University of Nantes, Laboratory Motricité, Interactions, Performance, EA 4334, 25 bis boulevard Guy Mollet, BP 72206, 44322 Nantes cedex 3, France.
J Appl Physiol (1985). 2010 Jun;108(6):1727-36. doi: 10.1152/japplphysiol.01305.2009. Epub 2010 Mar 18.
Our aim was to determine whether muscle synergies are similar across trained cyclists (and thus whether the same locomotor strategies for pedaling are used), despite interindividual variability of individual EMG patterns. Nine trained cyclists were tested during a constant-load pedaling exercise performed at 80% of maximal power. Surface EMG signals were measured in 10 lower limb muscles. A decomposition algorithm (nonnegative matrix factorization) was applied to a set of 40 consecutive pedaling cycles to differentiate muscle synergies. We selected the least number of synergies that provided 90% of the variance accounted for VAF. Using this criterion, three synergies were identified for all of the subjects, accounting for 93.5+/-2.0% of total VAF, with VAF for individual muscles ranging from 89.9+/-8.2% to 96.6+/-1.3%. Each of these synergies was quite similar across all subjects, with a high mean correlation coefficient for synergy activation coefficients (0.927+/-0.070, 0.930+/-0.052, and 0.877+/-0.110 for synergies 1-3, respectively) and muscle synergy vectors (0.873+/-0.120, 0.948+/-0.274, and 0.885+/-0.129 for synergies 1-3, respectively). Despite a large consistency across subjects in the weighting of several monoarticular muscles into muscle synergy vectors, we found larger interindividual variability for another monoarticular muscle (soleus) and for biarticular muscles (rectus femoris, gastrocnemius lateralis, biceps femoris, and semimembranosus). This study demonstrated that pedaling is accomplished by the combination of the similar three muscle synergies among trained cyclists. The interindividual variability of EMG patterns observed during pedaling does not represent differences in the locomotor strategy for pedaling.
我们的目的是确定肌肉协同作用是否在受过训练的自行车运动员之间相似(因此是否使用相同的蹬踏运动策略),尽管个体肌电图模式存在个体差异。九名受过训练的自行车运动员在以 80%最大功率进行的恒负荷蹬踏运动中接受了测试。表面肌电图信号在 10 个下肢肌肉中进行了测量。使用分解算法(非负矩阵分解)对 40 个连续蹬踏周期进行分析,以区分肌肉协同作用。我们选择了提供 90%方差解释的最小协同数 VAF。使用此标准,所有受试者均确定了三个协同作用,占总 VAF 的 93.5+/-2.0%,个体肌肉的 VAF 范围为 89.9+/-8.2%至 96.6+/-1.3%。这些协同作用中的每一个在所有受试者中都非常相似,协同作用激活系数的平均相关系数很高(0.927+/-0.070、0.930+/-0.052 和 0.877+/-0.110 分别用于协同作用 1-3),肌肉协同作用向量的平均相关系数也很高(0.873+/-0.120、0.948+/-0.274 和 0.885+/-0.129 分别用于协同作用 1-3)。尽管在将几个单关节肌肉加权到肌肉协同作用向量时,受试者之间存在很大的一致性,但我们发现另一个单关节肌肉(比目鱼肌)和双关节肌肉(股直肌、外侧腓肠肌、股二头肌和半膜肌)的个体间变异性更大。这项研究表明,蹬踏是由受过训练的自行车运动员之间相似的三个肌肉协同作用组合完成的。在蹬踏过程中观察到的肌电图模式的个体间变异性并不代表蹬踏运动策略的差异。