2026-05-05T10:53:10Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/821642026-03-25T08:00:29Zcom_10324_1191com_10324_931com_10324_894col_10324_1379

00925njm 22002777a 4500 dc Medrano Paredes, Mario author Fernández González, Carmen author Saoudi, Hichem author Pozo Catá, Jorge author Díaz Pernas, Francisco Javier author Martínez Zarzuela, Mario author 2025 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 Gait & Posture, 121. doi.org/10.1016/j.gaitpost.2025.07.234 0966-6362 https://uvadoc.uva.es/handle/10324/82164 10.1016/j.gaitpost.2025.07.234 Gait & Posture 121 Comparative evaluation of monocular deep learning pose estimation and IMU-based systems for remote kinematic assessment