Leonardo Cangelmi
Papers
1
Total Citations
10
H-Index
1
About
Leonardo Cangelmi is a researcher at the forefront of neurorehabilitation, specializing in the intersection of machine learning and robotic therapy for stroke recovery. His work focuses on predicting and enhancing upper limb motor function in stroke survivors, a critical area where impairment is common and recovery trajectories are highly variable. In his most cited paper, "Integrating Machine Learning with Robotic Rehabilitation May Support Prediction of Recovery of the Upper Limb Motor Function in Stroke Survivors" (2024, 10 citations), Cangelmi demonstrates how ML algorithms can analyze patient data to identify optimal rehabilitation intensity and type, personalizing treatment plans for better outcomes. This contribution is significant because it moves beyond one-size-fits-all therapy, offering a data-driven path to accelerate recovery. By bridging robotics and computational modeling, Cangelmi’s work holds promise for transforming clinical practice, making rehabilitation more adaptive and effective. His research not only advances the science of motor recovery but also provides practical tools for clinicians, marking him as an emerging voice in precision neurorehabilitation.
Research Focus
Key Achievements
Top Papers
- 1