RT info:eu-repo/semantics/article
T1 Comparative evaluation of monocular deep learning pose estimation and IMU-based systems for remote kinematic assessment
A1 Medrano Paredes, Mario
A1 Fernández González, Carmen
A1 Saoudi, Hichem
A1 Pozo Catá, Jorge
A1 Díaz Pernas, Francisco Javier
A1 Martínez Zarzuela, Mario
AB 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
PB Elsevier
SN 0966-6362
YR 2025
FD 2025
LK https://uvadoc.uva.es/handle/10324/82164
UL https://uvadoc.uva.es/handle/10324/82164
LA eng
NO Gait & Posture, 121. doi.org/10.1016/j.gaitpost.2025.07.234
NO Producción Científica
DS UVaDOC
RD 26-ene-2026