RF Sensing Based Breathing Patterns Detection Leveraging USRP Devices

Mubashir Rehman, Raza Ali Shah, Muhammad Bilal Khan, Najah Abed AbuAli, Syed Aziz Shah, Xiao-Dong Yang, Akram Alomainy , Muhmmad Ali Imran, Qammer H. Abbasi

    Research output: Contribution to journalArticlepeer-review

    20 Citations (Scopus)
    74 Downloads (Pure)

    Abstract

    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 COVID19. 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.
    Original languageEnglish
    Article number3855
    Number of pages14
    JournalSensors
    Volume21
    Issue number11
    DOIs
    Publication statusPublished - 2 Jun 2021

    Bibliographical note

    This article is an open access article distributed under the terms and
    conditions of the Creative Commons Attribution (CC BY) license (https://
    creativecommons.org/licenses/by/ 4.0/).

    Keywords

    • CSI
    • OFDM
    • SDR
    • USRP
    • Breathing pattern
    • COVID-19

    ASJC Scopus subject areas

    • Analytical Chemistry
    • Information Systems
    • Instrumentation
    • Atomic and Molecular Physics, and Optics
    • Electrical and Electronic Engineering
    • Biochemistry

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