Federolf P, Reid R, Gilgien M, Haugen P, Smith G
Human Performance Laboratory, University of Calgary, Calgary, Canada.
Scand J Med Sci Sports. 2014 Jun;24(3):491-9. doi: 10.1111/j.1600-0838.2012.01455.x. Epub 2012 Mar 22.
Analyzing an athlete's "technique," sport scientists often focus on preselected variables that quantify important aspects of movement. In contrast, coaches and practitioners typically describe movements in terms of basic postures and movement components using subjective and qualitative features. A challenge for sport scientists is finding an appropriate quantitative methodology that incorporates the holistic perspective of human observers. Using alpine ski racing as an example, this study explores principal component analysis (PCA) as a mathematical method to decompose a complex movement pattern into its main movement components. Ski racing movements were recorded by determining the three-dimensional coordinates of 26 points on each skier which were subsequently interpreted as a 78-dimensional posture vector at each time point. PCA was then used to determine the mean posture and principal movements (PMk ) carried out by the athletes. The first four PMk contained 95.5 ± 0.5% of the variance in the posture vectors which quantified changes in body inclination, vertical or fore-aft movement of the trunk, and distance between skis. In summary, calculating PMk offered a data-driven, quantitative, and objective method of analyzing human movement that is similar to how human observers such as coaches or ski instructors would describe the movement.
在分析运动员的“技术”时，体育科学家通常关注预先选定的变量，这些变量可量化运动的重要方面。相比之下，教练和从业者通常使用主观和定性特征，根据基本姿势和运动组成部分来描述运动。体育科学家面临的一个挑战是找到一种合适的定量方法，该方法要纳入人类观察者的整体视角。以高山滑雪比赛为例，本研究探索主成分分析（PCA）作为一种数学方法，将复杂的运动模式分解为其主要运动成分。通过确定每位滑雪者身上26个点的三维坐标来记录滑雪比赛动作，这些坐标随后在每个时间点被解释为一个78维的姿势向量。然后使用主成分分析来确定运动员执行的平均姿势和主要动作（PMk）。前四个PMk包含姿势向量中95.5±0.5%的方差，这些方差量化了身体倾斜度、躯干的垂直或前后移动以及滑雪板之间的距离变化。总之，计算PMk提供了一种数据驱动、定量且客观的分析人类运动的方法，这与教练或滑雪教练等人类观察者描述运动的方式类似。
