论文标题

异常呼吸模式分类器可能有助于以准确且不引人注目的方式对Covid-19感染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

论文作者

Wang, Yunlu, Hu, Menghan, Li, Qingli, Zhang, Xiao-Ping, Zhai, Guangtao, Yao, Nan

论文摘要

研究意义:本文的扩展版本已被IEEE Internet Internet Journal(doi:10.1109/jiot.2020.2991456)接受,请引用期刊版本。在流行病的预防和控制期间,我们的研究可能有助于基于呼吸特征感染Covid-19(新的冠状病毒)的患者的预后,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。在Covid-19中发生的一种重要症状是tachypnea。感染Covid-19的人的呼吸更快。我们的研究可以用来区分各种呼吸模式,并且我们的设备可以最初用于实际使用。可以在线下载此方法在一个主题和两个主题的情况下工作的演示视频。研究详细信息:以偏远和不引人注目的方式准确检测出意外的异常呼吸模式具有重要意义。在这项工作中,我们在深度摄像头和深度学习上进行了创新的资本,以实现这一目标。这项任务的挑战是双重的:现实世界数据的数量不足以培训获得深层模型;不同类型的呼吸模式的类内变化很大,外层差异很小。在本文中,考虑到实际呼吸信号的特征,首先提出了一种新型有效的呼吸模拟模型(RSM),以填补大量训练数据和稀缺现实世界数据之间的空白。拟议的深层模型和建模想法具有扩展到大规模应用,例如公共场所,睡眠场景和办公环境等大型应用程序的巨大潜力。

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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