异常呼吸模式分类器可能有助于以准确和不引人注目的方式对感染COVID-19的人进行大规模筛查
Abnormal respiratory patterns classifier may contribute to large-scale screening of people infected with COVID-19 in an accurate and unobtrusive manner
摘要：
研究意义：本文的扩展版本已被IEEE Internet of Things期刊（DOI：10.1109/JIOT.2020.2991456）接受，请引用期刊版本。在疫情防控期间，我们的研究有助于基于呼吸特征的COVID-19（新型冠状病毒）感染患者的预后、诊断和筛查。根据最新的临床研究，COVID-19的呼吸模式与流感和普通感冒的呼吸模式不同。COVID-19中发生的一个重要症状是呼吸急促。感染COVID-19的人呼吸更快。本研究可用于各种呼吸模式的识别，并可初步投入实用。该方法在单受试者和双受试者情况下的演示视频可以在线下载。研究详情：以远程和不显眼的方式准确检测人的意外异常呼吸模式具有重要意义。在这项工作中，我们创新地利用深度相机和深度学习来实现这一目标。这项任务的挑战是双重的：真实世界的数据量不足以训练获得深度模型;不同类型的呼吸模式的类内变化很大，类外变化很小。本文针对呼吸信号的特点，提出了一种新颖高效的呼吸仿真模型（Respiratory Simulation Model，简称RSM），以弥补训练数据量大和真实数据量少的不足。所提出的深度模型和建模思想具有很大的潜力，可以扩展到大规模的应用，如公共场所，睡眠场景，办公环境。
英文摘要：
Research significance: The extended version of this paper has been accepted by IEEE Internet of Things journal (DOI: 10.1109/JIOT.2020.2991456), please cite the journal version. During the epidemic prevention and control period, our study can be helpful in prognosis, diagnosis and screening for the patients infected with COVID-19 (the novel coronavirus) based on breathing characteristics. According to the latest clinical research, the respiratory pattern of COVID-19 is different from the respiratory patterns of flu and the common cold. One significant symptom that occurs in the COVID-19 is Tachypnea. People infected with COVID-19 have more rapid respiration. Our study can be utilized to distinguish various respiratory patterns and our device can be preliminarily put to practical use. Demo videos of this method working in situations of one subject and two subjects can be downloaded online. Research details: Accurate detection of the unexpected abnormal respiratory pattern of people in a remote and unobtrusive manner has great significance. In this work, we innovatively capitalize on depth camera and deep learning to achieve this goal. The challenges in this task are twofold: the amount of real-world data is not enough for training to get the deep model; and the intra-class variation of different types of respiratory patterns is large and the outer-class variation is small. In this paper, considering the characteristics of actual respiratory signals, a novel and efficient Respiratory Simulation Model (RSM) is first proposed to fill the gap between the large amount of training data and scarce real-world data. The proposed deep model and the modeling ideas have the great potential to be extended to large scale applications such as public places, sleep scenario, and office environment.
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