Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features

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dc.contributor.author Richter, Chris
dc.contributor.author King, Enda
dc.contributor.author Falvey, Éanna
dc.contributor.author Franklyn-Miller, Andrew
dc.date.accessioned 2017-11-13T10:15:48Z
dc.date.available 2017-11-13T10:15:48Z
dc.date.issued 2017-10-01
dc.identifier.citation Richter, C., King, E., Falvey, E. and Franklyn-Miller, A. (2017) 'Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features', Journal of Biomechanics. In Press. doi:10.1016/j.jbiomech.2017.10.025 en
dc.identifier.startpage 1 en
dc.identifier.endpage 29 en
dc.identifier.issn 0021-9290
dc.identifier.uri http://hdl.handle.net/10468/5011
dc.identifier.doi 10.1016/j.jbiomech.2017.10.025
dc.description.abstract This study examines the ability of commonly used supervised learning techniques to classify the execution of a maximum effort change of direction task into predefined movement pattern as well as the influence of fuzzy executions and the impact of selected features (e.g. peak knee flexion) towards classification accuracy. The experiment utilized kinematic and kinetic data from 323 male subjects with chronic athletic groin pain. All subjects undertook a biomechanical assessment and had been divided previously into 3 different movement strategies in an earlier paper. Examined supervised learning techniques were: a decision tree, an ensemble of decision trees, a discriminant analysis model, a naive Bayes classifier, a k-nearest-neighbour model, a multi-class model for support vector machines, a stepwise forward regression model, a neural network and a correlation approach. Performance (measured by comparing the predefined and classified movement pattern) was highest for the correlation approach (82 % - CI 81 to 83 %) and support vector machine (80 % - CI 79 to 80 %). The percentage of fuzzy observations within the data was between 15 and 25 %. The most informative features for classification were: hip flexion angle, ankle rotation angle, a flexion moment [ankle and hip] and thorax flexion. Findings of this study support the assumption that multiple patterns are used to execute a movement task and demonstrate that classification models can predict movement patterns with a high accuracy (83 %). en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Elsevier en
dc.relation.uri https://www.sciencedirect.com/science/article/pii/S0021929017305572
dc.rights © 2017 Elsevier B.V. This manuscript version is made available under the CC BY-NC-ND 4.0 license. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ en
dc.subject Movement classification en
dc.subject Subgroup analysis en
dc.subject Change of direction manoeuvre en
dc.subject Athletic groin pain en
dc.subject Biomechanical assessment en
dc.title Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Éanna Falvey, Medicine, University College Cork, Cork, Ireland. T: +353 21 490 300 Email: eanna.falvey@ucc.ie en
dc.internal.availability Full text available en
dc.check.info Access to this article is restricted until 12 months after publication by request of the publisher. en
dc.check.date 2018-10-31
dc.description.version Accepted Version en
dc.description.status Peer reviewed en
dc.identifier.journaltitle Journal of Biomechanics en
dc.internal.IRISemailaddress eanna.falvey@ucc.ie en


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© 2017 Elsevier B.V. This manuscript version is made available under the CC BY-NC-ND 4.0 license. Except where otherwise noted, this item's license is described as © 2017 Elsevier B.V. This manuscript version is made available under the CC BY-NC-ND 4.0 license.
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