Federica Ragni
Papers
7
Total Citations
69
H-Index
4
About
Federica Ragni is a researcher whose work sits at the intersection of robotic rehabilitation engineering, human-robot interaction, and medical device design. Her research focuses primarily on developing and validating end-effector-based robotic systems for upper and lower limb rehabilitation, with a strong commitment to rigorous, multi-sensor validation methodologies that ensure these devices meet clinical and regulatory standards. Ragni's most influential contribution, "Multi-Sensor Validation Approach of an End-Effector-Based Robot for the Rehabilitation of the Upper and Lower Limb" (2020, 26 citations), established a comprehensive framework for assessing robotic rehabilitation device performance — a critical challenge in translating these technologies into real physiotherapy settings. Her earlier preliminary validation work (2019, 16 citations) laid the groundwork for this approach, demonstrating her sustained commitment to evidence-based device development. Beyond hardware validation, Ragni has made notable contributions to machine learning-driven human intention prediction using wearable sensors, exploring how algorithms like Linear Discriminant Analysis and Random Forest can enhance human-robot interaction in both clinical and industrial environments. Her work on regulatory-compatible medical device design further reflects a holistic vision, bridging innovation with the practical demands of healthcare compliance — making her research particularly valuable for engineers, clinicians, and developers working to responsibly advance rehabilitation robotics.
Research Focus
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Top Papers
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