3 years ago

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

Thisstudyexaminestheabilityofcommonlyusedsupervisedlearningtechniquestoclassifytheexecutionofamaximumeffortchangeofdirectiontaskintopredefinedmovementpatternaswellastheinfluenceoffuzzyexecutionsandtheimpactofselectedfeatures(e.g.peakkneeflexion)towardsclassificationaccuracy.Theexperimentutilizedkinematicandkineticdatafrom323malesubjectswithchronicathleticgroinpain.Allsubjectsundertookabiomechanicalassessmentandhadbeendividedpreviouslyinto3differentmovementstrategiesinanearlierpaper.Examinedsupervisedlearningtechniqueswere:a decisiontree,anensembleofdecisiontrees,adiscriminantanalysismodel,anaiveBayesclassifier,ak-nearest-neighbourmodel,amulti-classmodelforsupportvectormachines,astepwiseforwardregressionmodel,aneuralnetworkandacorrelationapproach.Performance(measuredbycomparingthepredefinedandclassifiedmovementpattern)washighestforthecorrelationapproach(82%-CI81to83%)andsupportvectormachine(80%-CI79to80%).Thepercentageoffuzzyobservationswithinthedatawasbetween15 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 %).

Publisher URL: www.sciencedirect.com/science

DOI: S0021929017305572

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