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分析与站立时自愿全身运动相关的关节方差。

Analyses of joint variance related to voluntary whole-body movements performed in standing.

机构信息

São Paulo City University (UNICID), São Paulo, Brazil.

出版信息

J Neurosci Methods. 2010 Apr 30;188(1):89-96. doi: 10.1016/j.jneumeth.2010.01.023. Epub 2010 Jan 25.

DOI:10.1016/j.jneumeth.2010.01.023
PMID:20105441
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2943229/
Abstract

This article investigates two methodological issues resulting from a recent study of center of mass positional stability during performance of whole-body targeting tasks (Freitas et al., 2006): (1) Can identical results be obtained with uncontrolled manifold (UCM) variance analysis when it is based on estimating the Jacobian using multiple linear regression (MLR) analysis compared to that using typical analytic formal geometric model? (2) Are kinematic synergies more related to stabilization of the instantaneous anterior-posterior position of the center of mass (COM(AP)) or the center of pressure (COP(AP))? UCM analysis was used to partition the variance of the joint configuration into 'bad' variance, leading to COM(AP) or COP(AP) variability, and 'good' variance, reflecting the use of motor abundance. Findings indicated (1) nearly identical UCM results for both methods of Jacobian estimation; and (2) more 'good' and less 'bad' joint variance related to stability of COP(AP) than to COM(AP) position. The first result requires further investigation with more degrees of freedom, but suggests that when a formal geometric model is unavailable or overly complex, UCM analysis may be possible by estimating the Jacobian using MLR. Correct interpretation of the second result requires analysis of the singular values of the Jacobian for different performance variables, which indicates how certain amount of joint variance affects each performance variable. Thus, caution is required when interpreting differences in joint variance structure among various performance variables obtained by UCM analysis without first investigating how the different relationships captured by the Jacobian translate those variances into performance-level variance.

摘要

本文研究了最近进行的一项整体身体目标任务中质心位置稳定性研究中出现的两个方法学问题(Freitas 等人,2006 年):(1)当基于使用多元线性回归(MLR)分析估计雅可比矩阵时,与使用典型解析形式几何模型相比,非控制流形(UCM)方差分析是否可以获得相同的结果?(2)运动协同是否更与质心(COM)前后位置(AP)或压力中心(COP)的稳定性有关?UCM 分析用于将关节配置的方差分为“不良”方差,导致 COM(AP)或 COP(AP)的变异性,以及“良好”方差,反映了运动的丰富性。研究结果表明:(1)两种雅可比矩阵估计方法的 UCM 结果几乎相同;(2)与 COM(AP)位置相比,与 COP(AP)稳定性相关的“良好”和“不良”关节方差更多。第一个结果需要进一步研究自由度更多的情况,但表明当不存在正式的几何模型或模型过于复杂时,可以通过使用 MLR 估计雅可比矩阵来进行 UCM 分析。要正确解释第二个结果,需要分析不同性能变量的雅可比矩阵的奇异值,这表明一定量的关节方差如何影响每个性能变量。因此,在没有首先研究雅可比矩阵捕获的不同关系如何将这些方差转换为性能水平方差的情况下,在使用 UCM 分析获得的不同性能变量之间的关节方差结构的差异时,需要谨慎解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/a4159dfce427/nihms232657f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/f251a79b6048/nihms232657f1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/acd6b0e461d3/nihms232657f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/a4159dfce427/nihms232657f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/f251a79b6048/nihms232657f1a.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/acd6b0e461d3/nihms232657f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0ab3/2943229/a4159dfce427/nihms232657f3.jpg

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