Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar

Ma, C; Shi, Q; Hua, B; Zhang, Y; Xu, Z; Chu, L; Braun, R; Shi, J

Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar

Ma, C Shi, Q Hua, B Zhang, Y Xu, Z Chu, L Braun, R

Shi, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2024, 8, (2), pp. 122-134
Issue Date:: 2024-06-01

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Field	Value	Language
dc.contributor.author	Ma, C
dc.contributor.author	Shi, Q
dc.contributor.author	Hua, B
dc.contributor.author	Zhang, Y
dc.contributor.author	Xu, Z
dc.contributor.author	Chu, L
dc.contributor.author	Braun, R https://orcid.org/0000-0002-4153-8769
dc.contributor.author	Shi, J
dc.date.accessioned	2024-08-21T05:44:53Z
dc.date.available	2024-08-21T05:44:53Z
dc.date.issued	2024-06-01
dc.identifier.citation	IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology, 2024, 8, (2), pp. 122-134
dc.identifier.issn	2469-7257
dc.identifier.issn	2469-7249
dc.identifier.uri	http://hdl.handle.net/10453/180511
dc.description.abstract	Software-defined radio (SDR) can be used to detect human respiratory and heartbeat signals with the merits of low costs, high flexibility, and fast implementation. This paper proposes a human respiratory heartbeat detection system based on SDR micro-Doppler radar. The system can adjust radar parameters in real-time according to the detection environment, breaking the hardware limitations of traditional radar. Data pre-processing is performed on the transmit and receive baseband signals to obtain a composite signal containing human respiratory and heartbeat signals. In addressing the difficulty of detecting heartbeat signals compared to respiratory signals, an adaptive heartbeat signal enhancement detection algorithm named the one-time differential weighted step-size normalized least mean square (ODWS-NLMS) is proposed. This algorithm enhances the step size through weighted improvements utilizing the first-order differential characteristics of composite signals. Experiments were conducted in three distinct real-world environments, and the results indicate that the proposed algorithm outperforms discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) in terms of average accuracy, root mean square error (RMSE), and signal-to-noise ratio (SNR).
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology
dc.relation.isbasedon	10.1109/JERM.2024.3378977
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4003 Biomedical engineering
dc.subject.classification	4006 Communications engineering
dc.title	Noncontact Heartbeat and Respiratory Signal Separation Using a Sub 6 GHz SDR Micro-Doppler Radar
dc.type	Journal Article
utslib.citation.volume	8
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CRIN - Realtime Information Networks
pubs.organisational-group	University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Global Big Data Technologies Research Centre (GBDTC)
utslib.copyright.status	closed_access	*
dc.date.updated	2024-08-21T05:44:46Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	8
utslib.citation.issue	2

Abstract:

Software-defined radio (SDR) can be used to detect human respiratory and heartbeat signals with the merits of low costs, high flexibility, and fast implementation. This paper proposes a human respiratory heartbeat detection system based on SDR micro-Doppler radar. The system can adjust radar parameters in real-time according to the detection environment, breaking the hardware limitations of traditional radar. Data pre-processing is performed on the transmit and receive baseband signals to obtain a composite signal containing human respiratory and heartbeat signals. In addressing the difficulty of detecting heartbeat signals compared to respiratory signals, an adaptive heartbeat signal enhancement detection algorithm named the one-time differential weighted step-size normalized least mean square (ODWS-NLMS) is proposed. This algorithm enhances the step size through weighted improvements utilizing the first-order differential characteristics of composite signals. Experiments were conducted in three distinct real-world environments, and the results indicate that the proposed algorithm outperforms discrete wavelet transform (DWT) and ensemble empirical mode decomposition (EEMD) in terms of average accuracy, root mean square error (RMSE), and signal-to-noise ratio (SNR).

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http://hdl.handle.net/10453/180511