Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using High-Precision GNSS
A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed race...
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Published in | Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 6073 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
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19.09.2024
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Abstract | A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study's findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development. |
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AbstractList | A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study’s findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development. A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study's findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development.A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study aimed to develop a method of classifying sub-techniques and analyzing skiing characteristics during cross-country skiing skating style timed races on snow using high-precision kinematic GNSS devices. The study involved attaching GNSS devices to the heads of two athletes during skating style timed races on cross-country ski courses. These devices provided precise positional data and recorded vertical and horizontal head movements and velocity over ground (VOG). Based on these data, sub-techniques were classified by defining waveform patterns for G2, G3, G4, and G6P (G6 with poling action). The validity of the classification was verified by comparing the GNSS data with video analysis, a process that yielded classification accuracies ranging from 95.0% to 98.8% for G2, G3, G4, and G6P. Notably, G4 emerged as the fastest technique, with sub-technique selection varying among skiers and being influenced by skiing velocity and course inclination. The study's findings have practical implications for athletes and coaches as they demonstrate that high-precision kinematic GNSS devices can accurately classify sub-techniques and detect skiing characteristics during skating style cross-country skiing races, thereby providing valuable insights for training and strategy development. |
Audience | Academic |
Author | Stöggl, Thomas Miyamoto, Naoto Hirose, Kiyoshi Uda, Shunya Takeda, Masaki Lindinger, Stefan Nakano, Hiroshi Linnamo, Vesa |
AuthorAffiliation | 4 Department of Sport and Exercise Science, University of Salzburg, 5020 Salzburg, Austria; thomas.stoeggl@sbg.ac.at 3 Department of Production System Engineering and Sciences, Komatsu University, Nu 1-3 Shicho-machi, Komatsu Ishikawa 923-8511, Japan 1 Graduate School of Health and Sports Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan; cyhj0007@mail4.doshisha.ac.jp (S.U.); cyhk0003@mail4.doshisha.ac.jp (H.N.) 2 Research Center for Sports Sensing, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan; namiyamoto@mail.doshisha.ac.jp (N.M.); kiyoshi.hirose@komatsu-u.ac.jp (K.H.) 7 Faculty of Health and Sports Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan 6 Laboratory of Biomechanics of Skiing, Via Monte Confinale, 10, 23032 Bormio, Italy; steflindinger@gmx.com 5 Faculty of Sport and Health Sciences, University of Jyväskylä, FI-40014 Jyväskylä, Finland; vesa.linnamo@jyu.fi |
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Cites_doi | 10.3390/rs8080671 10.1080/14763140408522832 10.3390/ijerph191811580 10.1519/JSC.0000000000003787 10.1111/sms.13887 10.1007/s42978-021-00112-6 10.1519/JSC.0b013e318231a799 10.1249/mss.0b013e3180537201 10.1080/14763141.2021.1959948 10.1371/journal.pone.0270331 10.3390/s16040473 10.1007/s00421-010-1535-2 10.3390/s23031205 10.3390/s21082705 10.3389/fphys.2019.01091 10.1080/02640414.2024.2340296 10.3390/s19224947 10.1123/ijspp.2013-0383 |
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Snippet | A comprehensive analysis of cross-country skiing races is a pivotal step in establishing effective training objectives and tactical strategies. This study... |
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SubjectTerms | Accuracy Adult Athletes Athletes - classification Biomechanical Phenomena Camcorders Classification Cross country skiing Equipment and supplies high-precision GNSS Humans Kinematics Male Methods Satellite Communications Sensors Skating Skating - physiology skating techniques Skiing - physiology Skis and skiing Snow sub-technique classification Velocity |
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Title | Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using High-Precision GNSS |
URI | https://www.ncbi.nlm.nih.gov/pubmed/39338819 https://www.proquest.com/docview/3110691839/abstract/ https://www.proquest.com/docview/3110912985/abstract/ https://pubmed.ncbi.nlm.nih.gov/PMC11436182 https://doaj.org/article/378dbf04a77b40128c161f141d6d690e |
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