Accurate and synchronized motion tracking is essential for advancing quantitative assessment and personalized rehabilitation. This paper presents a comprehensive infrastructure and methodology for multi-modal data acquisition designed to power AI-based rehabilitation. The system integrates multiple inertial measurements units (IMUs) and dual-camera recordings within a unified software environment that ensures reliable connectivity, synchronization and calibration. A static calibration procedure corrects sensor-to-segment misalignments, while real-time visualization enables immediate assessment of signal quality during acquisition. Although video recordings will be used exclusively for model development, their combination with IMU data will enable the creation of multimodal datasets to train AI-models that rely solely on inertial data during clinical deployment. These models aim to enhance signal accuracy by compensating for noise, drift and alignment errors. The presented infrastructure and methodology established a robust foundation for the development of AI-based rehabilitation tools to empower unsupervised rehabilitation. This works has been officially published: n Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: HEALTHINF, ISBN 978-989-758-802-0, ISSN 2184-4305, pages 269-278 Full Citation: Tzatzimaki, K.; Portokallidis, N.; Drosatos, G.; Kaldoudi, E. and Didaskalou, S. (2026). A Comprehensive Infrastructure and Methodology for Multi-Modal Data Acquisition to Empower AI-Based Rehabilitation. In Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: HEALTHINF, ISBN 978-989-758-802-0, ISSN 2184-4305, pages 269-278. https://doi.org/10.5220/0014351200004070