Software defined radio frequency sensing framework for Internet of Medical Things

AbuAli, N, Khan, MB, Ullah, F, Hayajneh, M, Ullah, H and Mumtaz, S ORCID: https://orcid.org/0000-0001-6364-6149, 2024. Software defined radio frequency sensing framework for Internet of Medical Things. Information Fusion, 103: 102106. ISSN 1566-2535

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Abstract

The escalating demand for biomedical systems that can precisely diagnose and manage critical diseases underscores the need for innovative solutions. A non-invasive and intelligent Internet of Medical Things (IoMT) system emerges as a promising technology, potentially enabling physicians to assess patients with reduced health risks. The respiratory rate is a pivotal vital sign among the primary clinical assessments. The allure of Radio Frequency(RF) sensing lies in its ability to monitor respiratory patterns without direct contact. However, the practical implementation of such systems often necessitates supplementary hardware to manage the extensive data and radio functionalities, leading to concerns related to cost and feasibility. Software-Defined Radio (SDR) technology presents itself as a viable solution to these challenges. This research introduces a comprehensive framework for the IoMT system, aiming to diagnose respiratory abnormalities early through RF sensing and SDR technology. We employ a deep learning framework and compare its performance with traditional machine learning models to ensure reliable and precise classification of respiratory abnormalities. The achieved results underscore the superiority of deep learning frameworks over conventional machine learning models in classifying respiratory anomalies. Specifically, the deep learning framework exhibits exceptional performance in discerning the temporal dependencies and patterns inherent in respiratory abnormalities, achieving an average accuracy exceeding 98% for each respiratory abnormality classification.

Item Type:	Journal article
Publication Title:	Information Fusion
Creators:	AbuAli, N., Khan, M.B., Ullah, F., Hayajneh, M., Ullah, H. and Mumtaz, S.
Publisher:	Elsevier BV
Date:	March 2024
Volume:	103
ISSN:	1566-2535
Identifiers:	Number Type 10.1016/j.inffus.2023.102106 DOI 1836487 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Laura Ward
Date Added:	27 Nov 2023 14:25
Last Modified:	27 Nov 2023 14:25
URI:	https://irep.ntu.ac.uk/id/eprint/50447

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