Publication:
Enhancing Accuracy in WBANs Through Optimal Sensor Positioning and Signal Processing: A Systematic Methodology

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dc.citedby0
dc.contributor.authorAl Barazanchi I.I.en_US
dc.contributor.authorAbdulrahman M.M.en_US
dc.contributor.authorThabit R.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorDalavi A.M.en_US
dc.contributor.authorSekhar R.en_US
dc.contributor.authorid57659035200en_US
dc.contributor.authorid57223305107en_US
dc.contributor.authorid58891173100en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid55986078200en_US
dc.contributor.authorid55792622100en_US
dc.date.accessioned2025-03-03T07:45:32Z
dc.date.available2025-03-03T07:45:32Z
dc.date.issued2024
dc.description.abstractWireless Body Area Networks (WBANs) have transformed fitness monitoring by providing continuous real-time data collection from small sensors placed on or inside the human body. Even though WBANs have important benefits, their accuracy and dependability are jeopardized by issues with sensor placement and signal processing. Incorrect sensor positioning can lead to inaccurate information, while noisy surroundings make signal processing more challenging. This focuses on using a single methodical approach to improve sensor placement and enhance signal processing in WBANs. We suggest a comprehensive solution that tackles placement sensitivity and noise reduction by combining anatomical modeling, real-time feedback mechanisms, and innovative machine learning algorithms. Experimental results show a 25% increase in signal quality and a 35% improvement in data precision when compared to conventional techniques. This method not only enhances the dependability of health monitoring systems but also validates their ability to be scaled and their effectiveness in various medical uses. Our results highlight the potential of WBANs to transform health care services, providing more accurate diagnostics and customized treatments. ? 2024 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/EECSI63442.2024.10776392
dc.identifier.epage774
dc.identifier.scopus2-s2.0-85214713651
dc.identifier.spage768
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85214713651&doi=10.1109%2fEECSI63442.2024.10776392&partnerID=40&md5=449bd1da60a368637608b07cb24052fb
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/36890
dc.pagecount6
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleInternational Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
dc.subjectBody sensor networks
dc.subjectContinuous time systems
dc.subjectDiagnosis
dc.subject- wireless body area network
dc.subjectMachine-learning
dc.subjectModeling anatomically
dc.subjectPlacement of sensors
dc.subjectReal-time feedback
dc.subjectSensor placement
dc.subjectSensor positioning
dc.subjectSensor signals
dc.subjectSignal-processing
dc.subjectWireless body area network
dc.titleEnhancing Accuracy in WBANs Through Optimal Sensor Positioning and Signal Processing: A Systematic Methodologyen_US
dc.typeConference paperen_US
dspace.entity.typePublication
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