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, 38 (13). pp. 1496-1505. ISSN 0264-0414
Abstract
A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p < 0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (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: | 05 Jul 2022 12:52 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/02640414.2020.1746088 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:159856 |