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dc.contributor.authorVonstad, Elise Klæbo
dc.contributor.authorSu, Xiaomeng
dc.contributor.authorVereijken, Beatrix
dc.contributor.authorBach, Kerstin
dc.contributor.authorNilsen, Jan Harald
dc.date.accessioned2021-02-02T14:08:14Z
dc.date.available2021-02-02T14:08:14Z
dc.date.created2020-12-09T09:45:21Z
dc.date.issued2020
dc.identifier.issn1424-8220
dc.identifier.urihttps://hdl.handle.net/11250/2725864
dc.description.abstractUsing standard digital cameras in combination with deep learning (DL) for pose estimation is promising for the in-home and independent use of exercise games (exergames). We need to investigate to what extent such DL-based systems can provide satisfying accuracy on exergame relevant measures. Our study assesses temporal variation (i.e., variability) in body segment lengths, while using a Deep Learning image processing tool (DeepLabCut, DLC) on two-dimensional (2D) video. This variability is then compared with a gold-standard, marker-based three-dimensional Motion Capturing system (3DMoCap, Qualisys AB), and a 3D RGB-depth camera system (Kinect V2, Microsoft Inc). Simultaneous data were collected from all three systems, while participants (N = 12) played a custom balance training exergame. The pose estimation DLC-model is pre-trained on a large-scale dataset (ImageNet) and optimized with context-specific pose annotated images. Wilcoxon’s signed-rank test was performed in order to assess the statistical significance of the differences in variability between systems. The results showed that the DLC method performs comparably to the Kinect and, in some segments, even to the 3DMoCap gold standard system with regard to variability. These results are promising for making exergames more accessible and easier to use, thereby increasing their availability for in-home exercise.en_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleComparison of a Deep Learning-Based Pose Estimation System to Marker-Based and Kinect Systems in Exergaming for Balance Trainingen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.volume20en_US
dc.source.journalSensorsen_US
dc.source.issue23en_US
dc.identifier.doi10.3390/s20236940
dc.identifier.cristin1857776
dc.description.localcodec 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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