## 2 Materials and Methods
### Assessment Procedures  
The movement analyses were conducted in the Neuromechanics Laboratory of the Central Analytics Facilities, Stellenbosch University, Tygerberg, Cape Town, South Africa, which is a setting designed for clinical gait analysis using standard lighting with no interfering daylight changes. All the participants were asked to walk at a self-selected speed on a 10-m runway. Sagittal and frontal video recordings were obtained using static Bonita video cameras (sampling frequency: 50 Hz). Only good-quality color videos were used for the analyses. Joint poses were tracked with the pre-trained human model of the DeepLabCut toolbox (ResNet101 [13]) in both sagittal (view on the left-hand side; Figure 1) and frontal plane (view on the front side). DeepLabCut is a free and open-source toolbox that can be used for markerless pose estimation in a number of species and behaviors, in different situations and diverse backgrounds. DeepLabCut is based on machine-learning algorithms with deep neural networks programmed in Python 3, and pre-trained models are developed both for humans and other animal species [13]. The following joints and anatomical landmarks were tracked in the sagittal plane: left shoulder, left elbow, left wrist and left hip; and in the frontal plane: chin, right and left hip, left shoulder and left elbow. The tracking of joints and anatomical landmarks was visually inspected. In the case of inaccurate marker tracking (pixel accuracy was above the accuracy threshold of 5 pixels), additional frames were extracted and labeled manually, according to the protocol described by [PERSON] et al. [12]. First, outliers were extracted to remove labels with a likelihood below 0.9. Second, 20 frames were extracted from various participants and manually labeled. The data sets of the original ResNet101 model and additionally labeled frames were then merged, and the model was retrained until the loss plateaued [12]. No changes were made to the frames regarding occlusions, motion blur or inconsistencies in frame quality. The trajectories of joint locations and anatomical landmarks were subsequently imported into MATLAB (R2017b, Mathworks Inc., Natick, MA, USA) and included when the likelihood was above 0.9. The positions of the markers were used to calculate joint angles using the cosine rule (Figure 2):  
\[angle\left(\alpha,\beta,\gamma,\delta\right)=acos\frac{\alpha^{2}+b^{2}-c^{2}}{2 ab}\]  
The shoulder angle (\(\alpha\)) in the sagittal plane (i.e., shoulder flexion/extension) was calculated as the angle between the line from the mid-shoulder point to the hip marker and the line from the mid-shoulder point to the elbow marker. The elbow angle (\(\beta\)) in the sagittal plane (i.e., elbow flexion/extension) was calculated as the angle between the line from the mid-shoulder point to the elbow marker and the line from the elbow to the wrist marker. The shoulder angle (\(\gamma\)) in the frontal plane (i.e., shoulder abduction/adduction) was calculated as the angle between the line from the mid-shoulder point to the elbow marker and the line from the chin to the middle of the two hip markers. The trunk angle (\(\delta\)) in the frontal plane (i.e., trunk lateral sway) was calculated as the angle between the line from the mid-shoulder point to the middle of the two hip markers and the vertical. In the formula, \(a\) and \(b\) represent the segments that form the angle, while \(c\) represents the segment opposite to the angle. The angles were calculated per frame, and the angle trajectories were subsequently filtered using a Butterworth filter (fc = 10 Hz). Per video, strides were manually extracted (heel strike to heel strike) along with the moment of toe-off. After time normalization (0-100% gait cycle), the average trajectories of the three  
Figure 1: Representation of markerless movement tracking using the pre-trained human model in DeepLabCut. strides were calculated per participant.