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肌电图信号在步态中的相位行为:多变量统计的应用。

Phasic behavior of EMG signals during gait: Use of multivariate statistics.

机构信息

Department of Biomedical Engineering and Applied Therapeutics, Cleveland Clinic Foundation, Cleveland, Ohi, USA.

出版信息

J Electromyogr Kinesiol. 1993;3(1):51-60. doi: 10.1016/1050-6411(93)90023-P.

Abstract

The present study represents an attempt to ascertain whether there are some underlying trends that, in some combination, can explain all the variations in the linear envelopes of 16 EMG signals of selected leg muscles. Two kinds of analyses were performed: (a) a factor analysis of EMG data corresponding to 16 muscles of the lower limb and (b) a "multi-dimensional scaling" (MDS) procedure. The latter technique involved mapping the Cartesian coordinates for 16 points subject to the constraint that the distance between any two points reflected the degree of coactivity for the corresponding muscles. The results of these analyses showed that four factors could account for 91.5% of the variance in the original data set. These factors could be clearly demarcated on the "muscle map," tending to support the notion that there are motor "programs" for groups of muscles that have to perform a given function during locomotion. Further analysis of the loading matrix (i.e., correlations between each EMG vector and each of the four factors) showed groups of muscles that acted in a similar manner. The muscle groups could be divided into those that act at the times of (a) heelstrike, (b) single limb loading response, (c) propulsion phase, or else (d) acted in a biphasic manner.

摘要

本研究旨在确定是否存在某些潜在趋势,这些趋势以某种组合的形式可以解释所选腿部肌肉的 16 个肌电图信号线性包络的所有变化。进行了两种分析:(a)对下肢 16 块肌肉的肌电图数据进行因子分析;(b)进行“多维尺度”(MDS)处理。后者的技术涉及到对 16 个点的笛卡尔坐标进行映射,其约束条件是任意两个点之间的距离反映了相应肌肉的共同活动程度。这些分析的结果表明,四个因素可以解释原始数据集 91.5%的方差。这些因素在“肌肉图”上可以清楚地划分出来,这倾向于支持这样一种观点,即存在着针对特定功能的肌肉群组的运动“程序”。对加载矩阵(即,每个肌电图向量与四个因素中的每一个之间的相关性)进行进一步分析,显示出以相似方式作用的肌肉群。可以将肌肉群分为在以下四个阶段起作用的肌肉群:(a)脚跟触地,(b)单肢负荷反应,(c)推进阶段,或者(d)以双相方式作用。

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