Improving energy expenditure estimates from wearable devices: A machine learning approach

O’Driscoll, R, Turicchi, J orcid.org/0000-0003-1174-813X, Hopkins, M orcid.org/0000-0002-7655-0215 et al. (3 more authors) (2020) Improving energy expenditure estimates from wearable devices: A machine learning approach. Journal of Sports Sciences. ISSN 0264-0414

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Copyright, Publisher and Additional Information: © 2020 Informa UK Limited, trading as Taylor & Francis Group. This is an author produced version of a journal article published in Journal of Sports Sciences. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: Machine learning, heart rate, energy expenditure, accelerometer
Dates:
  • Accepted: 4 March 2020
  • Published (online): 6 April 2020
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Maths and Physical Sciences (Leeds) > School of Food Science and Nutrition (Leeds) > FSN Nutrition and Public Health (Leeds)
The University of Leeds > Faculty of Medicine and Health (Leeds) > School of Psychology (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 01 May 2020 08:32
Last Modified: 01 May 2020 08:32
Status: Published online
Publisher: Taylor & Francis
Identification Number: https://doi.org/10.1080/02640414.2020.1746088
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Embargoed until: 6 April 2021

Filename: Modelling study JSS - revision 2.pdf

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