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Movement pattern recognition in basketball free-throw shooting

机译：篮球罚球命中中的运动模式识别

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The purpose of the present study was to analyze the movement patterns of free-throw shooters in basketball at different skill levels. There were two points of interest. First, to explore what information can be drawn from the movement pattern and second, to examine the methodological possibilities of pattern analysis. To this end, several qualitative and quantitative methods were employed. The resulting data were converged in a triangulation. Using a special kind of ANN named Dynamically Controlled Networks (DyCoN), a 'complex feature' consisting of several isolated features (angle displacements and velocities of the articulations of the kinematic chain) was calculated. This 'complex feature' was displayed by a trajectory combining several neurons of the network, reflecting the devolution of the twelve angle measures over the time course of each shooting action. In further network analyses individual characteristics were detected, as well as movement phases. Throwing patterns were successfully classified and the stability and variability of the realized pattern were established. The movement patterns found were clearly individually shaped as well as formed by the skill level. The triangulation confirmed the individual movement organizations. Finally, a high stability of the network methods was documented.
机译：本研究的目的是分析不同技能水平的篮球罚球手的运动方式。有两个景点。首先，探索可以从运动模式中获取哪些信息，其次，研究模式分析的方法学可能性。为此，采用了几种定性和定量方法。所得数据在三角剖分中收敛。使用一种称为动态控制网络（DyCoN）的特殊ANN，计算了一个“复杂特征”，该“复杂特征”由几个孤立的特征（运动链的关节的角位移和速度）组成。通过组合网络中多个神经元的轨迹来显示此“复杂特征”，反映了在每次射击动作的时间过程中十二个角度量度的下降。在进一步的网络分析中，检测到了各个特征以及运动阶段。成功地对投掷模式进行了分类，并建立了所实现模式的稳定性和可变性。所发现的运动模式显然是单独塑造的，也是由技能水平形成的。三角剖分确认了各个运动的组织。最后，记录了网络方法的高度稳定性。

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