Department of Physical Performance, Norwegian School of Sport Sciences, 4014 Oslo, Norway.
Center of Alpine Sports Biomechanics, Engadin Health and Innovation Foundation, 7503 Samedan, Switzerland.
Sensors (Basel). 2021 Apr 12;21(8):2705. doi: 10.3390/s21082705.
Position-time tracking of athletes during a race can provide useful information about tactics and performance. However, carrier-phase differential global navigation satellite system (dGNSS)-based tracking, which is accurate to about 5 cm, might also allow for the extraction of variables reflecting an athlete's technique. Such variables include cycle length, cycle frequency, and choice of sub-technique. The aim of this study was to develop a dGNSS-based method for automated determination of sub-technique and cycle characteristics in cross-country ski skating. Sub-technique classification was achieved using a combination of hard decision rules and a neural network classifier (NNC) on position measurements from a head-mounted dGNSS antenna. The NNC was trained to classify the three main sub-techniques (G2-G4) using optical marker motion data of the head trajectory of six subjects during treadmill skiing. Hard decision rules, based on the head's sideways and vertical movement, were used to identify phases of turning, tucked position and G5 (skating without poles). Cycle length and duration were derived from the components of the head velocity vector. The classifier's performance was evaluated on two subjects during an in-field roller skiing test race by comparison with manual classification from video recordings. Classification accuracy was 92-97% for G2-G4, 32% for G5, 75% for turning, and 88% for tucked position. Cycle duration and cycle length had a root mean square (RMS) deviation of 2-3%, which was reduced to <1% when cycle duration and length were averaged over five cycles. In conclusion, accurate dGNSS measurements of the head's trajectory during cross-country skiing contain sufficient information to classify the three main skating sub-techniques and characterize cycle length and duration.
运动员在比赛中的位置-时间跟踪可以提供有关战术和表现的有用信息。然而，基于载波相位差分全球导航卫星系统（dGNSS）的跟踪精度约为 5 厘米，也可能允许提取反映运动员技术的变量。这些变量包括周期长度、周期频率和子技术选择。本研究的目的是开发一种基于 dGNSS 的方法，用于自动确定越野滑雪滑冰中的子技术和周期特征。使用硬决策规则和神经网络分类器（NNC）组合，根据头戴式 dGNSS 天线的位置测量结果，对位置测量结果进行分类。NNC 经过训练，可使用六名受试者在跑步机滑雪期间的头部轨迹光学标记运动数据对三个主要子技术（G2-G4）进行分类。硬决策规则基于头部的侧向和垂直运动，用于识别转弯、蜷缩位置和 G5（无杆滑冰）阶段。周期长度和持续时间源自头部速度矢量的分量。在现场滚轮滑雪测试赛中，通过与视频记录的手动分类进行比较，评估了分类器在两名受试者中的性能。G2-G4 的分类准确率为 92-97%，G5 为 32%，转弯为 75%，蜷缩位置为 88%。周期持续时间和周期长度的均方根（RMS）偏差为 2-3%，当将周期持续时间和长度平均五个周期时，偏差减少到<1%。总之，越野滑雪中头部轨迹的准确 dGNSS 测量包含足够的信息，可以对三个主要的滑冰子技术进行分类，并描述周期长度和持续时间。
