受環境光源和使用者膚色的影響，並且更有隱私性。然而，使用雷達量測心率可能會受到環境、隨機身體移動和呼吸訊號的諧波等干擾因素的影響，造成量測不準。此外，熱顯像儀的體溫量測的準確度會受到距離的影響。
因此，本論文提出一套基於都卜勒雷達與熱顯像儀的非接觸式多參數生理訊號量測系統，將都卜勒雷達與熱顯像儀整合，提升心率、呼吸率和體溫量測的準確度。心率量測部分，使用都卜勒雷達的多通道訊號，並透過深度學習模型估測心率，藉由熱顯像儀的距離估計限制雷達的搜尋範圍，則可以減少環境雜訊的影響。呼吸率量測部分，使用都卜勒雷達與熱顯像儀量測人體呼吸時產生的移動訊號，透過計算多通道訊號的訊雜比以得到較準確的呼吸率。體溫量測部分，使用熱顯像儀量測皮膚表面的溫度，並透過都卜勒雷達量測精準的人體與感測器的距離，以選擇該距離最合適的熱顯像儀溫度校正公式計算出較準確的體溫。
本論文的驗證分成離線實驗(實驗一)和即時實驗(實驗二)。實驗一分成訓練集與測試集，分別有9和4名受測者。實驗一的心率、呼吸率和體溫的量測結果，平均絕對誤差(MAE)/均方根誤差(RMSE)分別為4.67/5.93BPM、1.13/1.67 BPM和0.39/0.44°C。實驗二有5名受測者，心率、呼吸率和體溫的量測結果，MAE/RMSE分別為3.62/4.75 BPM、1.16/1.48 BPM和0.48/0.61°C。實驗結果顯示本論文藉由兩個感測器的整合可以提升量測準確度。

Pulse rate, respiratory rate, and body temperature are key physiological parameters, and monitoring them aids in medical diagnosis. Recent research has explored the use of Doppler radar and thermal camera for non-contact physiological measurements. These sensors offer privacy and are unaffected by environmental lighting or skin color. However, radar-based pulse rate measurement can be disrupted by environmental noise, body movements, and respiratory signal harmonics. Additionally, temperature accuracy of thermal camera is influenced by distance.
This thesis proposes a non-contact multi-parameter physiological signal measurement system based on Doppler radar and thermal camera to improve the accuracy of pulse rate, respiratory rate, and body temperature measurements. Pulse rate is estimated using multi-channel radar signals and a deep learning model. Limiting the radar's search range based on the thermal camera's distance estimation can reduce environmental noise interference. Respiratory rate is measured by detecting breathing movements with Doppler radar and thermal imaging, and is made more accurate by calculating the signal-to-noise ratio of multi-channel signals. Body temperature is measured by the thermal camera, with the radar providing distance data to apply the correct temperature correction formula for more accurate results.
The thesis includes offline and real-time experiments. In the offline experiment, with 9 subjects in the training set and 4 in the test set, the mean absolute error (MAE)/root mean square error (RMSE) for pulse rate, respiratory rate, and body temperature were 4.67/5.93 BPM, 1.13/1.67 BPM, and 0.39/0.44°C, respectively. The real-time experiment, with 5 subjects, MAE/RMSE values are 3.62/4.75 BPM, 1.16/1.48 BPM, and 0.48/0.61°C. These results show that combining the two sensors can improve the accuracy.

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