Simone Pasinetti
Papers
13
Total Citations
250
H-Index
9
About
Simone Pasinetti is a researcher specializing in human-robot interaction, computer vision, and intelligent automation systems, with particular expertise in gesture recognition for collaborative robotics. His most influential work centers on developing deep learning frameworks that enable intuitive, safe communication between humans and robots, pioneering the application of Faster R-CNN object detectors to hand gesture recognition in collaborative workspaces — research that has accumulated over 80 citations across related publications. His 2019 paper on deep learning-based gesture recognition (53 citations) and the MEGURU gesture-based robot programming platform (43 citations) represent landmark contributions to making collaborative robots more accessible and responsive to human operators. Pasinetti also created the HANDS RGB-D dataset (28 citations), a valuable open resource advancing reproducible research in human-robot interaction. Beyond gesture interfaces, his work spans robotic rehabilitation systems driven by EMG signals, augmented reality teleoperation, safety monitoring in robotic cells using Time-of-Flight cameras, occupational health surveillance for firefighters, and vision-based quality control for laser welding. Across these diverse domains, Pasinetti consistently bridges advanced machine learning with practical industrial and medical applications, establishing himself as a versatile contributor to intelligent robotics and smart manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1Deep learning-based hand gesture recognition for collaborative robots53 citations · 2019
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- 4HANDS: an RGB-D dataset of static hand-gestures for human-robot interaction28 citations · 2021
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- 7Hands-Free: a robot augmented reality teleoperation system16 citations · 2020
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- 10In-Line Monitoring of Laser Welding Using a Smart Vision System6 citations · 2018