Silvia Campagnini
Papers
2
Total Citations
46
H-Index
2
About
Silvia Campagnini is a researcher at the intersection of neurorehabilitation and data science, whose work is shaping how we understand and optimize recovery after stroke. Her primary research areas include robot-assisted gait rehabilitation, motor learning, and the application of machine learning for clinical outcome prediction. Campagnini’s major contribution lies in systematically evaluating how different control strategies in robotic exoskeletons affect gait patterns, providing critical evidence for designing more effective rehabilitation protocols. Her highly cited 2022 systematic review (41 citations) on this topic has become a foundational reference for clinicians and engineers alike. Additionally, she is pioneering the use of predictive modeling in post-stroke care, developing machine learning models that can forecast functional outcomes based on patient characteristics—a step toward truly personalized rehabilitation. Her work on cross-validating these models (5 citations) addresses a key gap in the field, moving beyond descriptive studies toward actionable prognostic tools. By bridging robotics, clinical assessment, and AI, Campagnini is helping to transform stroke rehabilitation from a one-size-fits-all approach into a data-driven, patient-specific science.
Research Focus
Key Achievements
Top Papers
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