## 2 Related Works  
The field of determining human key joint positions has witnessed several notable trends and advancements. Marker- and sensor-based technologies have been widely used and established as the primary methods for capturing and analyzing human motion in fields such as clinical biomechanics, animation, sports, and research [3; 15; 16; 17]. However, these technologies come with limitations like high operation costs and maintenance requirements, as well as the need for a controlled environment. In addition, potential issues like occlusions and drifts can arise from poor marker and sensor placements. These limitations have spurred researchers to develop more cost-effective and reliable motion capture technologies [18; 19]. The advantages, challenges, and limitations of the marker-based and sensor-based motion capture systems have been discussed in [20]. To overcome these limitations, the latest advancements in the field implement machine learning and computer vision techniques, particularly deep learning algorithms, to achieve precise and reliable identification of key points in the human body [21]. This eliminates the need for the physical placement of markers and sensors and does not require the subject to be confined in a closed controlled environment. Deep learning methods are at the forefront of the field of machine learning and have taken over most industries, including manufacturing [22], finance [23], construction [24; 25], medicine [26], criminology [27], computer vision [28], and more. The application of these methods for human key point estimation has also been extensively studied in biomedical and computer-vision-based literature [29; 30]. [PERSON] et al. provides a comprehensive review of recent deep learning-based solutions for 2D and 3D pose estimation, systematically analyzing and comparing them based on their input data and inference procedures [4]. These techniques have shown promising results and are being employed in various biomedical research studies to assess their clinical viability [31; 32; 33; 34]. However, these methods have not yet been commercialized, and their implementation in human motion analysis continues to be an active area of research. Several deep learning frameworks have been developed that provide researchers with sets of tools, algorithms, and abstractions to simplify the development, training, and deployment of deep learning models of interest. The current state-of-the-art frameworks include PyTorch [35], TensorFlow [36], Keras [37], etc. These frameworks can be trained with different forms of datasets based on the purpose intended for the model. Various libraries built on these frameworks intended for 2D and 3D human pose estimation have been proposed in the literature. Openpose, DensePose, Alphapose, and HRNET (High-Resolution Net) are among the most discussed libraries for the marker-less human pose estimation system discussed in the literature [5; 6; 7; 14]. These libraries have been used to develop the prediction model by utilizing various publicly available datasets. Various large-scale datasets for training such deep learning models have been proposed and discussed widely in recent years. The state-of-the-art dataset called COCO has proven to be significant for object detection, segmentation, and captioning tasks [13]. It has features like object segmentation, context recognition, and super pixel stuff segmentation. With over 330 K images, 1.5 million object instances, and 80 object categories, it provides a diverse training resource and is utilized to build various popular neural network models like OpenPose, DensePose, etc. Another popular dataset called MPII Human Pose Dataset [38] contains around 25,000 images of people in various everyday activities. It includes detailed annotations for body joint positions. Similarly, Human3.6m [39] stands as a widely used benchmark dataset for 3D human pose estimation in recent years. It provides accurate 3D joint positions for multiple subjects performing various activities.