2026-04-14T19:45:57Zhttps://uvadoc.uva.es/oai/request

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

Medrano Paredes, Mario Fernández González, Carmen Saoudi, Hichem Pozo Catá, Jorge Díaz Pernas, Francisco Javier Martínez Zarzuela, Mario 2026-01-26T13:53:15Z 2026-01-26T13:53:15Z 2025 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 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 eng info:eu-repo/semantics/embargoedAccess Comparative evaluation of monocular deep learning pose estimation and IMU-based systems for remote kinematic assessment info:eu-repo/semantics/article