## 1 Introduction  
Human movement analysis is imperative for tailoring evidence-based interventions in individuals with and without a physical disability [1]. Two-dimensional movement analysis has been used to assist in clinical decision making for the past decades [2]. This process, however, requires researchers and/or clinicians to track defined markers on a frame-to-frame basis using software packages, for example Kinovea [3, 4], which is time-consuming, and trained personnel are needed. In addition, skin markers are not always comfortable for individuals, particularly for children with or without disabilities, who might find them interfering with their normal gait. Quantitative 2D gait analysis is, however, particularly beneficial in low- to middle-income countries (LMICs) since it is less expensive and easily accessible. An important application of 2D movement analysis in LMICs has been in children with physical disabilities, such as human immunodeficiency virus (HIV) encephalopathy (HIVE) [5]. HIVE is a childhood disability particularly observed in LMICs such as South Africa. Most of the children who suffer from HIV worldwide (approximately 1.8 million children) reside in African sub-Saharan LMICs, such as South Africa [6; 7]. Most children acquire HIV through vertical transmission, including the transmission of the virus in utero, at birth, or through breastfeeding. As it can affect the developing fetal and infant brain, the virus can induce the neurological complication HIVE [8], which results in an impaired gait pattern [9] and upper limb function [10]. To improve clinical care for children with HIVE, accessible, affordable and easy-to-use tools are imperative to allow for tailored clinical decision making in non-specialized, smaller clinical centers in remote, less populated areas in LIMCs. Recent advances in biomedical engineering have resulted in new techniques based on deep learning to track body landmarks in simple video recordings. Deep learning approaches for human body landmark tracking in video recordings typically employ convolutional neural networks and pose estimation architectures that process sequential frames to identify and localize anatomical key points. These models are trained on large datasets of annotated images to learn hierarchical visual features that represent body parts and their spatial relationships, enabling them to detect joint positions even under varying lighting conditions, partial occlusions, and complex backgrounds. The use of this technique includes a high degree of automation and allows for recordings in a natural environment [8; 11]. [PERSON] described the potential of markerless tracking using neural networks in the field of human movement science [8]. More recently, DeepLabCut was introduced as a free and open-source toolbox to track user-defined features in video files [12; 13]. A great advantage of DeepLabCut over other pose estimation software packages is that only a limited number of labeled frames are required to obtain deep neural networks that match human labeling accuracy [8]. Furthermore, DeepLabCut provides exceptional flexibility with a user-friendly interface that allows researchers to define custom key points specific to their research questions, rather than being limited to predefined landmark sets common in other platforms. An assessment tool based on markerless tracking of limb movement during gait may provide a solution for cost-efficient movement analysis in LMICs, especially with a free and open-source toolbox such as DeepLabCut. Therefore, the current study has several aims. (1) It is important to investigate the feasibility of markerless tracking during gait in a population specific to LMICs, such as children with HIVE. (2) We will assess whether markerless tracking during gait is sensitive to observe expected differences in gait between children with and without HIVE.