## 4 Discussion  
The aim of this study was to investigate the feasibility of using markerless tracking to assess upper body, including upper limb and trunk, movements in children with and without HIVE, and whether expected differences could be quantified between children with and without HIVE. The findings of this study indicate that it is feasible to obtain 2D joint angles from videos using markerless tracking with DeepLabCut in children with and without HIVE, and to obtain differences between children with and without HIVE in upper limb and trunk movements during gait. This study explored the potential of markerless movement tracking with DeepLabCut software to use as a tool for clinical gait analysis in LMIC settings. The results of the study showed that DeepLabCut can successfully be used to obtain 2D gait analysis, which can save valuable time for clinical research. This observation confirms suggestions made by [PERSON] (2021), who described the potential value of pose estimation software packages for human movement analysis [8]. The implementation of markerless movement tracking systems in clinical gait analysis can serve as a useful alternative in settings where expensive gait laboratory instruments are unavailable, particularly in LMICs. In many clinical settings in LMICs, there are no resources available (too expensive) for 3D gait analysis systems, while resources are often limited, and only 2D gait analysis systems are available in high-income countries as well. It is important to note that the pre-trained human model (ResNet101), without additional manual labeling of frames, did not achieve sufficient accuracy to track markers in the sagittal plane, which could be explained by the fact that only children with and without disabilities were included, instead of adults, suggesting that both children and specifically children with motor disorders were most likely less or not present in the large datasets of annotated images to learn hierarchical visual features that represent body parts from which the anatomical joints are detected. Therefore, for the implementation of DeepLabCut in clinical settings, researchers are advised to manually label 20-50 frames with user-identified/user-defined markers specifically applicable for the laboratory and retrain the ResNet101 model to extract body landmarks from new videos automatically, especially when being used for pathological patient groups that have most likely not been included in the training models of the pretrained human model (ResNet101). In line with our hypothesis, which was based on a clinical study [10], differences in upper limb and trunk movements between children with HIVE and spastic diplegia and their TD peers were observed. The results showed that the shoulder movements of children with HIVE obtained in both sagittal and frontal plane deviated from TD children, showing more shoulder extension from mid-stance to late stance in the sagittal plane and more shoulder abduction as indicated by a higher overall ROM, as well as more abduction during the swing phase in the frontal plane (Figure 3). Trunk sway was also considerably more pronounced in children with HIVE compared to the TD, showing more lateral sway at initial contact, early stance and late swing. Both the deviated shoulder and trunk movements observed in children with HIVE are consistent with upper body movements that have previously been observed in children with spastic diplegia resulting from CP [23], individuals after stroke [24; 25; 26] and individuals with Parkinson's disease, who show deviations in trunk sway only [27]. These altered movements observed during gait in individuals with different neuromuscular conditions have been suggested to contribute to compensating for gait instability by helping to maintain balance, redistribute weight or adjust for deficits in lower limb control [14; 15; 16; 17].