TY - JOUR
T1 - Lifelogging data validation model for Internet of Things enabled healthcare system
AU - Yang, Po
AU - Stankevicius, Dainius
AU - Marozas, Vaidotas
AU - Deng, Zhikun
AU - Liu, Enjie
AU - Lukoševicǐus, Arunas
AU - Dong, Feng
AU - Xu, Lida
AU - Min, Geyong
N1 - URL of first deposit: http://researchonline.ljmu.ac.uk/id/eprint/3797/
Date of Frist deposit:20/06/2016
Date of acceptance provided by UoA Lead
First deposited at Liverpool John Moore's university repository on 20 Jun 2016, so compliant (deposited on time). http://researchonline.ljmu.ac.uk/id/eprint/3797/. The publisher has no embargo for accepted manuscripts and usually all repositories make the full text available automatically as general discussed with our librarians. On the output record on the LJMU repository it states "Last Modified 12 Oct 2019", so I emailed them for clarifications to ensure that any modification doesn't affect compliance. LJM repository confirmed that the modification on 12th October was just the generation of a coversheet. The item was released on 16th September 2016 – this was a point in time where IEEE began asking for embargo periods which is why it wasn’t released immediately, when they changed their policy we released the document. They aren’t submitting this paper but they recommend that we added an Access2 exception on it as at the time of deposit the publisher was asking for an embargo and then the policy changed causing delay in the AMM being released, or 'Other Exception'. 'Other Exception' increases our risk factor so we selected Access Exception 2. If audited we have the email from LJM as evidence. 14/01/2021 - CBoula
PY - 2016/7/19
Y1 - 2016/7/19
N2 - 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.
AB - 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.
KW - Adaptation models
KW - Biomedical monitoring
KW - Data models
KW - Internet of things
KW - Medical services
KW - Reliability
KW - Uncertainty
U2 - 10.1109/TSMC.2016.2586075
DO - 10.1109/TSMC.2016.2586075
M3 - Article
SN - 2168-2216
VL - 48
SP - 50
EP - 64
JO - IEEE Transactions on Systems, Man, and Cybernetics: Systems
JF - IEEE Transactions on Systems, Man, and Cybernetics: Systems
IS - 1
ER -
SDG 3 Good Health and Well-being