## 1 Introduction  
Human pose estimation has become a popular technology in rehabilitation, offering significant advancements in patient monitoring and motor assessment [1]. Artificial intelligence and computer vision enhance tele-rehabilitation for stroke patients by tracking movement kinematics from video footage captured via everyday devices [2]. Traditional methods, such as YOLO for patient detection and MediaPipe for pose estimation, have demonstrated high accuracy in classifying various patient poses [3, 4]. Despite challenges related to complex environments and diverse body shapes, advancements in techniques like coarse-to-fine heatmap shrinking and spatial-temporal networks have improved the precision of both 2D and 3D pose estimations [5]. Virtual rehabilitation systems, such as those using PoseNet [6], enable home-based exercise with real-time joint tracking, facilitating progress assessment and personalized treatment adjustments. Moreover, the integration of low-cost sensors and high-end datasets has led to robust inertial pose estimation algorithms [7], while advancements in stereo vision and convolutional neural networks offer improved 3D pose accuracy [8, 9]. Smart walkers and combined inertial measurement units with computer vision further support comprehensive and real-time full-body pose estimation, enhancing rehabilitation effectiveness and precision [10; 11]. The evolution of motion capture and analysis technologies has provided valuable insights into movement patterns, posture, and coordination, essential for tailoring personalized rehabilitation plans [12]. Traditional motion capture systems, although highly accurate, are often cumbersome and require significant expertise. In contrast, video-based tracking systems have emerged as practical alternatives, offering real-time monitoring, visual feedback, and remote capabilities [13; 14]. These systems facilitate immediate adjustments to rehabilitation protocols, improving patient outcomes by quantifying movement improvements and enhancing engagement through visual feedback. Such innovations are particularly beneficial for remote rehabilitation settings, where patients perform exercises independently. The ongoing advancements in deep learning, including CNNs and models like BlazePose GHUM 3D, address challenges such as depth ambiguities and occlusions, continuing to refine pose estimation accuracy [15]. The integration of these technologies promises to have a transformative impact on rehabilitation practices, combining precision with accessibility for more effective patient care. Recent advancements in 3D human pose estimation have led to the development of innovative models that significantly improve accuracy on benchmark datasets like Human3.6M [16] or 3 DPW [17]. For example, [PERSON] and [PERSON], who proposed a self-supervised method for 3D human pose estimation from a single image using an ensemble of three networks without relying on limb articulation constraints or 3D pose priors, achieved 43.4 mm MPJPE on Human3.6M [18]. [PERSON] et al., in 2022, introduced Mesh Graphormer, which integrates Graph Convolutional Neural Networks with transformers for 3D human pose and mesh reconstruction, achieving 45.6 mm MPJPE on the 3 DPW dataset [19]. [PERSON] et al. developed Pose2 Mesh, a Graph Convolutional Network that converts 2D human poses directly into 3D meshes to address appearance domain gaps and representation issues, achieving 47.9 mm MPJPE on 3 DPW [20]. In 2021, [PERSON] et al. presented PoseFormer, a transformer-based model utilizing spatial-temporal attention for the estimation of 3D human poses from videos without convolutional architectures, achieving 51.4 mm MPJPE on Human3.6M [21]. Most notably, the most recent and significant results in 3D human pose estimation have been achieved by combining transformer architectures with other neural network models. For example, the SlowFastFormer model proposed by [PERSON] et al.