Lifelogging data validation model for internet of things enabled personalized healthcare
Yang, P; Stankevicius, D; Marozas, V; et al.Deng, Z; Liu, E; Lukosevicius, A; Dong, F; Xu, L; Min, G
Date: 19 July 2016
Article
Journal
IEEE Transactions on Systems, Man, and Cybernetics: Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Publisher DOI
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 ...
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, it takes lifelogging physical activity
(LPA) as a target to explore how to improve validity of lifelogging
data in an IoT enabled healthcare system. A rule based adaptive
lifelogging physical activity validation model, LPAV-IoT, is
proposed for eliminating irregular uncertainties 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
lifelogging physical activity. 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 MHA [38] 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 irregular uncertainty and adaptively indicating the
reliability of LPA data on certain condition of IoT environments
Computer Science
Faculty of Environment, Science and Economy
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