Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei 10607, Taiwan.
Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea.
Sensors (Basel). 2021 May 3;21(9):3172. doi: 10.3390/s21093172.
During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc.
在 2019 冠状病毒病（COVID-19）大流行期间，医务人员需要非接触式设备来降低病毒传播的风险。COVID-19 患者通常会发烧并呼吸困难。对有呼吸问题的患者进行无人监督的护理将是死亡率上升的主要原因。周期性线性递增频率啁啾，称为调频连续波（FMCW），是一种雷达技术，具有低功耗操作和高分辨率检测能力，可以检测到任何微小的运动。在这项研究中，我们使用 FMCW 开发了一种非接触式医疗设备，可以实时监测和分类呼吸模式。呼吸障碍患者的呼吸特征异常，无法用呼吸频率来表示。因此，我们创建了一个极端梯度提升（XGBoost）分类模型，并采用梅尔频率倒谱系数（MFCC）特征提取来对呼吸模式行为进行分类。XGBoost 是一种集成机器学习技术，具有快速执行时间和良好的可扩展性，适用于预测。在这项研究中，MFCC 特征提取有助于机器学习提取呼吸信号的特征。基于这些结果，该系统获得了可以接受的准确性。因此，我们提出的系统有可能用于检测和监测 COVID-19、哮喘等患者呼吸问题的存在。
