AI-Driven Lightweight Real-Time SDR Sensing System for Anomalous Respiration Identification using Ensemble Learning

Umer Saeed, Qammer H. Abbasi, Syed Aziz Shah

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    73 Downloads (Pure)

    Abstract

    In less than three years, more than six million fatalities have been reported worldwide due to the coronavirus pandemic. COVID-19 has been contained within a broad range due to restrictions and effective vaccinations. However, there is a greater risk of pandemics in the future, which can cause similar circumstances as the coronavirus. One of the most serious symptoms of coronavirus is rapid respiration decline that can lead to mortality in a short period. This situation, along with other respiratory conditions such as asthma and pneumonia, can be fatal. Such a condition requires a reliable, intelligent, and secure system that is not only contactless but also lightweight to be executed in real-time. Wireless sensing technology is the ultimate solution for modern healthcare systems as it eliminates close interactions with infected individuals. In this paper, a lightweight real-time solution for anomalous respiration identification is provided using the radio-frequency sensing device USRP and the ensemble learning approach extra-trees. A wireless software-defined radio platform is used to acquire human respiration data based on the change in the channel state information. To improve the performance of the trained models, the respiration data is utilised to produce large simulated data sets using the curve fitting technique. The final data set consists of eight distinct types of respiration: eupnea, bradypnea, tachypnea, sighing, biot, Cheyne-stokes, Kussmaul, and central sleep apnea. The ensemble learning approach: extra-trees are trained, validated, and tested. The results showed that the proposed platform is lightweight and highly accurate in identifying several respirations in a static setting.
    Original languageEnglish
    Pages (from-to)381-392
    Number of pages12
    JournalCCF Transactions on Pervasive Computing and Interaction
    Volume4
    Issue number4
    Early online date29 Sept 2022
    DOIs
    Publication statusPublished - Dec 2022

    Bibliographical note

    This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

    Funder

    The studentship is supported by internal funding of the Research Centre for Intelligent Healthcare, Coventry University, UK.

    Keywords

    • RF sensing
    • Ensemble learning
    • Software-defined radio
    • Anomalous respiration
    • Smart healthcare

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