Stefano Ramat
Papers
2
Total Citations
26
H-Index
2
About
Stefano Ramat is a researcher whose work bridges biomedical engineering, neuroscience, and computer vision, with a focus on sensorimotor rehabilitation and robotic systems. His key research areas include quantitative assessment of upper limb proprioception, particularly in stroke patients, and the development of computational models for object recognition in robotic vision. In his most cited study (2014, 23 citations), Ramat developed a novel robotic-based tool to evaluate upper limb sense of position in healthy individuals and stroke survivors, demonstrating its reliability for clinical assessment. This work contributes to understanding proprioceptive deficits and improving rehabilitation strategies for motor recovery. Additionally, his earlier research on hierarchical self-organizing maps (HSOM) for color stereo image recognition (2007, 3 citations) explores clustering features from HSV color space and depth, advancing robotic vision capabilities. While his citation counts reflect a focused and emerging impact, Ramat’s contributions are notable for integrating robotics with neuroscience to address real-world clinical challenges. His work offers valuable insights for students and researchers interested in neurorehabilitation, human-robot interaction, and computational perception.
Research Focus
Key Achievements
Top Papers
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- 2