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The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces

Mark White, Jonathon Neville, Paul Rees Orcid Logo, Huw Summers Orcid Logo, Neil Bezodis Orcid Logo

Journal of Biomechanics, Volume: 140, Start page: 111167

Swansea University Authors: Mark White, Paul Rees Orcid Logo, Huw Summers Orcid Logo, Neil Bezodis Orcid Logo

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DOI (Published version): 10.1016/j.jbiomech.2022.111167

Abstract

Functional principal components define modes of variation in time series, which represent characteristic movement patterns in biomechanical data. Their usefulness however depends on the prior choices made in data processing. Recent research showed that better curve alignment achieved with registrati...

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Published in: Journal of Biomechanics
ISSN: 0021-9290
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60096
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The best classifier achieved a 5.5 percentage point improvement over the equivalent unregistered model. However, registration was detrimental to the jump height models, although this performance variable may be a special case given its direct relationship with impulse. Our meta-models revealed the relative contributions made by various preprocessing operations, highlighting that registration does not generalise so well to new data. 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spelling 2022-09-07T12:33:03.3495723 v2 60096 2022-05-27 The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces 725158b503e2be11ce4cc531afe08990 Mark White Mark White true false 537a2fe031a796a3bde99679ee8c24f5 0000-0002-7715-6914 Paul Rees Paul Rees true false a61c15e220837ebfa52648c143769427 0000-0002-0898-5612 Huw Summers Huw Summers true false 534588568c1936e94e1ed8527b8c991b 0000-0003-2229-3310 Neil Bezodis Neil Bezodis true false 2022-05-27 EAAS Functional principal components define modes of variation in time series, which represent characteristic movement patterns in biomechanical data. Their usefulness however depends on the prior choices made in data processing. Recent research showed that better curve alignment achieved with registration (dynamic time warping) reduces errors in linear models predicting jump height. However, the efficacy of registration in different preprocessing combinations, including time normalisation, padding and feature extraction, is largely unknown. A more comprehensive analysis is needed, given the potential value of registration to machine learning in biomechanics. We evaluated popular preprocessing methods combined with registration, creating 512 models based on ground reaction force data from 385 countermovement jumps. The models either predicted jump height or classified jumps into those performed with or without arm swing. Our results show that the classification models benefited from registration in various forms, particularly when landmarks were placed at critical points. The best classifier achieved a 5.5 percentage point improvement over the equivalent unregistered model. However, registration was detrimental to the jump height models, although this performance variable may be a special case given its direct relationship with impulse. Our meta-models revealed the relative contributions made by various preprocessing operations, highlighting that registration does not generalise so well to new data. Nonetheless, our analysis shows the potential for registration in further biomechanical applications, particularly in classification, when combined with the other appropriate preprocessing operations. Journal Article Journal of Biomechanics 140 111167 Elsevier BV 0021-9290 Curve registration; Dynamic Time Warping; Functional Principal Component Analysis; Analysis of Characterising Phases; Countermovement jump; Classification models 1 7 2022 2022-07-01 10.1016/j.jbiomech.2022.111167 COLLEGE NANME Engineering and Applied Sciences School COLLEGE CODE EAAS Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was partly funded by the Engineering and Physical Sciences Research Council (EPSRC) in the UK (Grant EP/W522545/1). 2022-09-07T12:33:03.3495723 2022-05-27T15:00:18.8250601 Faculty of Science and Engineering School of Engineering and Applied Sciences - Biomedical Engineering Mark White 1 Jonathon Neville 2 Paul Rees 0000-0002-7715-6914 3 Huw Summers 0000-0002-0898-5612 4 Neil Bezodis 0000-0003-2229-3310 5 60096__24229__08998f4cab354e52a135e3eddb1c4067.pdf 60096.pdf 2022-06-06T09:15:44.3768091 Output 3249300 application/pdf Version of Record true © 2022 The Authors. This is an open access article under the CC BY license true eng http://creativecommons.org/licenses/by/4.0/
title The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
spellingShingle The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
Mark White
Paul Rees
Huw Summers
Neil Bezodis
title_short The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
title_full The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
title_fullStr The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
title_full_unstemmed The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
title_sort The effects of curve registration on linear models of jump performance and classification based on vertical ground reaction forces
author_id_str_mv 725158b503e2be11ce4cc531afe08990
537a2fe031a796a3bde99679ee8c24f5
a61c15e220837ebfa52648c143769427
534588568c1936e94e1ed8527b8c991b
author_id_fullname_str_mv 725158b503e2be11ce4cc531afe08990_***_Mark White
537a2fe031a796a3bde99679ee8c24f5_***_Paul Rees
a61c15e220837ebfa52648c143769427_***_Huw Summers
534588568c1936e94e1ed8527b8c991b_***_Neil Bezodis
author Mark White
Paul Rees
Huw Summers
Neil Bezodis
author2 Mark White
Jonathon Neville
Paul Rees
Huw Summers
Neil Bezodis
format Journal article
container_title Journal of Biomechanics
container_volume 140
container_start_page 111167
publishDate 2022
institution Swansea University
issn 0021-9290
doi_str_mv 10.1016/j.jbiomech.2022.111167
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
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 - Biomedical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Biomedical Engineering
document_store_str 1
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description Functional principal components define modes of variation in time series, which represent characteristic movement patterns in biomechanical data. Their usefulness however depends on the prior choices made in data processing. Recent research showed that better curve alignment achieved with registration (dynamic time warping) reduces errors in linear models predicting jump height. However, the efficacy of registration in different preprocessing combinations, including time normalisation, padding and feature extraction, is largely unknown. A more comprehensive analysis is needed, given the potential value of registration to machine learning in biomechanics. We evaluated popular preprocessing methods combined with registration, creating 512 models based on ground reaction force data from 385 countermovement jumps. The models either predicted jump height or classified jumps into those performed with or without arm swing. Our results show that the classification models benefited from registration in various forms, particularly when landmarks were placed at critical points. The best classifier achieved a 5.5 percentage point improvement over the equivalent unregistered model. However, registration was detrimental to the jump height models, although this performance variable may be a special case given its direct relationship with impulse. Our meta-models revealed the relative contributions made by various preprocessing operations, highlighting that registration does not generalise so well to new data. Nonetheless, our analysis shows the potential for registration in further biomechanical applications, particularly in classification, when combined with the other appropriate preprocessing operations.
published_date 2022-07-01T20:11:57Z
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score 11.04748

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