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本文引用的文献

1
Motor abundance contributes to resolving multiple kinematic task constraints.运动丰富性有助于解决多种运动任务约束。
Motor Control. 2010 Jan;14(1):83-115. doi: 10.1123/mcj.14.1.83.
2
The principle of superposition in human prehension.人类抓握中的叠加原理。
Robotica. 2004 Mar 1;22(2):231-234. doi: 10.1017/S0263574703005344.
3
The sources of two components of variance: an example of multifinger cyclic force production tasks at different frequencies.方差两个组成部分的来源:不同频率下多手指循环力产生任务的一个例子。
Exp Brain Res. 2009 Jun;196(2):263-77. doi: 10.1007/s00221-009-1846-x. Epub 2009 May 26.
4
Hierarchical control of static prehension: II. Multi-digit synergies.静态抓握的分层控制:II. 多指协同作用。
Exp Brain Res. 2009 Mar;194(1):1-15. doi: 10.1007/s00221-008-1663-7. Epub 2008 Dec 2.
5
Flexible muscle modes and synergies in challenging whole-body tasks.具有挑战性的全身任务中的灵活肌肉模式与协同作用。
Exp Brain Res. 2008 Aug;189(2):171-87. doi: 10.1007/s00221-008-1413-x. Epub 2008 Jun 3.
6
Do synergies improve accuracy? A study of speed-accuracy trade-offs during finger force production.协同作用能提高准确性吗?一项关于手指力量产生过程中速度-准确性权衡的研究。
Motor Control. 2008 Apr;12(2):151-72. doi: 10.1123/mcj.12.2.151.
7
Do synergies decrease force variability? A study of single-finger and multi-finger force production.协同作用会降低力量变异性吗?单指和多指力量产生的研究。
Exp Brain Res. 2008 Jul;188(3):411-25. doi: 10.1007/s00221-008-1371-3. Epub 2008 Apr 19.
8
Neuromechanics of muscle synergies for posture and movement.用于姿势和运动的肌肉协同作用的神经力学
Curr Opin Neurobiol. 2007 Dec;17(6):622-8. doi: 10.1016/j.conb.2008.01.002. Epub 2008 Mar 4.
9
Multi-muscle synergies in an unusual postural task: quick shear force production.多肌肉协同作用在一个不寻常的姿势任务中:快速剪切力产生。
Exp Brain Res. 2008 May;187(2):237-53. doi: 10.1007/s00221-008-1299-7. Epub 2008 Feb 16.
10
What do synergies do? Effects of secondary constraints on multidigit synergies in accurate force-production tasks.协同作用有什么作用?在精确力量产生任务中,次要约束对多手指协同作用的影响。
J Neurophysiol. 2008 Feb;99(2):500-13. doi: 10.1152/jn.01029.2007. Epub 2007 Nov 28.

多肌肉协同作用在双重姿势任务中:叠加原理的证据。

Multi-muscle synergies in a dual postural task: evidence for the principle of superposition.

机构信息

Department of Kinesiology, The Pennsylvania State University, Rec.Hall-267, University Park, PA 16802, USA.

出版信息

Exp Brain Res. 2010 Apr;202(2):457-71. doi: 10.1007/s00221-009-2153-2. Epub 2010 Jan 5.

DOI:10.1007/s00221-009-2153-2
PMID:20047089
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2852137/
Abstract

We used the framework of the uncontrolled manifold hypothesis to quantify multi-muscle synergies stabilizing the moment of force about the frontal axis (M(Y)) and the shear force in the anterior-posterior direction (F(X)) during voluntary body sway performed by standing subjects. We tested a hypothesis whether the controller could stabilize both M(Y) and F(X) at the same time when the task and the visual feedback was provided only on one of the variables (M(Y)). Healthy young subjects performed voluntary body sway in the anterior-posterior direction while different loads were attached at the ankle level producing horizontal forces acting forward or backwards. Principal component analysis was used to identify three M-modes within the space of integrated indices of muscle activation. Variance in the M-mode space across sway cycles was partitioned into two components, one that did not affect a selected performance variable (M(Y) or F(X)) and the other that did. Under all loading conditions and for each performance variable, a higher value for the former variance component was found. We interpret these results as reflections of two multi-M-mode synergies stabilizing both F(X) and M(Y). The indices of synergies were modulated within the sway cycle; both performance variables were better stabilized when the body moved forward than when it moved backward. The results show that the controller can use a set of three elemental variables (M-modes) to stabilize two performance variables at the same time. No negative interference was seen between the synergy indices computed for the two performance variables supporting the principle of superposition with respect to multi-muscle postural control.

摘要

我们使用非控制流形假说的框架来量化多肌肉协同作用,以稳定站立受试者自愿身体摆动时关于额轴的力矩(M(Y))和前后方向剪切力(F(X))。我们测试了一个假设,即当任务和视觉反馈仅提供一个变量(M(Y))时,控制器是否可以同时稳定 M(Y)和 F(X)。健康的年轻受试者在前后方向上进行自愿的身体摆动,同时在脚踝处施加不同的负载,产生向前或向后作用的水平力。主成分分析用于识别肌肉激活综合指标空间内的三个 M 模式。在摆动周期内,M 模式空间中的方差分为两个分量,一个不影响选定的性能变量(M(Y)或 F(X)),另一个则影响。在所有负载条件下和每个性能变量下,前一个方差分量的值更高。我们将这些结果解释为两种多 M 模式协同作用稳定 F(X)和 M(Y)的反映。协同作用指数在摆动周期内发生调制;当身体向前移动时,两个性能变量都得到更好的稳定,而当身体向后移动时则不稳定。结果表明,控制器可以使用一组三个基本变量(M 模式)同时稳定两个性能变量。对于两个性能变量计算的协同作用指数之间没有看到负干扰,这支持了多肌肉姿势控制的叠加原理。