Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns with SDR Sensing and Deep Multilayer Perceptron

Umer Saeed, Syed Yaseen Shah, Adnan Zahid , Ahmad Jawad, Muhammad Ali Imran , Qammer H. Abbasi, Syed Aziz Shah

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    19 Citations (Scopus)
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    Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive methods are essential especially during the COVID-19 pandemic as they minimise the burden on healthcare personnel. One notable symptom of COVID-19 infection is a rapid respiratory rate, which requires constant real-time monitoring of respiratory patterns. In this paper, Software Defined Radio (SDR) based Radio-Frequency sensing technique and supervised machine learning algorithm is employed to provide a platform for detecting and monitoring various respiratory: eupnea, biot, bradypnea, sighing, tachypnea, and kussmaul. The variations in Channel State Information produced by human respiratory were utilised to identify distinct respiratory patterns using fine-grained Orthogonal Frequency-Division Multiplexing signals. The proposed platform based on the SDR and the Deep Multilayer Perceptron classifier exhibits the ability to effectively detect and classify the afore-mentioned distinct respiratory with an accuracy of up to 99%. Moreover, the effectiveness of the proposed scheme in terms of diagnosis accuracy, precision, recall, F1-score, and confusion matrix is demonstrated by comparison with a state-of-the-art machine learning classifier: Random Forest.
    Original languageEnglish
    Pages (from-to)20833-20840
    Number of pages8
    JournalIEEE Sensors Journal
    Issue number18
    Early online date12 Jul 2021
    Publication statusPublished - 15 Sept 2021


    • Electrical and Electronic Engineering
    • Instrumentation


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