Lifelogging data validation model for Internet of Things enabled healthcare system

5.00
Hdl Handle:
http://hdl.handle.net/10547/621949
Title:
Lifelogging data validation model for Internet of Things enabled healthcare system
Authors:
Yang, Po; Stankevicius, Dainius; Marozas, Vaidotas; Deng, Zhikun; Liu, Enjie; Lukoševicǐus, Arunas; Dong, Feng; Xu, Lida; Min, Geyong
Abstract:
Internet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.
Affiliation:
Liverpool John Moores University; Kaunas University of Technology, Lithuania; Old Dominion University; University of Bedfordshire; Exeter University
Citation:
Yang P, Stankevicius D, Marozas V, Deng Z, Liu E, Lukoševicǐus A, Dong F, Xu L,Min G (2016) 'Lifelogging data validation model for Internet of Things enabled healthcare system', IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP (69).
Publisher:
IEEE
Journal:
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Issue Date:
19-Jul-2016
URI:
http://hdl.handle.net/10547/621949
DOI:
10.1109/TSMC.2016.2586075
Additional Links:
http://ieeexplore.ieee.org/document/7516690/
Type:
Article
Language:
en
Sponsors:
CARRE, iManageCancer
Appears in Collections:
Computing

Full metadata record

DC FieldValue Language
dc.contributor.authorYang, Poen
dc.contributor.authorStankevicius, Dainiusen
dc.contributor.authorMarozas, Vaidotasen
dc.contributor.authorDeng, Zhikunen
dc.contributor.authorLiu, Enjieen
dc.contributor.authorLukoševicǐus, Arunasen
dc.contributor.authorDong, Fengen
dc.contributor.authorXu, Lidaen
dc.contributor.authorMin, Geyongen
dc.date.accessioned2017-01-19T11:40:09Z-
dc.date.available2017-01-19T11:40:09Z-
dc.date.issued2016-07-19-
dc.identifier.citationYang P, Stankevicius D, Marozas V, Deng Z, Liu E, Lukoševicǐus A, Dong F, Xu L,Min G (2016) 'Lifelogging data validation model for Internet of Things enabled healthcare system', IEEE Transactions on Systems, Man, and Cybernetics: Systems, PP (69).en
dc.identifier.doi10.1109/TSMC.2016.2586075-
dc.identifier.urihttp://hdl.handle.net/10547/621949-
dc.description.abstractInternet of Things (IoT) technology offers opportunities to monitor lifelogging data by a variety of assets, like wearable sensors, mobile apps, etc. But due to heterogeneity of connected devices and diverse human life patterns in an IoT environment, lifelogging personal data contains huge uncertainty and are hardly used for healthcare studies. Effective validation of lifelogging personal data for longitudinal health assessment is demanded. In this paper, lifelogging physical activity (LPA) is taken as a target to explore how to improve the validity of lifelogging data in an IoT enabled healthcare system. A rule-based adaptive LPA validation (LPAV) model, LPAV-IoT, is proposed for eliminating irregular uncertainties (IUs) and estimating data reliability in IoT healthcare environments. A methodology specifying four layers and three modules in LPAV-IoT is presented for analyzing key factors impacting validity of LPA. A series of validation rules are designed with uncertainty threshold parameters and reliability indicators and evaluated through experimental investigations. Following LPAV-IoT, a case study on a personalized healthcare platform myhealthavatar connecting three state-of-the-art wearable devices and mobile apps are carried out. The results reflect that the rules provided by LPAV-IoT enable efficiently filtering at least 75% of IU and adaptively indicating the reliability of LPA data on certain condition of IoT environments.en
dc.description.sponsorshipCARRE, iManageCanceren
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/document/7516690/en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectInternet of Thingsen
dc.subjectmedical servicesen
dc.subjectuncertaintyen
dc.subjectbiomedical monitoringen
dc.subjectadaptation modelsen
dc.subjectreliabilityen
dc.subjectdata modelsen
dc.titleLifelogging data validation model for Internet of Things enabled healthcare systemen
dc.typeArticleen
dc.contributor.departmentLiverpool John Moores Universityen
dc.contributor.departmentKaunas University of Technology, Lithuaniaen
dc.contributor.departmentOld Dominion Universityen
dc.contributor.departmentUniversity of Bedfordshireen
dc.contributor.departmentExeter Universityen
dc.identifier.journalIEEE Transactions on Systems, Man, and Cybernetics: Systemsen
dc.date.updated2017-01-19T10:58:13Z-
This item is licensed under a Creative Commons License
Creative Commons
All Items in UOBREP are protected by copyright, with all rights reserved, unless otherwise indicated.