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Exploring relations between EMG and biomechanical data recorded during a golf swing
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research. Kaunas University of Technology, Kaunas, Lithuania.ORCID iD: 0000-0003-2185-8973
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).
Kaunas University of Technology, Kaunas, Lithuania.
Halmstad University, School of Business, Engineering and Science, The Rydberg Laboratory for Applied Sciences (RLAS).ORCID iD: 0000-0002-9337-5113
2017 (English)In: Expert systems with applications, ISSN 0957-4174, E-ISSN 1873-6793, Vol. 88, 109-117 p.Article in journal (Refereed) Published
Abstract [en]

Exploring relations between patterns of peak rotational speed of thorax, pelvis and arm, and patterns of EMG signals recorded from eight muscle regions of forearms and shoulders during the golf swing is the main objective of this article. The linear canonical correlation analysis, allowing studying relations between sets of variables, was the main technique applied. To get deeper insights, linear and nonlinear random forests-based prediction models relating a single output variable, e.g. a thorax peak rotational speed, with a set of input variables, e.g. an average intensity of EMG signals were used. The experimental investigations using data from 16 golfers revealed statistically significant relations between sets of input and output variables. A strong direct linear relation was observed between lin- ear combinations of EMG averages and peak rotational speeds. The coefficient of determination values R2 = 0 . 958 and R2 = 0 . 943 obtained on unseen data by the random forest models designed to predict peak rotational speed of thorax and pelvis , indicate high modelling accuracy. However, predictions of peak rotational speed of arm were less accurate. This was expected, since peak rotational speed of arm played a minor role in the linear combination of peak speeds. The most important muscles to predict peak rotational speed of the body parts were identified. The investigations have shown that the canon- ical correlation analysis is a promising tool for studying relations between sets of biomechanical and EMG data. Better understanding of these relations will lead to guidelines concerning muscle engagement and coordination of thorax, pelvis and arms during a golf swing and will help golf coaches in providing substantiated advices. ©2017 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
Kidlington, Oxford: Pergamon Press, 2017. Vol. 88, 109-117 p.
Keyword [en]
Canonical correlation, Random forest, Prediction, EMG, Golf
National Category
Sport and Fitness Sciences
Identifiers
URN: urn:nbn:se:hh:diva-34611DOI: 10.1016/j.eswa.2017.06.041ISI: 000408789300008Scopus ID: 2-s2.0-85021670724OAI: oai:DiVA.org:hh-34611DiVA: diva2:1123246
Funder
Knowledge Foundation, 2012/0319
Available from: 2017-07-12 Created: 2017-07-12 Last updated: 2017-11-29Bibliographically approved

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Verikas, AntanasOlsson, M. Charlotte

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