## 4 Discussion and Conclusions  
Marker-less motion tracking systems have emerged as a transformative technology for both rehabilitation and general physical assessment due to their ability to provide real-time feedback and progress monitoring. These systems are particularly valuable in rehabilitation, offering the potential for continuous tracking and adjustment of exercise protocols based on immediate data. Visual feedback from such systems can enhance patient engagement by making exercises more interactive and motivating. Despite these advantages, the accuracy and reliability of these systems remain a critical concern. Other researchers highlighted the variability in accuracy among different systems, emphasizing that methodological variations lead to diverse outcomes in joint coordinate predictions, angles, and depth errors [30]. Our study's contribution lies in advancing the accuracy of three-dimensional human skeleton tracking by refining depth coordinate predictions through linear regression  
Figure 25: Depth coordinate changes during the bending both upper arms in dark clothing: (**a**)—right elbow joint; (**b**)—right wrist joint; (**c**)—left elbow joint; (**d**)—left wrist joint. models; yet, it underscores the necessity for rigorous testing in real-world rehabilitation settings before drawing definitive comparisons with other systems. Traditional single-camera systems in human pose estimation are limited by significant depth prediction errors [31], particularly in scenarios where joints are obstructed or out of the camera's view. Our research reveals consistent depth coordinate errors associated with single camera setups, which is consistent with recent studies aimed at improving joint reconstruction accuracy (see Table 1). In contrast, our dual-camera approach mitigates common depth prediction inaccuracies and occlusions, enhancing the precision of joint tracking and movement analysis. This improvement is critical for rehabilitation, as it ensures more reliable and detailed data, which are necessary for effective intervention and monitoring. Unlike previous methods that trade off frame-by-frame accuracy for overall robustness, our system maintains consistent accuracy across individual frames, thus offering a more reliable solution for rehabilitation purposes. We acknowledge the depth estimation errors in the left hand as a limitation of our setup, which uses front and right-side cameras to capture human motion. Although the right-side camera mitigates some depth inaccuracies, occlusion from the human body often leaves the left-hand camera reliant on the less precise depth data from the front camera. Based on our results and recent advances, future work could explore advanced network architectures to reconcile depth inconsistencies by integrating multiple perspectives and learning scene-level depth patterns [32]. Furthermore, self-supervised approaches that use large datasets could further refine depth predictions by addressing occlusions and inconsistencies [33]. For our second database, we evaluate the impact of clothing colors with varying lightness on depth estimation accuracy and find no significant differences under normal lighting conditions. However, we do not systematically explore scenarios with dim lighting, clothing that matches the background, or indistinguishable hands (e.g., due to gloves). Future iterations of our system could address these limitations by incorporating advanced preprocessing techniques, such as dynamic brightness adjustment, or leveraging specialized camera systems. For example, systems designed for poor light conditions ([PERSON] et al. [34]) or those that combine RGB and infrared inputs for improved joint detection ([PERSON] et al. [35]) could improve robustness in challenging environments. Although our dual-camera system significantly reduces depth prediction errors and improves joint tracking, limitations remain, particularly with the fixed 90-degree angle between cameras.