Human Movement Research Laboratory (MOVI-LAB), Graduate Program in Movement Sciences, Department of Physical Education, Faculty of Sciences, São Paulo State University (Unesp), Bauru 17033-360, SP, Brazil.
LaBioCoM Biomechanics and Motor Control Laboratory, EEFERP School of Physical Education and Sport of Ribeirão Preto, USP University of São Paulo, Campus Ribeirão Preto, Ribeirão Preto 14040-907, SP, Brazil.
Int J Environ Res Public Health. 2022 Jan 21;19(3):1179. doi: 10.3390/ijerph19031179.
Kicking is a fundamental skill in soccer that often contributes to match outcomes. Lower limb movement features (e.g., joint position and velocity) are determinants of kick performance. However, obtaining kicking kinematics under field conditions generally requires time-consuming manual tracking. The current study aimed to compare a contemporary markerless automatic motion estimation algorithm (OpenPose) with manual digitisation (DVIDEOW software) in obtaining on-field kicking kinematic parameters. An experimental dataset of under-17 players from all outfield positions was used. Kick attempts were performed in an official pitch against a goalkeeper. Four digital video cameras were used to record full-body motion during support and ball contact phases of each kick. Three-dimensional positions of hip, knee, ankle, toe and foot centre-of-mass (CM) generally showed no significant differences when computed by automatic as compared to manual tracking (whole kicking movement cycle), while only z-coordinates of knee and calcaneus markers at specific points differed between methods. The resulting time-series matrices of positions (r = 0.94) and velocity signals (r = 0.68) were largely associated (all < 0.01). The mean absolute error of OpenPose motion tracking was 3.49 cm for determining positions (ranging from 2.78 cm (CM) to 4.13 cm (dominant hip)) and 1.29 m/s for calculating joint velocity (0.95 m/s (knee) to 1.50 m/s (non-dominant hip)) as compared to reference measures by manual digitisation. Angular range-of-motion showed significant correlations between methods for the ankle (r = 0.59, < 0.01, ) and knee joint displacements (r = 0.84, < 0.001, ) but not in the hip (r = 0.04, = 0.85, ). Markerless motion tracking (OpenPose) can help to successfully obtain some lower limb position, velocity, and joint angular outputs during kicks performed in a naturally occurring environment.
踢球是足球中的一项基本技能,通常对比赛结果有影响。下肢运动特征(例如,关节位置和速度)是踢球表现的决定因素。然而,在现场条件下获得踢球运动学通常需要耗时的手动跟踪。本研究旨在比较一种现代无标记自动运动估计算法(OpenPose)与手动数字化(DVIDEOW 软件)在获取现场踢球运动学参数方面的效果。使用了来自所有外场位置的 17 岁以下球员的实验数据集。踢球尝试是在正式球场上对守门员进行的。使用四台数字摄像机记录每个踢球的支撑和触球阶段的全身运动。髋关节、膝关节、踝关节、脚趾和足部质心(CM)的三维位置在自动跟踪与手动跟踪(整个踢球运动周期)计算时通常没有显著差异,而只有在特定点处的膝关节和跟骨标记的 z 坐标在两种方法之间存在差异。位置(r = 0.94)和速度信号(r = 0.68)的时间序列矩阵高度相关(均<0.01)。与手动数字化相比,OpenPose 运动跟踪确定位置的平均绝对误差为 3.49 厘米(范围从 2.78 厘米(CM)到 4.13 厘米(优势髋部)),计算关节速度的平均绝对误差为 1.29 米/秒(0.95 米/秒(膝关节)至 1.50 米/秒(非优势髋部))。与手动数字化的参考测量相比,踝关节(r = 0.59,<0.01,)和膝关节位移(r = 0.84,<0.001,)的运动范围显示出显著相关性,但髋关节(r = 0.04,= 0.85,)没有。无标记运动跟踪(OpenPose)可帮助在自然发生的环境中成功获得一些下肢位置、速度和关节角度输出。