Random Body Movement Interference Mitigation in Radar Breath Detection Based on L1 Norm

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

Random Body Movement Interference Mitigation in Radar Breath Detection Based on L1 Norm

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

Shi, J

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Sensors Letters, 2023, 7, (12), pp. 1-4
Issue Date:: 2023-12-01

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Field	Value	Language
dc.contributor.author	Ma, C
dc.contributor.author	Xu, Z
dc.contributor.author	Hua, B
dc.contributor.author	Zhang, Y
dc.contributor.author	Shi, Q
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	2024-04-17T09:36:47Z
dc.date.available	2024-04-17T09:36:47Z
dc.date.issued	2023-12-01
dc.identifier.citation	IEEE Sensors Letters, 2023, 7, (12), pp. 1-4
dc.identifier.issn	2475-1472
dc.identifier.issn	2475-1472
dc.identifier.uri	http://hdl.handle.net/10453/178020
dc.description.abstract	Radar-based noncontact detection of vital signs holds significant advanta-ges over conventional measurement devices. However, random body motion (RBM) leads to a decrease in the accuracy of radar-based human respiratory frequency estimation. In order to tackle this challenge, in this letter, we propose an RBM interference mitigation framework based on the L1 norm, which can be delineated into three specific steps. In the first step, we normalize the amplitude of the preprocessed signal to detect the signal region affected by RBM interference. In the second step, we introduce a labeling technique to eliminate the regions affected by RBM interference. In the third step, we reconstruct the missing respiratory signal data using the L1 norm-based least squares method. The experiments were conducted in a real-world environment, and the results demonstrate that the proposed RBM interference mitigation method based on the L1 norm effectively reduces the impact of RBM on respiratory rate accuracy. In comparison to existing algorithms, it exhibits superior root-mean-square error and real-time monitoring performance.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Sensors Letters
dc.relation.isbasedon	10.1109/LSENS.2023.3332679
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	4009 Electronics, sensors and digital hardware
dc.title	Random Body Movement Interference Mitigation in Radar Breath Detection Based on L1 Norm
dc.type	Journal Article
utslib.citation.volume	7
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
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-17T09:36:46Z
pubs.issue	12
pubs.publication-status	Published
pubs.volume	7
utslib.citation.issue	12

Abstract:

Radar-based noncontact detection of vital signs holds significant advanta-ges over conventional measurement devices. However, random body motion (RBM) leads to a decrease in the accuracy of radar-based human respiratory frequency estimation. In order to tackle this challenge, in this letter, we propose an RBM interference mitigation framework based on the L1 norm, which can be delineated into three specific steps. In the first step, we normalize the amplitude of the preprocessed signal to detect the signal region affected by RBM interference. In the second step, we introduce a labeling technique to eliminate the regions affected by RBM interference. In the third step, we reconstruct the missing respiratory signal data using the L1 norm-based least squares method. The experiments were conducted in a real-world environment, and the results demonstrate that the proposed RBM interference mitigation method based on the L1 norm effectively reduces the impact of RBM on respiratory rate accuracy. In comparison to existing algorithms, it exhibits superior root-mean-square error and real-time monitoring performance.

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http://hdl.handle.net/10453/178020