## 2 Materials and Methods
### Data Analysis
#### 2.3.3 Functional Evaluation  
For functional evaluation, the muscle thickness and pennation angle of the fascicles were computed. For this computation, an adapted version of the method described in [PERSON] et al. (2018) was used, which was optimized for the imaging settings used in this study. First of all, the frequency spectra of the images were decomposed, as explained previously, into five levels with four orientations. Each level of detail images contains a specific structure size range (Table 1). Accordingly, for aponeurosis detection level 5 was used, whereas for fascicle detection levels 2-4 were adopted (Figure 2, 2 nd step). For both, the third orientation (\(\pi\) rad) was used, since the structures selected from this orientation are perpendicular to the ultrasound beam. Selection of only this orientation leads to a reduction in noise in the resulting detail images depicting the aponeuroses and fascicles. Subsequently, an adaptive threshold was applied to the detail images of the aponeuroses and fascicles ([PERSON] et al., 2017), which segmented the image into three clusters with minimal variance within each cluster. Finally, the clusters containing the aponeuroses or fascicles was selected (Figure 2, 3 rd step).  
For aponeurosis detection, the coordinates of the middle point of the anterior and posterior aponeurosis were manually selected in the first frame only. In some cases, another collagen structure was present near the aponeuroses. Therefore, for both the anterior and posterior aponeurosis, the biggest structure was selected in the detail image depicting the aponeuroses within a range of \(\pm\) two millimeters from these manually selected coordinates. The coordinates of the two points were updated to the center of the selected biggest structures within this four millimeter range. Subsequently, the edges of the anterior and posterior aponeurosis adhering to the muscle were detected. A first order polynomial was fit through these edges, resulting in the segmentation of the muscle (Figure 2, 4 th step). The anterior-posterior muscle thickness was estimated by averaging the vertical distances between both segmented edges for the entire field-of-view. In rare cases, a thick muscle fiber bundle was captured as the posterior aponeurosis. Hence, in case the angle of the posterior aponeurosis was bigger than three degrees, the frame was ignored in the functional analysis. In some frames, an artery was visible and mistaken for aponeurosis. These falsely detected aponeuroses were excluded through visual inspection.  
For fascicle detection, the segmented muscle area was selected in the thresholded detail image displaying the fascicles (Figure 2, 5 th step). The outer two millimeters of the selected area were disregarded, since these regions mostly contained noisy structures, which affected fascicles detection negatively. In addition, all structures smaller than the empirically determined threshold of 200 pixels were also removed, since these were too small for pennation angle computation. Through the remainder of the structures within the muscle, a first polynomial was fit through the center of masses of the left and right part of each fascicle (Figure 2, last step). Subsequently, the pennation angle of the fascicles was calculated with respect to the posterior aponeurosis. A representative pennation angle per frame was estimated by obtaining the median of the pennation angles within the frame.