Statistica

In the next step, the k-means clustering method was applied, using Statistica (StatSoft, PL), in order to find identify the groups of in terms of the ordering the order of appearance of maximum peaks of muscle force acting on the ankle joint during gait cycle the k-mean clustering method was applied. To do this, Statistica (StatSoft, PL) was used. The procedure follows provides a way to classify a given data set through a certain number of clusters. The number of clusters (two) was chosen automatically by the software. The main idea is to define k-centroids (one for each cluster) in such a way that the centroids are placed as far from each other where the Euclidean distances between objects were calculated. The next step is to take each point belonging to a given data set and associate it to the nearest centroid. The program moves objects between those clusters with the goal to minimize variability within clusters and maximize variability between clusters. In the last step, the Shapiro-Wilk test was applied to assess normal data distribution in all muscles the four groups analyzed: i.e. during isometric and dynamic conditions and in the two clusters (Table 1). The nNon-parametric Kruskal-Wallis TestFriedman-test was used in order to detect differences between all 3 groups. A significant Pp-value was set at 0.05 for all analysis.