## 1 Introduction  
The estimation of human poses is a critical task in the field of computer vision, with a multitude of applications across diverse fields, including healthcare, entertainment, and robotics [1]. This process involves the analysis of images or videos in order to accurately determine the positions and orientations of key points or joints within the human body. One of the most crucial aspects of human pose estimation is the capacity to accurately identify the key joint positions of the human body during various movements. By monitoring the locations of these key points over time, researchers can analyze the kinematics of human movements, including joint angles, joint velocities, accelerations, and trajectories. This can provide valuable insights into a person's movement pattern, which can assist clinicians in diagnosing and treating a variety of musculoskeletal conditions [2]. This analysis yields valuable information for the study of human motion, with implications for areas such as sports performance, ergonomics, and rehabilitation. A variety of technologies have been developed to track human key points during different human movements. Optical motion capture systems have emerged as the most popular, precise, reliable, and widely adopted technology for capturing human movements in clinical settings [3]. This technology is based on the attachment of reflective markers to the skin, a specialized camera system for tracking marker movement, and a kinematic model that converts marker positions into joint positions. However, the process of placing markers on subjects can be both laborious and time-consuming. Furthermore, the marker-based system requires subjects to operate within a confined environment, rendering it unsuitable for a multitude of other applications. In the context of sports applications, alternative systems such as inertial measurement units (IMUs) offer greater flexibility by not constraining subjects to closed environments. Nevertheless, the attachment of sensors to the subject's surface remains a necessary step, which can be impractical and might not accurately represent natural movement conditions. Consequently, the limitations of both marker-based and sensor-based technologies hinder their practicality in clinical and sports settings, as well as in forensic applications such as person identification, action classification, and anomaly detection in body movements during rehabilitation. The current developments in key point identification focus on marker-less and sensor-less systems, with the objective of overcoming the limitations and challenges associated with these approaches. These advancements leverage computer vision and machine learning techniques, specifically deep learning algorithms, to identify key points accurately and robustly in the human body without the need for physical markers or sensors. These techniques typically use convolutional neural networks (CNNs) to learn features from 2D images and then predict the positions of body joints and landmarks. This approach has the potential to outperform traditional methods that rely on handcrafted features and model-based algorithms [4]. A number of libraries have been developed and proposed for the task of human pose estimation, including OpenPose [5], Dense Pose [6], AlphaPose [7], HRNet [8], and others. These libraries use deep learning frameworks [9] for developing, training, and deploying the model for the task. These frameworks provide ease to researchers trying to validate the estimations and explore the potential of their applicability in clinical or sports biomechanics. However, the accuracy of such trained models is limited by the quantity and quality of the data available for training the model. Only high-resolution and dedicated datasets are useful for developing the desired accurate inference engine, which can be potentially implemented in biomechanical applications.