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    Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment

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    Authors
    Qi, Jun
    Yang, Po
    Hanneghan, Martin
    Fan, Dina
    Deng, Zhikun
    Dong, Feng
    Issue Date
    2016-10-13
    Subjects
    Internet of Things
    wearable computing
    wearable monitoring
    life-logging data
    physical activity
    B800 Medical Technology
    
    Metadata
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    Abstract
    The popular use of wearable devices and mobile phones makes the effective capture of life-logging physical activity (PA) data in an Internet of Things (IoT) environment possible. The effective collection of measures of PA in the long term is beneficial to interdisciplinary healthcare research and collaboration from clinicians, researchers and patients. However, due to heterogeneity of connected devices and rapid change of diverse life patterns in an IoT environment, life-logging PA information captured by mobile devices usually contains much uncertainty. In this study, the authors project the distribution of irregular uncertainty by defining a walking speed related score named as daily activity in physical space and present an ellipse-fitting model-based validity improvement method for reducing uncertainties of life-logging PA measures in an IoT environment. The experimental results reflect that the proposed method remarkably improves the validity of PA measures in a healthcare platform.
    Citation
    Qi J, Yang P, Hanneghan M, Fan D, Deng Z, Dong F (2016) 'Ellipse fitting model for improving the effectiveness of life-logging physical activity measures in an Internet of Things environment', IET Networks, 5 (5), pp.107-113.
    Publisher
    Institution of Engineering and Technology
    Journal
    IET Networks
    URI
    http://hdl.handle.net/10547/623788
    DOI
    10.1049/iet-net.2015.0109
    Additional Links
    https://ieeexplore.ieee.org/document/7587513
    Type
    Article
    Language
    en
    ISSN
    2047-4954
    EISSN
    2047-4962
    ae974a485f413a2113503eed53cd6c53
    10.1049/iet-net.2015.0109
    Scopus Count
    Collections
    Computing

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