A pervasive respiratory monitoring sensor for COVID-19 pandemic
File(s)
Published version
Author(s)
Chen, Xiaoshuai
Jiang, Shuo
Li, Zeyu
Lo, Benny
Type
Journal Article
Abstract
Goal: The SARS-CoV-2 viral infection could cause severe acute respiratory syndrome, disturbing the regular breathing and leading to continuous coughing. Automatic respiration monitoring systems could provide the necessary metrics and warnings for timely intervention, especially for those with mild symptoms. Current respiration detection systems are expensive and too obtrusive for any large-scale deployment. Thus, a low-cost pervasive ambient sensor is proposed. Methods: We will posit a barometer on the working desk and develop a novel signal processing algorithm with a sparsity-based filter to remove the similar-frequency noise. Three modes (coughing, breathing and others) will be conducted to detect coughing and estimate different respiration rates. Results: The proposed system achieved 97.33% accuracy of cough detection and 98.98% specificity of respiration rate estimation. Conclusions: This system could be used as an effective screening tool for detecting subjects suffering from COVID-19 symptoms and enable large scale monitoring of patients diagnosed with or recovering.
Date Issued
2020-12-02
Date Acceptance
2020-11-30
Citation
IEEE Open Journal of Engineering in Medicine and Biology, 2020, 2, pp.11-16
ISSN
2644-1276
Publisher
Institute of Electrical and Electronics Engineers
Start Page
11
End Page
16
Journal / Book Title
IEEE Open Journal of Engineering in Medicine and Biology
Volume
2
Copyright Statement
© 2020 The Author(s). This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/
License URL
Identifier
https://ieeexplore.ieee.org/document/9277874
Publication Status
Published
Date Publish Online
2020-12-02