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About

Andrea Mannini’s research lies at the intersection of rehabilitation robotics, human motion analysis, and machine learning, with a focus on restoring function after neurological injury. A key contribution is the systematic review of control strategies in robot-assisted gait rehabilitation for post-stroke patients (41 citations), which critically evaluated how different robotic approaches influence motor learning and recovery. Mannini has also advanced the understanding of upper limb kinematics, notably through work on synergistic patterns in healthy motion—including the wrist—and by developing cross-validated machine learning models to predict functional outcomes after robot-assisted therapy (5 citations). In the domain of sensing and actuation, Mannini contributed to early work on dielectric elastomer actuators for artificial muscle applications (11 citations), and developed a magnetic-free Extended Kalman Filter for kinematic assessment using wearable sensors, applied to activities like yoga (3 citations). More recently, Mannini is pioneering the use of non-immersive virtual reality for cognitive rehabilitation in individuals with severe acquired brain injury, as outlined in a 2025 trial protocol. This body of work demonstrates a sustained commitment to translating engineering innovations into clinically meaningful rehabilitation tools.