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http://dx.doi.org/10.5762/KAIS.2021.22.5.296

Classification of Respiratory States based on Visual Information using Deep Learning  

Song, Joohyun (Department of Computer Engineering, Keimyung University)
Lee, Deokwoo (Department of Computer Engineering, Keimyung University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.5, 2021 , pp. 296-302 More about this Journal
Abstract
This paper proposes an approach to the classification of respiratory states of humans based on visual information. An ultra-wide-band radar sensor acquired respiration signals, and the respiratory states were classified based on two-dimensional (2D) images instead of one-dimensional (1D) vectors. The 1D vector-based classification of respiratory states has limitations in cases of various types of normal respiration. The deep neural network model was employed for the classification, and the model learned the 2D images of respiration signals. Conventional classification methods use the value of the quantified respiration values or a variation of them based on regression or deep learning techniques. This paper used 2D images of the respiration signals, and the accuracy of the classification showed a 10% improvement compared to the method based on a 1D vector representation of the respiration signals. In the classification experiment, the respiration states were categorized into three classes, normal-1, normal-2, and abnormal respiration.
Keywords
Respiration; Respiratory states; UWB radar; Visual information; Deep neural network;
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