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    Modified dynamic time warping (MDTW) for estimating temporal dietary patterns

    Access Status
    Fulltext not available
    Authors
    Khanna, N.
    Eicher-Miller, H.
    Verma, H.
    Boushey, Carol
    Gelfand, S.
    Delp, E.
    Date
    2018
    Type
    Conference Paper
    
    Metadata
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    Citation
    Khanna, N. and Eicher-Miller, H. and Verma, H. and Boushey, C. and Gelfand, S. and Delp, E. 2018. Modified dynamic time warping (MDTW) for estimating temporal dietary patterns, pp. 948-952.
    Source Title
    2017 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2017 - Proceedings
    DOI
    10.1109/GlobalSIP.2017.8309100
    ISBN
    9781509059904
    School
    School of Public Health
    URI
    http://hdl.handle.net/20.500.11937/68486
    Collection
    • Curtin Research Publications
    Abstract

    © 2017 IEEE. Chronic diseases such as heart disease, diabetes, and obesity are known to develop over many years and have been strongly linked with diet. However, the concept of time is not fully incorporated into most of the research investigating these associations. This is partially due to the lack of suitable distance measures for comparing time series corresponding to different eating patterns. This paper develops the concept of temporal dietary pattern (TDP) and presents dynamic time warping based novel distance measure, referred as Modified Dynamic Time Warping (MDTW), for comparing different eating patterns. An efficient algorithm for estimating MDTW distance is used in k-means clustering for comparing 24-hour dietary data and identifying TDPs. Efficacy of the proposed distance measure is shown by estimating TDPs for a representative sample of the adult US population (from the National Health and Nutrition Examination Survey).

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