Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/115686
DC FieldValueLanguage
dc.contributorDepartment of Electrical and Electronic Engineeringen_US
dc.creatorGe, Yen_US
dc.creatorWang, Hen_US
dc.creatorHo, IWHen_US
dc.date.accessioned2025-10-20T02:13:39Z-
dc.date.available2025-10-20T02:13:39Z-
dc.identifier.issn2168-2194en_US
dc.identifier.urihttp://hdl.handle.net/10397/115686-
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.subjectBiomedical signal processingen_US
dc.subjectChannel state informationen_US
dc.subjectRespiratory rate detectionen_US
dc.subjectWireless sensingen_US
dc.titleWiRe-breath : a sustainable WiFi-based real-time respiratory monitoring solutionen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.doi10.1109/JBHI.2025.3619664en_US
dcterms.abstractRespiratory monitoring, including respiratory rate monitoring and apnea detection, plays an essential role in daily healthcare, especially for older adults. Existing WiFi-based respiratory detection models lack a systematic device deployment strategy and face limitations in real-time monitoring in complex environments. This paper presents WiRe-Breath, a non-intrusive and real-time respiratory monitoring system based on off-the-shelf WiFi devices. Unlike previous approaches, only periodic beacons from routers are utilized for respiratory analysis in the proposed system, significantly increasing sustainability and reliability for long-term monitoring. To ensure high accuracy, we propose a respiratory sensing model and determine the optimal device deployment strategy. Specifically, we enhance respiratory rate features through effective motion enhancement and respiratory rate extraction approaches. Additionally, an apnea detection algorithm is designed for disease surveillance. A web application is also implemented for real-time respiratory status monitoring. Our experimental results indicate that the proposed system achieves an average accuracy of 98.86% for respiratory rate detection in quiet environments and 97.72% in environments with interference. WiRe-Breath also demonstrates effective apnea detection, fulfills real-time monitoring requirements, and delivers state-of-the-art performance.en_US
dcterms.accessRightsembargoed accessen_US
dcterms.bibliographicCitationIEEE journal of biomedical and health informatics, Date of Publication: 09 October 2025, Early Access, https://doi.org/10.1109/JBHI.2025.3619664en_US
dcterms.isPartOfIEEE journal of biomedical and health informaticsen_US
dcterms.issued2025-
dc.identifier.eissn2168-2208en_US
dc.description.validate202510 bcchen_US
dc.description.oaNot applicableen_US
dc.identifier.FolderNumbera4128-
dc.identifier.SubFormID52116-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextSmart Traffic Funden_US
dc.description.pubStatusEarly releaseen_US
dc.date.embargo0000-00-00 (to be updated)en_US
dc.description.oaCategoryGreen (AAM)en_US
Appears in Collections:Journal/Magazine Article
Open Access Information
Status embargoed access
Embargo End Date 0000-00-00 (to be updated)
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.