Back to articles
Articles
Volume: 33 | Article ID: art00004
Image
Remote estimation of respiration rate by optical flow using convolutional neural networks
  DOI :  10.2352/ISSN.2470-1173.2021.8.IMAWM-267  Published OnlineJanuary 2021
Abstract

In this paper, we propose a novel system for remotely estimating the respiration rate of people. Periodic inhalation and exhalation during respiration cycles induce subtle upper body movements, which are reflected by the local image deformation over time when recorded by a digital camera. This local image deformation can be recovered by estimating the optical flow between consecutive frames. We propose the usage of convolutional neural networks designed for general image registration to estimate the induced optical flow, the periodicity of which is then leveraged to obtain the respiration rate by frequency analysis. The proposed system is robust to lighting condition, camera type (RGB, infrared), clothing, and posture (sitting in chair/lying in bed); and it could be used by individuals with a webcam, or by healthcare centers to monitor the patients at night.

Subject Areas :
Views 64
Downloads 20
 articleview.views 64
 articleview.downloads 20
  Cite this article 

Tianqi Guo, Qian Lin, Jan Allebach, "Remote estimation of respiration rate by optical flow using convolutional neural networksin Proc. IS&T Int’l. Symp. on Electronic Imaging: Imaging and Multimedia Analytics in a Web and Mobile World,  2021,  pp 267-1 - 267-11,  https://doi.org/10.2352/ISSN.2470-1173.2021.8.IMAWM-267

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA