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Abdominal and erector spinae muscle activity during gait: the use of cluster analysis to identify patterns of activity.

机译:步态期间腹部和直立脊柱肌肉活动:使用聚类分析来识别活动模式。

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Objective. To describe patterns of muscle activation during gait in selected abdominal and lumbar muscles using cluster analysis.Participants. 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.Results. 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.Conclusions. 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.RelevanceThe 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.
机译:目的。使用聚类分析来描述选定的腹部和腰部肌肉在步态中的肌肉激活模式。 38名健康成人志愿者的便利性样本。记录右内,外斜肌,腹直肌和腰直肌脊柱的肌电活动,并在5%的步幅内计算每条肌肉的均方根值。将这些值标准化为最大努力等距肌肉收缩。聚类分析用于确定活动和激活水平相似的受试者组。聚类分析确定了内斜肌,外斜肌和腹直肌的两种活动模式。在腰直肌脊柱中,观察到三种活动模式。在大多数情况下,对每种肌肉观察到的模式在激活水平的大小上是不同的。在腹直肌和外斜肌中,大多数受试者的活动水平较低(<最大自愿收缩的5.0%),在整个跨步周期中相对恒定。在内侧斜肌和竖脊肌中,观察到了更明显的活动爆发,大多数情况下接近脚部撞击。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所观察到的模式。聚类分析对于将具有相似肌肉活动模式的受试者分组非常有用。它提供的证据表明,如果将一组整体作为步态中任何特定肌肉的``规范''呈现出来,则可能无法观察到亚组。相关性识别肌肉活动的常见变化可能对识别具有肌电图模式的个体具有重要意义。可能会影响他们受伤的机会。替代地,群集可以提供与肌肉活动有关的重要信息,这些信息在表现良好或在特定损伤后表现良好的那些人中。

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