Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using High-Precision GNSS

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 18; p. 6073
• Main Authors: Uda, Shunya, Miyamoto, Naoto, Hirose, Kiyoshi, Nakano, Hiroshi, Stöggl, Thomas, Linnamo, Vesa, Lindinger, Stefan, Takeda, Masaki
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.09.2024; MDPI
• Subjects: 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; Japan; Spain; cross-country skiing; sub-technique classification; high-precision GNSS; skating techniques
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24186073

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.