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dc.contributor.authorOguntala, George A.*
dc.contributor.authorAbd-Alhameed, Raed A.*
dc.contributor.authorNoras, James M.*
dc.contributor.authorHu, Yim Fun*
dc.contributor.authorNnabuike, Eya N.*
dc.contributor.authorAli, N.*
dc.contributor.authorElfergani, I.T.*
dc.contributor.authorRodriguez, J.*
dc.date.accessioned2019-05-28T15:04:02Z
dc.date.available2019-05-28T15:04:02Z
dc.date.issued2019-05-16
dc.identifier.citationOguntala GA, Abd-Alhameed RA, Noras JM et al (2019) SmartWall: Novel RFID-enabled Ambient Human Activity Recognition using Machine Learning for Unobtrusive Health Monitoring. IEEE Access. 7: 68022-68033.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17069
dc.descriptionYesen_US
dc.description.abstractHuman activity recognition from sensor readings have proved to be an effective approach in pervasive computing for smart healthcare. Recent approaches to ambient assisted living (AAL) within a home or community setting offers people the prospect of more individually-focused care and improved quality of living. However, most of the available AAL systems are often limited by computational cost. In this paper, a simple, novel non-wearable human activity classification framework using the multivariate Gaussian is proposed. The classification framework augments prior information from the passive RFID tags to obtain more detailed activity profiling. The proposed algorithm based on multivariate Gaussian via maximum likelihood estimation is used to learn the features of the human activity model. Twelve sequential and concurrent experimental evaluations are conducted in a mock apartment environment. The sampled activities are predicted using a new dataset of the same activity and high prediction accuracy is established. The proposed framework suits well for the single and multi-dwelling environment and offers pervasive sensing environment for both patients and carers.en_US
dc.description.sponsorshipTertiary Education Trust Fund of Federal Government of Nigeria and by the European Union’s Horizon 2020 research and innovation programme under Grant Agreement H2020-MSCA-ITN-2016 SECRET-722424en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1109/ACCESS.2019.2917125en_US
dc.rights(c) 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other worksen_US
dc.subjectAmbient assisted livingen_US
dc.subjectHuman activity recognitionen_US
dc.subjectMachine learningen_US
dc.subjectMultivariate Gaussianen_US
dc.subjectPervasive computingen_US
dc.titleSmartWall: Novel RFID-enabled Ambient Human Activity Recognition using Machine Learning for Unobtrusive Health Monitoringen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-05
dc.date.application2019-05
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
refterms.dateFOA2019-05-28T15:04:02Z


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