使用函数数据分析分析休闲跑步者的运动学数据
Analysing kinematic data from recreational runners using functional data analysis
AI总结：
提出多元函数混合效应模型分析休闲跑步者运动学数据，通过mv-FPCA和线性混合效应模型拟合，发现跑步速度对髋膝角度有显著影响，方法可推广至复杂实验中的平滑数据流建模。
摘要：
我们提出了一种多元函数混合效应模型，用于处理来自大量休闲跑步者的运动学数据。跑步者的矢状面髋关节和膝关节角度被联合建模为二元函数，并使用随机效应函数来解释身体两侧测量值之间的依赖关系。该模型的拟合首先应用多元函数主成分分析（mv-FPCA），然后使用标量线性混合效应模型对 mv-FPCA 得分进行建模。我们引入了模拟和自助法来构建固定效应函数的同时置信带，并重构协方差函数以总结数据中的变异性结构，从而深入研究所提模型的适用性。在我们的科学应用中，我们观察到跑步速度对髋关节和膝关节角度均有统计学上的显著影响。我们还观察到强烈的受试者内相关性，反映了跑步技术的高度特异性。我们的方法更普遍地适用于对复杂实验设计中多个受试者重复测量的多个平滑运动学或动力学数据流进行建模。
英文摘要：
We present a multivariate functional mixed effects model for kinematic data from a large number of recreational runners. The runners' sagittal plane hip and knee angles are modelled jointly as a bivariate function with random effects functions used to account for the dependence among measurements from either side of the body. The model is fitted by first applying multivariate functional principal component analysis (mv-FPCA) and then modelling the mv-FPCA scores using scalar linear mixed effects models. Simulation and bootstrap approaches are introduced to construct simultaneous confidence bands for the fixed effects functions, and covariance functions are reconstructed to summarise the variability structure in the data and thoroughly investigate the suitability of the proposed model. In our scientific application, we observe a statistically significant effect of running speed on both the hip and knee angles. We also observe strong within-subject correlations, reflecting the highly idiosyncratic nature of running technique. Our approach is more generally applicable to modelling multiple streams of smooth kinematic or kinetic data measured repeatedly for multiple subjects in complex experimental designs.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
