Contactless Small-Scale Movement Monitoring System Using Software Defined Radio for Early Diagnosis of COVID-19

Mubashir Rehman, Raza Ali Shah, Muhammad Bilal Khan, Najah Abed Abu Ali, Abdullah Alhumaidi Alotaibi, Turke Althobaiti, Naeem Ramzan, Syed Aziz Shaha, Xiaodong Yang, Akram Alomainy, Muhammad Ali Imran, Muhammad Ali Imran, Qammer H. Abbasi

    Research output: Contribution to journalArticlepeer-review

    29 Citations (Scopus)
    126 Downloads (Pure)


    The exponential growth of the novel coronavirus disease (N-COVID-19) has affected millions of people already and it is obvious that this crisis is global. This situation has enforced scientific researchers to gather their efforts to contain the virus. In this pandemic situation, health monitoring and human movements are getting significant consideration in the field of healthcare and as a result, it has emerged as a key area of interest in recent times. This requires a contactless sensing platform for detection of COVID-19 symptoms along with containment of virus spread by limiting and monitoring human movements. In this paper, a platform is proposed for the detection of COVID-19 symptoms like irregular breathing and coughing in addition to monitoring human movements using Software Defined Radio (SDR) technology. This platform uses Channel Frequency Response (CFR) to record the minute changes in Orthogonal Frequency Division Multiplexing (OFDM) subcarriers due to any human motion over the wireless channel. In this initial research, the capabilities of the platform are analyzed by detecting hand movement, coughing, and breathing. This platform faithfully captures normal, slow, and fast breathing at a rate of 20, 10, and 28 breaths per minute respectively using different methods such as zero-cross detection, peak detection, and Fourier transformation. The results show that all three methods successfully record breathing rate. The proposed platform is portable, flexible, and has multifunctional capabilities. This platform can be exploited for other human body movements and health abnormalities by further classification using artificial intelligence.

    Original languageEnglish
    Article number9422749
    Pages (from-to)17180-17188
    Number of pages9
    JournalIEEE Sensors Journal
    Issue number15
    Early online date4 May 2021
    Publication statusPublished - 1 Aug 2021

    Bibliographical note

    © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


    This work was supported in part by the Zayed Health Center at United Arab Emirates University (UAEU) under Grant G00003476, in part by the Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/T021063/1 and Grant EP/R511705/1, and in part by the Taif University Research Supporting, Taif University, Taif, Saudi Arabia, under Project TURSP-2020/277.


    • CFR
    • COVID-19
    • OFDM
    • SDR
    • USRP
    • breathing rate measurement

    ASJC Scopus subject areas

    • Instrumentation
    • Electrical and Electronic Engineering


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