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A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis

Mayara S. Bianchim, Melitta McNarry Orcid Logo, Alan R. Barker, Craig A. Williams, Sarah Denford, Lena Thia, Rachel Evans, Kelly Mackintosh Orcid Logo

Measurement in Physical Education and Exercise Science, Volume: 28, Issue: 2, Pages: 172 - 181

Swansea University Authors: Melitta McNarry Orcid Logo, Kelly Mackintosh Orcid Logo

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DOI (Published version): 10.1080/1091367x.2023.2271444

Abstract

This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7...

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Published in: Measurement in Physical Education and Exercise Science
ISSN: 1091-367X 1532-7841
Published: Informa UK Limited 2024
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URI: https://cronfa.swan.ac.uk/Record/cronfa64781
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spelling v2 64781 2023-10-20 A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis 062f5697ff59f004bc8c713955988398 0000-0003-0813-7477 Melitta McNarry Melitta McNarry true false bdb20e3f31bcccf95c7bc116070c4214 0000-0003-0355-6357 Kelly Mackintosh Kelly Mackintosh true false 2023-10-20 STSC This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst wearing GENEActivs (both wrists) and ActiGraphs GT9X (both wrists and waist). Three supervised learning classifiers (K-Nearest Neighbour, Random Forest and eXtreme Gradient Boosted Decision Tree) were used to identify the input signal pattern for each PA type and intensity, with a 10-fold cross-validation utilized to assess the performance of the classifiers. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity PA activities. All other models, for activity type and intensities, exceeded 97% accuracy, with a sensitivity and specificity of greater than 95%, irrespective of accelerometer brand, placement or health condition. Journal Article Measurement in Physical Education and Exercise Science 28 2 172 181 Informa UK Limited 1091-367X 1532-7841 Threshold, Physical Activity, ENMO, MAD, youth 2 4 2024 2024-04-02 10.1080/1091367x.2023.2271444 COLLEGE NANME Sport and Exercise Sciences COLLEGE CODE STSC Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported by the Cystic Fibrosis Trust UK under its programme grant for Strategic Research Centres (grant reference number RP-PG-0108-10011). 2024-04-08T13:55:57.4045640 2023-10-20T08:44:21.5989375 Faculty of Science and Engineering School of Engineering and Applied Sciences - Sport and Exercise Sciences Mayara S. Bianchim 1 Melitta McNarry 0000-0003-0813-7477 2 Alan R. Barker 3 Craig A. Williams 4 Sarah Denford 5 Lena Thia 6 Rachel Evans 7 Kelly Mackintosh 0000-0003-0355-6357 8 64781__28998__1e8b3f1b72b84a6e8b3fbb9f951d9892.pdf 64781.pdf 2023-11-13T12:12:41.0375509 Output 848246 application/pdf Version of Record true © 2023 The Author(s). Published with license by Taylor & Francis Group, LLC. Distributed under the terms of a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). true eng http://creativecommons.org/licenses/by-nc- nd/4.0/
title A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
spellingShingle A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
Melitta McNarry
Kelly Mackintosh
title_short A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
title_full A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
title_fullStr A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
title_full_unstemmed A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
title_sort A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis
author_id_str_mv 062f5697ff59f004bc8c713955988398
bdb20e3f31bcccf95c7bc116070c4214
author_id_fullname_str_mv 062f5697ff59f004bc8c713955988398_***_Melitta McNarry
bdb20e3f31bcccf95c7bc116070c4214_***_Kelly Mackintosh
author Melitta McNarry
Kelly Mackintosh
author2 Mayara S. Bianchim
Melitta McNarry
Alan R. Barker
Craig A. Williams
Sarah Denford
Lena Thia
Rachel Evans
Kelly Mackintosh
format Journal article
container_title Measurement in Physical Education and Exercise Science
container_volume 28
container_issue 2
container_start_page 172
publishDate 2024
institution Swansea University
issn 1091-367X
1532-7841
doi_str_mv 10.1080/1091367x.2023.2271444
publisher Informa UK Limited
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Sport and Exercise Sciences{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Sport and Exercise Sciences
document_store_str 1
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description This study aimed to develop and validate machine learning models to predict intensities in children and adolescents with cystic fibrosis (CF) across different accelerometry brands and placements. Thirty-five children and adolescents with CF (11.6 ± 2.8 yrs; 15 girls) and 28 healthy youth (12.2 ± 2.7 yrs; 16 girls) performed six activities whilst wearing GENEActivs (both wrists) and ActiGraphs GT9X (both wrists and waist). Three supervised learning classifiers (K-Nearest Neighbour, Random Forest and eXtreme Gradient Boosted Decision Tree) were used to identify the input signal pattern for each PA type and intensity, with a 10-fold cross-validation utilized to assess the performance of the classifiers. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity PA activities. All other models, for activity type and intensities, exceeded 97% accuracy, with a sensitivity and specificity of greater than 95%, irrespective of accelerometer brand, placement or health condition.
published_date 2024-04-02T13:55:54Z
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