Davide Baldassini

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About

Davide Baldassini is a researcher at the intersection of biomedical engineering and assistive robotics, with a primary focus on electromyography (EMG)-based control systems for hand rehabilitation. His key research areas include neural signal processing, human-machine interfaces, and robotic assistive devices designed to restore motor function in individuals with hand impairments. Baldassini’s major contribution lies in developing an artificial neural network (ANN) classifier that can predict a user’s intended hand grasp movements—such as pinching or grasping—from EMG signals *before* the actual kinematic motion occurs. This breakthrough enables real-time, intuitive control of robotic hand devices, significantly enhancing their utility in clinical and domestic settings. His most cited work, “Artificial neural network EMG classifier for functional hand grasp movements prediction” (2016), has garnered 73 citations, underscoring its impact on the field of myoelectric control. Additionally, his follow-up study on an EMG-controlled robotic hand rehabilitation device for home-based training (10 citations) demonstrates his commitment to translating lab innovations into practical, accessible tools for daily therapy. Baldassini’s work is pivotal for advancing non-invasive, patient-driven neurorehabilitation technologies.