COVID-19 患者通常會發燒並呼吸困難。患有呼吸系統疾病的 COVID-19 患者在隔離期間需要醫療人員的密切關懷。非接觸式監測設備將是解決關懷 COVID-19 患者並降低病毒傳播風險的方案。本研究使用調頻連續波(FMCW)雷達和機器學習(ML)分別獲取呼吸資訊和分析呼吸信號。通過計算接收信號的到達角度(AoA)和利用FMCW雷達的多輸入多輸出(MIMO)系統可以同時檢測房間中的多個物件。
COVID-19 患者通常會發燒並呼吸困難。患有呼吸系統疾病的 COVID-19 患者在隔離期間需要醫療人員的密切監督。非接觸式監測設備將是一個合適的解決方案，在監測 COVID-19 患者同時會降低病毒傳播風險。本研究使用調頻連續波(FMCW)雷達和機器學習(ML)分別獲取呼吸資訊和分析呼吸信號。通過計算接收信號的到達角度(AoA)和利用FMCW雷達的多輸入多輸出(MIMO)系統可以同時檢測房間中的多個物件。運用快速傅立葉轉換(FFT)和一些信號處理以獲得呼吸波形，透過機器學習協助系統自動分析呼吸信號。藉由機器學習可自動分辨五種呼吸訊號。這篇論文中，可分辨五種呼吸模式: 正常呼吸、深呼吸、深呼吸和快速呼吸、屏住呼吸和快速呼吸。本研究還比較了幾種ML演算法的性能，例如多類別邏輯回歸(MLR)、決策樹(DT)、隨機森林 (RF)、支持向量機(SVM)、極限梯度提升(XGB)、輕型梯度提升機(LGBM)、分類式梯度提升(CB)分類器、多層感知器(MLP)和三個提出的堆疊式集成模型，即堆疊式集成分類器(SEC)、基於提升樹堆疊式分類器(BTSC) 和 類神經堆疊式集成模型(NSEM)以獲得最好的機器學習模型。結果顯示，NSEM演算法以97.1 %的準確率達到最佳性能。在即時實現中，系統可以同時檢測多個具有不同呼吸特徵的物體, 並將呼吸信號分為五個不同的類別。

A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to differentiate the five kinds of respiratory signals automatically. In this study, we address five different breathing patterns: normal breath, deep breath, deep and quick breath, holding breath and quick breath. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously detect several objects with different breathing characteristics and classify the respiratory signals into five different classes.

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