## 4 Discussion
In some cases, excessive trunk sway or shoulder deviations may serve as a strategy to counteract muscle weakness, impaired coordination, or reduced proprioception, ultimately influencing overall gait efficiency and energy expenditure. In addition, several children with HIVE appeared to show two movements towards shoulder abduction during the gait cycle. These two 'bumps' (Figure 3), which occurred during late stance (around 40% of gait cycle) and late swing (approximately 85% of gait cycle), could be related to the spastic gait pattern, as also shown in children with spastic CP during gait [28; 29]. A double bump pattern in the arm swing during one gait cycle (i.e., stride) is often seen in the sagittal plane in healthy adults when walking very slowly [30]. In patients with neurologic disorders, however, this double bump pattern has been found to be present independent of the gait speed [31; 32]. In patients with stroke, it was found that the group with this altered arm swing coordination during gait showed a greater level of upper limb impairment (reduced Fugl-Meyer upper extremity score) and more spasticity in the internal shoulder rotators and elbow extensors, suggesting that neuromechanical control might be an important factor contributing to the altered arm-to-leg coordination pattern [32]. This should be further investigated in a larger sample of children with HIVE and spastic diplegia. In this study, the DeepLabCut pre-trained human model (ResNet101) was used [12]. In the frontal plane, tracking the predefined joints and anatomical landmarks was suitable,  
Figure 3: Joint angles in sagittal and frontal planes presented for children with HIVE (red) and TD children (green) (thick lines = median; dashed lines = interquartile ranges (1 st and 3 rd percentile)) over the gait cycle. Parts A and B are modified and updated versions of those presented in [PERSON] et al. 2020 [19]. showing sufficient accuracy. One of the limitations, however, was that in the sagittal plane, tracking accuracy was limited using the pre-trained human model, requiring retraining of the network with additional self-labeled frames. This could be explained by the pre-trained human model, which was based on typical adult gait, where, in this study, children with an impaired gait pattern were included as well as TD children. It is therefore important to note that additional training, e.g., on frames of individuals with similar conditions or gait complications, or fine-tuning of pre-trained human models, is necessary before implementing markerless pose estimation for clinical gait analysis for individuals with atypical gaits and compensatory upper body movements. Previous research has shown that manual annotation of key points in human movement is a valid and reliable method in typically developing populations [33; 34; 35; 36; 37; 38]. Furthermore, a recent study showed the validity of 2D markerless tracking using DeepLabCut compared to gold standard laboratory-based optoelectronic three-dimensional motion capture to measure joint angles in children [39]. However, to the best of our knowledge, no studies have investigated the reliability and validity of 2D manual annotation for human movement in individuals with atypical gaits, which is a limitation of this study. Another limitation of the study was that 2D movement analysis was used, while 3D movement analysis provides essential information required to prescribe suitable treatment in clinical settings [1]. Currently, 3D marker-based movement analysis systems that use stereophotogrammetry methods, such as Vicon, Optotrack and BTS systems, are acknowledged as the gold standard [40]. However, these systems are currently expensive, require a dedicated space and need trained personnel to be successfully employed, which limits accessibility, particularly for LMICs. Three-dimensional markerless movement analysis systems that use accessible cameras can provide solutions for these regions specifically.