RF sensing based breathing patterns detection leveraging USRP devices

Rehman, M., Shah, R. A., Khan, M. B., AbuAli, N. A., Shah, S. A. , Yang, X., Alomainy, A., Imran, M. A. and Abbasi, Q. H. (2021) RF sensing based breathing patterns detection leveraging USRP devices. Sensors, 21(11), 3855. (doi: 10.3390/s21113855) (PMCID:PMC8199736)

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Non-contact detection of the breathing patterns in a remote and unobtrusive manner has significant value to healthcare applications and disease diagnosis, such as in COVID-19 infection prediction. During the epidemic prevention and control period of COVID-19, non-contact approaches have great significance because they minimize the physical burden on the patient and have the least requirement of active cooperation of the infected individual. During the pandemic, these non-contact approaches also reduce environmental constraints and remove the need for extra preparations. According to the latest medical research, the breathing pattern of a person infected with COVID-19 is unlike the breathing associated with flu and the common cold. One noteworthy symptom that occurs in COVID-19 is an abnormal breathing rate; individuals infected with COVID-19 have more rapid breathing. This requires continuous real-time detection of breathing patterns, which can be helpful in the prediction, diagnosis, and screening for people infected with COVID-19. In this research work, software-defined radio (SDR)-based radio frequency (RF) sensing techniques and machine learning (ML) algorithms are exploited to develop a platform for the detection and classification of different abnormal breathing patterns. ML algorithms are used for classification purposes, and their performance is evaluated on the basis of accuracy, prediction speed, and training time. The results show that this platform can detect and classify breathing patterns with a maximum accuracy of 99.4% through a complex tree algorithm. This research has a significant clinical impact because this platform can also be deployed for practical use in pandemic and non-pandemic situations.

Item Type:Articles
Additional Information:This research was funded by Zayed Health Center at UAE University, grant number G00003476. This work was also funded by EPSRC, grant numbers EP/T021020/1 and EP/T021063/1.
Keywords:CSI, OFDM, SDR, USRP, breathing pattern, COVID-19.
Glasgow Author(s) Enlighten ID:Abbasi, Dr Qammer and Imran, Professor Muhammad and Shah, Mr Syed
Creator Roles:
Shah, S. A.Investigation, Validation, Visualization
Imran, M. A.Funding acquisition, Project administration, Supervision
Abbasi, Q. H.Funding acquisition, Project administration, Supervision, Validation, Visualization, Writing – review and editing
Authors: Rehman, M., Shah, R. A., Khan, M. B., AbuAli, N. A., Shah, S. A., Yang, X., Alomainy, A., Imran, M. A., and Abbasi, Q. H.
College/School:College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Sensors
ISSN (Online):1424-8220
Published Online:02 June 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Sensors 21(11): 3855
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
307829Quantum-Inspired Imaging for Remote Monitoring of Health & Disease in Community HealthcareJonathan CooperEngineering and Physical Sciences Research Council (EPSRC)EP/T021020/1ENG - Biomedical Engineering