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Towards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusion

dc.contributor.authorAl-Zyoud I
dc.contributor.authorLaamarti F
dc.contributor.authorMa X
dc.contributor.authorTobón D
dc.contributor.authorEl Saddik A.
dc.date.accessioned2023-10-24T19:26:27Z
dc.date.available2023-10-24T19:26:27Z
dc.date.created2022
dc.identifier.issn14248220
dc.identifier.urihttp://hdl.handle.net/11407/8148
dc.description.abstractHuman bio-signal fusion is considered a critical technological solution that needs to be advanced to enable modern and secure digital health and well-being applications in the metaverse. To support such efforts, we propose a new data-driven digital twin (DT) system to fuse three human physiological bio-signals: heart rate (HR), breathing rate (BR), and blood oxygen saturation level (SpO2). To accomplish this goal, we design a computer vision technology based on the non-invasive photoplethysmography (PPG) technique to extract raw time-series bio-signal data from facial video frames. Then, we implement machine learning (ML) technology to model and measure the bio-signals. We accurately demonstrate the digital twin capability in the modelling and measuring of three human bio-signals, HR, BR, and SpO2, and achieve strong performance compared to the ground-truth values. This research sets the foundation and the path forward for realizing a holistic human health and well-being DT model for real-world medical applications. © 2022 by the authors.eng
dc.language.isoeng
dc.publisherMDPI
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85144515996&doi=10.3390%2fs22249747&partnerID=40&md5=a32f31f7fef23eb8eeced6de1bda44f2
dc.sourceSensors
dc.sourceSensorseng
dc.subjectBio-signal fusioneng
dc.subjectComputer visioneng
dc.subjectDigital healtheng
dc.subjectDigital twineng
dc.subjectMachine learningeng
dc.subjectMetaverseeng
dc.titleTowards a Machine Learning-Based Digital Twin for Non-Invasive Human Bio-Signal Fusioneng
dc.typeArticle
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaArtículo
dc.identifier.doi10.3390/s22249747
dc.relation.citationvolume22
dc.relation.citationissue24
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationAl-Zyoud, I., Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
dc.affiliationLaamarti, F., Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada, Mohamed bin Zayed University of AI, Abu Dhabi, United Arab Emirates
dc.affiliationMa, X., Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
dc.affiliationTobón, D., Faculty of Engineering, University of Medellín, Medellín, 050010, Colombia
dc.affiliationEl Saddik, A., Multimedia Communications Research Laboratory (MCRLab), School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada, Mohamed bin Zayed University of AI, Abu Dhabi, United Arab Emirates
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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