  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.
