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首页> 外文期刊>Journal of Biomechanics >Clustering vertical ground reaction force curves produced during countermovement jumps
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Clustering vertical ground reaction force curves produced during countermovement jumps

机译：反向运动跳跃时产生的垂直地面反作用力曲线聚类

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摘要

The aim of this study is to assess and compare the performance of commonly used hierarchical, partitional (k-means) and Gaussian model-based (Expectation-Maximization algorithm) clustering techniques to appropriately identify subgroup patterns within vertical ground reaction force data, using a continuous waveform analysis. In addition, we also compared the performance across each technique using normalized and non-normalization input scores. Both generated and real data (one hundred and twenty two vertical jumps) were analyzed. The performance of each cluster technique was measured by assessing the ability to explain variances in jump height using a stepwise regression analysis. Only k-means (normalized scores; 82%) and hierarchical clustering (normalized scores; 85%) were able to extend the ability to describe variances in jump height beyond that achieved using the group analysis (i.e. one cluster; 78%). Further, our findings strongly indicate the need to normalize the input data (similarity measure) when clustering. In contrast to the group analysis, the subgroup analysis was able to identify cluster specific phases of variance, which improved the ability to explain variances in jump height, due to the identification of cluster specific predictor variables. Our findings therefore highlight the benefit of performing a subgroup analysis and may explain, at least in part, the contrasting findings between previous studies that used a single group level of analysis.
机译：这项研究的目的是评估和比较常用的分层，分区（k均值）和基于高斯模型（期望最大化算法）的聚类技术的性能，以在垂直地面反作用力数据中适当地识别子组模式。连续波形分析。此外，我们还使用归一化和非归一化输入得分比较了每种技术的性能。分析了生成的数据和实际数据（122个垂直跳变）。通过使用逐步回归分析评估解释跳跃高度方差的能力来衡量每种聚类技术的性能。只有k均值（归一化分数; 82％）和层次聚类（归一化分数; 85％）能够将描述跳跃高度方差的能力扩展到使用组分析所无法达到的水平（即一个聚类; 78％）。此外，我们的发现强烈表明在聚类时需要对输入数据（相似性度量）进行标准化。与组分析相比，子组分析能够识别特定于群集的方差阶段，由于识别了特定于群集的预测变量，从而提高了解释跳跃高度方差的能力。因此，我们的发现凸显了进行亚组分析的好处，并且可能至少部分解释了以前使用单一组水平分析的研究之间的对比结果。

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