White Steven G, McNair Peter J
Physical Rehabilitation Research Unit, School of Physiotherapy, Auckland University of Technology, Private Bag 92006, Auckland, New Zealand.
Clin Biomech (Bristol). 2002 Mar;17(3):177-84. doi: 10.1016/s0268-0033(02)00007-4.
To describe patterns of muscle activation during gait in selected abdominal and lumbar muscles using cluster analysis.
A sample of convenience of 38 healthy adult volunteers. Outcome measures. Electromyographic activity from the right internal and external obliques, rectus abdominis and lumbar erector spinae were recorded, and the root mean square values for each muscle were calculated throughout the stride in 5% epochs. These values were normalised to maximum effort isometric muscle contractions. Cluster analysis was used to identify groups of subjects with similar patterns of activity and activation levels.
Cluster analysis identified two patterns of activity for the internal oblique, external oblique and rectus abdominis muscles. In the lumbar erector spinae, three patterns of activity were observed. In most instances, the patterns observed for each muscle differed in the magnitude of the activation levels. In rectus abdominis and external oblique muscles, the majority of subjects had low levels of activity (<5.0% of a maximum voluntary contraction) that were relatively constant throughout the stride cycle. In the internal oblique and the erector spinae muscles, more distinct bursts of activity were observed, most often close to foot-strike. The different algorithms used for the cluster analysis yielded similar results and a discriminant function analysis provided further evidence to support the patterns observed.
Cluster analysis was useful in grouping subjects who had similar patterns of muscle activity. It provided evidence that there were subgroups that might otherwise not be observed if a group ensemble was presented as the "norm" for any particular muscle's role during gait.
The identification of common variations in muscle activity may prove valuable in identifying individuals with electromyographic patterns that might influence their chances of sustaining injury. Alternatively, clusters may provide important information related to muscle activity in those that do well or otherwise after a particular injury.
采用聚类分析描述特定腹部和腰部肌肉在步态中的肌肉激活模式。
38名健康成年志愿者的便利样本。结果测量。记录右侧腹内斜肌、腹外斜肌、腹直肌和腰竖脊肌的肌电活动,并在整个步幅的5%时间段内计算每块肌肉的均方根值。这些值被标准化为最大等长肌肉收缩力。聚类分析用于识别具有相似活动模式和激活水平的受试者组。
聚类分析确定了腹内斜肌、腹外斜肌和腹直肌的两种活动模式。在腰竖脊肌中,观察到三种活动模式。在大多数情况下,每块肌肉观察到的模式在激活水平的大小上有所不同。在腹直肌和腹外斜肌中,大多数受试者的活动水平较低(<最大自主收缩的5.0%),在整个步幅周期中相对恒定。在腹内斜肌和竖脊肌中,观察到更明显的活动爆发,最常出现在接近足跟着地时。用于聚类分析的不同算法产生了相似的结果,判别函数分析提供了进一步的证据来支持观察到的模式。
聚类分析有助于对具有相似肌肉活动模式的受试者进行分组。它提供了证据表明,如果将一组总体作为任何特定肌肉在步态中作用的“标准”,可能会遗漏一些亚组。
识别肌肉活动中的常见变化可能在识别具有可能影响其受伤几率的肌电图模式的个体方面具有价值。或者,聚类可能提供与特定损伤后表现良好或其他情况的个体的肌肉活动相关的重要信息。