主要组件分析结果对数据预处理条件的敏感性,当量化全身动力学试验对试验的变化时
Daniel P Armstrong1, Steven L Fischer1
1Department of Kinesiology and Health Sciences, University of Waterloo, Waterloo, Canada.
概括
全身动力学的主要成分分析 (PCA) 对数据预处理很敏感. 虽然总方差保持稳定,但主要组件 (PC) 中方差模式的分布可能会发生变化,影响动力变化解释.
科学领域:
- 生物力学 生物力学
- 运动科学 运动科学
- 数据分析 数据分析
背景情况:
- 了解全身动力学变异性对于分析人类运动至关重要.
- 主成分分析 (PCA) 是一种常见的方法,用于识别动力学数据中主导的方差模式.
- 在不同数据预处理步骤中PCA结果的一致性尚未得到充分证实.
研究的目的:
- 为了研究主要成分分析 (PCA) 识别的全身动力变量的方差模式的一致性.
- 确定不同数据预处理条件如何影响动力学变量的解释.
主要方法:
- 来自共同数据集的全身动态数据被使用.
- 轨迹数据在不同的条件下进行了预处理:全球和本地参考框架,不同数量的地标,以及对身高的规范化.
- 应用主要成分分析 (PCA) 来识别试验对试验动力学变量的方差模式.
主要成果:
- 不同的数据预处理条件并没有影响PCA.发现的总差异.
- 然而,预处理选择影响了主要组件 (PC) 间差异模式的分布.
- 这种跨PC的方差再分配可以改变动力学变量的解释.
结论:
- 数据预处理显著影响通过PCA.通过全身动力学变异性识别的特定变异模式.
- 研究人员必须仔细考虑和报告预处理方法,以确保准确解释动力变量的动力变化.
- 预处理步骤的标准化可能是必要的,以便在分析动力学变量的研究中进行可靠的比较.
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