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dc.contributor.authorFallmann, Sarah
dc.contributor.authorChen, Liming
dc.contributor.authorChen, Feng
dc.date.accessioned2020-06-03T07:49:35Z
dc.date.available2020-06-03T07:49:35Z
dc.date.issued2019-11-04
dc.identifier.citationFallmann, S., Chen, L. and Chen, F. (2019) A Home-Based IoT-Enabled Framework for Sleep Behaviour Assessment. 2019 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech), Fukuoka, Japan, November 2019, pp. 7-15.en
dc.identifier.urihttps://dora.dmu.ac.uk/handle/2086/19707
dc.description.abstractSleep has an impact on a person's life including their health and wellbeing, thus assessing sleep behaviour is of high importance to gain insight into people's general health status. In general, current sleep behaviour assessment is restricted to a controlled scenario within a hospital environment limited by the time of monitoring and to specific factors. As healthcare is shifting from reactive to preventive and predictive care with the support of digital health and IoT technology, there is a growing demand to make sleep assessment possible at home. In this paper, we propose a sleep behaviour assessment framework considering different facets of sleep such as sleep quality, regularity, circadian rhythm, environmental conditions and sleep hygiene. Hence, we describe methodologies and techniques which can help realise home-based sleep assessment. A salient feature of the framework is that it takes into account personal preferences and influential factors as well as doctor's recommendations and clinical history, thus, allowing personalised medical and behavioural assessment. In addition, the proposed framework supports a modular service-oriented design adaptable to both doctor and user needs and availability of underpinning technologies.en
dc.language.isoenen
dc.publisherIEEEen
dc.titleA Home-Based IoT-Enabled Framework for Sleep Behaviour Assessmenten
dc.typeConferenceen
dc.identifier.doihttps://doi.org/10.1109/dasc/picom/cbdcom/cyberscitech.2019.00018
dc.peerreviewedYesen
dc.funderEuropean Union (EU) Horizon 2020en
dc.projectidNo. 676157.en
dc.cclicenceCC-BY-NCen
dc.researchinstituteCyber Technology Institute (CTI)en


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