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dc.contributor.authorVonstad, Elise Klæbo
dc.contributor.authorSu, Xiaomeng
dc.contributor.authorVereijken, Beatrix
dc.contributor.authorNilsen, Jan Harald
dc.contributor.authorBach, Kerstin
dc.date.accessioned2019-11-18T08:55:47Z
dc.date.available2019-11-18T08:55:47Z
dc.date.created2018-10-25T13:57:29Z
dc.date.issued2018
dc.identifier.citationCEUR Workshop Proceedings. 2018, 2148 27-32.nb_NO
dc.identifier.issn1613-0073
dc.identifier.urihttp://hdl.handle.net/11250/2628888
dc.description.abstractIn exercise games, it is often possible to gain rewards, i.e. points, by only partly completing an intended movement, which can undermine the effect of using such games for exercise. To ensure usability and reliability of exergames, correct movements must be accurately identified. Aim of the current study was to evaluate performance of machine learning models in classifying weightshifting movements as correct or incorrect. Eleven healthy elderly (6 F) performed a stepping exercise in a correct (with weight shift) and an incorrect (without weight shift) version. A 3D Motion Capture (3DMoCap) system calculated joint center positions (JCPs); 2270 repetitions (1133 correct) were recorded. Random Forest (RF), k-Nearest Neighbor (k-NN) and Support Vector Machine (SVM) classification models were built. Evaluation: 10fold leave-one-group-out cross validation (CV), repeated for all persons. Results showed high accuracy and recall in all classifiers. Average accuracy and recall was RF = 0.989, k-NN = 0.949, SVM = 0.958. Highest was RF on all JCPs, and SVM on shoulder JCPs (both 0.996). Lowest was k-NN on ankle JCPs (0.879). This study shows that all three models can distinguish correct and incorrect repetitions with high accuracy and recall, also by using selected JCPs. RF consistently outperformed the other models.nb_NO
dc.language.isoengnb_NO
dc.publisherCEUR Workshop Proceedingsnb_NO
dc.titleClassification of movement quality in a weight-shifting exercisenb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionacceptedVersionnb_NO
dc.source.pagenumber27-32nb_NO
dc.source.volume2148nb_NO
dc.source.journalCEUR Workshop Proceedingsnb_NO
dc.identifier.cristin1623528
dc.description.localcode© Copyright 2018 held by the authors.nb_NO
cristin.unitcode194,63,10,0
cristin.unitcode194,65,30,0
cristin.unitnameInstitutt for datateknologi og informatikk
cristin.unitnameInstitutt for nevromedisin og bevegelsesvitenskap
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


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