This research article investigates the effectiveness of using high-precision kinematic Global Navigation Satellite System (GNSS) devices to classify sub-techniques and analyse skiing characteristics during cross-country skiing skating style races on snow. The study aimed to improve upon existing methods that relied on video analysis, which can be challenging in complex, fast-paced environments. The authors hypothesized that the precise head movement data collected from GNSS devices could accurately identify different sub-techniques, characterized by unique vertical and horizontal head movements and variations in velocity over ground (VOG). The study involved attaching GNSS devices to the heads of two elite skiers during timed races, then analysing the recorded data. The researchers successfully classified four sub-techniques with high accuracy (95.0% to 98.8%), demonstrating the viability of using high-precision GNSS for this purpose. Furthermore, they were able to analyze skiing characteristics like cycle length, cycle time, and skiing velocity for each sub-technique, providing valuable insights for training and strategy development. The findings suggest that this method can contribute significantly to the understanding and optimization of cross-country skiing performance.
Source:
Uda, S.; Miyamoto, N.; Hirose, K.; Nakano, H.; Stöggl, T.; Linnamo, V.; Lindinger, S.; Takeda, M. Cross-Country Ski Skating Style Sub-Technique Detection and Skiing Characteristic Analysis on Snow Using High-Precision GNSS. Sensors 2024, 24, 6073. https://doi.org/10.3390/s24186073
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