| An artificial neural network model of energy expenditure using nonintegrated acceleration signals. | |
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MedLine Citation:
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PMID: 17641221 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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Accelerometers are a promising tool for characterizing physical activity patterns in free living. The major limitation in their widespread use to date has been a lack of precision in estimating energy expenditure (EE), which may be attributed to the oversimplified time-integrated acceleration signals and subsequent use of linear regression models for EE estimation. In this study, we collected biaxial raw (32 Hz) acceleration signals at the hip to develop a relationship between acceleration and minute-to-minute EE in 102 healthy adults using EE data collected for nearly 24 h in a room calorimeter as the reference standard. From each 1 min of acceleration data, we extracted 10 signal characteristics (features) that we felt had the potential to characterize EE intensity. Using these data, we developed a feed-forward/back-propagation artificial neural network (ANN) model with one hidden layer (12 x 20 x 1 nodes). Results of the ANN were compared with estimations using the ActiGraph monitor, a uniaxial accelerometer, and the IDEEA monitor, an array of five accelerometers. After training and validation (leave-one-subject out) were completed, the ANN showed significantly reduced mean absolute errors (0.29 +/- 0.10 kcal/min), mean squared errors (0.23 +/- 0.14 kcal(2)/min(2)), and difference in total EE (21 +/- 115 kcal/day), compared with both the IDEEA (P < 0.01) and a regression model for the ActiGraph accelerometer (P < 0.001). Thus ANN combined with raw acceleration signals is a promising approach to link body accelerations to EE. Further validation is needed to understand the performance of the model for different physical activity types under free-living conditions. |
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Authors:
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Megan P Rothney; Megan Neumann; Ashley Béziat; Kong Y Chen |
Publication Detail:
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Type: Comparative Study; Journal Article; Research Support, N.I.H., Extramural; Validation Studies Date: 2007-07-19 |
Journal Detail:
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Title: Journal of applied physiology (Bethesda, Md. : 1985) Volume: 103 ISSN: 8750-7587 ISO Abbreviation: J. Appl. Physiol. Publication Date: 2007 Oct |
Date Detail:
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Created Date: 2007-10-05 Completed Date: 2007-12-06 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8502536 Medline TA: J Appl Physiol Country: United States |
Other Details:
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Languages: eng Pagination: 1419-27 Citation Subset: IM |
Affiliation:
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Department of Biomedical Engineering, Vanderbilt University Medical Center, Nashville, Tennessee, USA. rothneym@niddk.nih.gov |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Acceleration* Adolescent Adult Aged Calorimetry, Indirect / methods Energy Metabolism / physiology* Exercise / physiology Female Hip Humans Image Processing, Computer-Assisted Male Middle Aged Monitoring, Physiologic / instrumentation, methods* Motor Activity / physiology* Neural Networks (Computer)* Reproducibility of Results |
| Grant Support | |
ID/Acronym/Agency:
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DK-02973/DK/NIDDK NIH HHS; DK-069465/DK/NIDDK NIH HHS; HL-082988/HL/NHLBI NIH HHS; RR-00095/RR/NCRR NIH HHS |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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