The Sit-to-Stand (STS) motion is a critical functional activity often analyzed in clinical and biomechanical studies. This research examines the application of MocapMe (DeepLabCut-Based approach), a robust pose estimation tool, trained with data processed through OpenPose, for accurate STS motion analysis. By employing a two-camera setup, the author achieves a more comprehensive three-dimensional reconstruction of the movement, enhancing the accuracy and reliability of the kinematic data. OpenPose provides initial pose estimations, which are subsequently refined and filtered to eliminate noise and improve landmark detection accuracy. These refined data sets are then used to train MocapMe (DeepLabCut-based approach), leveraging its capability to adapt to specific datasets for more precise tracking. The dual-cameras system captures the STS motion from different angles, allowing for a more complete understanding of the biomechanical nuances involved in the transition. This setup significantly improves the robustness of the pose estimation by reducing occlusions and providing a fuller representation of body movements. The combined approach improves the accuracy of movement analysis and facilitates the identification of subtle variations in motor patterns, which are crucial for clinical assessments and rehabilitation monitoring. The results demonstrate that integrating OpenPose and DeepLabCut with a two-camera system can offer an effective, low-cost solution for detailed biomechanical analysis in both research and clinical settings, advancing the accuracy of human movement studies.