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

© 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 lac...

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Main Authors: Khanna, N., Eicher-Miller, H., Verma, H., Boushey, Carol, Gelfand, S., Delp, E.
Format: Conference Paper
Published: 2018
Online Access:http://hdl.handle.net/20.500.11937/68486
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author Khanna, N.
Eicher-Miller, H.
Verma, H.
Boushey, Carol
Gelfand, S.
Delp, E.
author_facet Khanna, N.
Eicher-Miller, H.
Verma, H.
Boushey, Carol
Gelfand, S.
Delp, E.
author_sort Khanna, N.
building Curtin Institutional Repository
collection Online Access
description © 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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spelling curtin-20.500.11937-684862018-06-29T12:34:52Z Modified dynamic time warping (MDTW) for estimating temporal dietary patterns Khanna, N. Eicher-Miller, H. Verma, H. Boushey, Carol Gelfand, S. Delp, E. © 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). 2018 Conference Paper http://hdl.handle.net/20.500.11937/68486 10.1109/GlobalSIP.2017.8309100 restricted
spellingShingle Khanna, N.
Eicher-Miller, H.
Verma, H.
Boushey, Carol
Gelfand, S.
Delp, E.
Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title_full Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title_fullStr Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title_full_unstemmed Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title_short Modified dynamic time warping (MDTW) for estimating temporal dietary patterns
title_sort modified dynamic time warping (mdtw) for estimating temporal dietary patterns
url http://hdl.handle.net/20.500.11937/68486