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基于高精度 GNSS 的雪上越野滑雪滑跑姿势次技术动作检测与滑雪特征分析

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

机构信息

Graduate School of Health and Sports Science, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan.

Research Center for Sports Sensing, Doshisha University, 1-3 Tatara Miyakodani, Kyotanabe, Kyoto 610-0394, Japan.

出版信息

Sensors (Basel). 2024 Sep 19;24(18):6073. doi: 10.3390/s24186073.

DOI:10.3390/s24186073
PMID:39338819
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11436182/
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.

摘要

对越野滑雪比赛进行全面分析是制定有效训练目标和战术策略的关键步骤。本研究旨在开发一种方法，使用高精度运动学 GNSS 设备对雪上越野滑雪滑跑式计时赛中的子技术进行分类和分析滑雪特征。研究中，在越野滑雪道上的滑跑式计时赛中，将 GNSS 设备附着在两名运动员的头部。这些设备提供了精确的位置数据，并记录了头部的垂直和水平运动以及地面速度（VOG）。基于这些数据，通过定义 G2、G3、G4 和 G6P（带撑杆动作的 G6）的波形模式对子技术进行分类。通过将 GNSS 数据与视频分析进行比较来验证分类的有效性，该过程产生了 G2、G3、G4 和 G6P 的分类准确率在 95.0%至 98.8%之间。值得注意的是，G4 是最快的技术，不同运动员的子技术选择因滑雪速度和赛道倾斜度而异。本研究的结果对运动员和教练具有实际意义，因为它们表明高精度运动学 GNSS 设备可以准确地对滑跑式越野滑雪比赛中的子技术进行分类并检测滑雪特征，从而为训练和策略制定提供有价值的见解。

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