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Research significance: The extended version of this paper has been acceptedby IEEE Internet of Things journal (DOI: 10.1109/JIOT.2020.2991456), pleasecite the journal version. During the epidemic prevention and control period,our study can be helpful in prognosis, diagnosis and screening for the patientsinfected with COVID-19 (the novel coronavirus) based on breathingcharacteristics. According to the latest clinical research, the respiratorypattern of COVID-19 is different from the respiratory patterns of flu and thecommon cold. One significant symptom that occurs in the COVID-19 is Tachypnea.People infected with COVID-19 have more rapid respiration. Our study can beutilized to distinguish various respiratory patterns and our device can bepreliminarily put to practical use. Demo videos of this method working insituations of one subject and two subjects can be downloaded online. Researchdetails: Accurate detection of the unexpected abnormal respiratory pattern ofpeople in a remote and unobtrusive manner has great significance. In this work,we innovatively capitalize on depth camera and deep learning to achieve thisgoal. The challenges in this task are twofold: the amount of real-world data isnot enough for training to get the deep model; and the intra-class variation ofdifferent types of respiratory patterns is large and the outer-class variationis small. In this paper, considering the characteristics of actual respiratorysignals, a novel and efficient Respiratory Simulation Model (RSM) is firstproposed to fill the gap between the large amount of training data and scarcereal-world data. The proposed deep model and the modeling ideas have the greatpotential to be extended to large scale applications such as public places,sleep scenario, and office environment.

Abnormal respiratory patterns classifier may contribute to large-scale  screening of people infected with COVID-19 in an accurate and unobtrusive  manner