Remote assessment of human motion is increasingly pivotal in clinical, sports, and rehabilitation contexts, particularly given the rise of telemedicine. While traditional motion capture systems deliver high-precision data, their dependence on expensive equipment and controlled laboratory conditions limits their broader application. Advances in computer vision have enabled the development of monocular video-based 3D human pose estimation methods, which leverage ubiquitous camera technologies to offer cost-effective and accessible kinematic analysis. This study systematically benchmarks joint angles derived from both video-based models and IMUs, addressing the gap in comparative evaluations under realistic, out-of-the-lab conditions
