Vis enkel innførsel

dc.contributor.authorRindal, Ole Marius Hoel
dc.contributor.authorSeeberg, Trine Margrethe
dc.contributor.authorTjønnås, Johannes
dc.contributor.authorHaugnes, Pål
dc.contributor.authorSandbakk, Øyvind
dc.date.accessioned2018-01-04T13:07:30Z
dc.date.available2018-01-04T13:07:30Z
dc.date.created2018-01-03T09:08:43Z
dc.date.issued2018
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/11250/2475706
dc.description.abstractThe automatic classification of sub-techniques in classical cross-country skiing provides unique possibilities for analyzing the biomechanical aspects of outdoor skiing. This is currently possible due to the miniaturization and flexibility of wearable inertial measurement units (IMUs) that allow researchers to bring the laboratory to the field. In this study, we aimed to optimize the accuracy of the automatic classification of classical cross-country skiing sub-techniques by using two IMUs attached to the skier’s arm and chest together with a machine learning algorithm. The novelty of our approach is the reliable detection of individual cycles using a gyroscope on the skier’s arm, while a neural network machine learning algorithm robustly classifies each cycle to a sub-technique using sensor data from an accelerometer on the chest. In this study, 24 datasets from 10 different participants were separated into the categories training-, validation- and test-data. Overall, we achieved a classification accuracy of 93.9% on the test-data. Furthermore, we illustrate how an accurate classification of sub-techniques can be combined with data from standard sports equipment including position, altitude, speed and heart rate measuring systems. Combining this information has the potential to provide novel insight into physiological and biomechanical aspects valuable to coaches, athletes and researchers.nb_NO
dc.language.isoengnb_NO
dc.publisherMDPInb_NO
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleAutomatic Classification of Sub-Techniques in Classical Cross-Country Skiing Using a Machine Learning Algorithm on Micro-Sensor Datanb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.description.versionpublishedVersionnb_NO
dc.source.volume18nb_NO
dc.source.journalSensorsnb_NO
dc.source.issue1nb_NO
dc.identifier.doi10.3390/s18010075
dc.identifier.cristin1534305
dc.description.localcode© 2017 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/).nb_NO
cristin.unitcode194,63,25,0
cristin.unitcode194,65,30,0
cristin.unitnameInstitutt for teknisk kybernetikk
cristin.unitnameInstitutt for nevromedisin og bevegelsesvitenskap
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal