Simone Benatti
University of Bologna, University of Modena and Reggio Emilia
Papers
10
Total Citations
272
H-Index
7
About
Simone Benatti is a prominent researcher specializing in human-machine interfaces, surface electromyography (sEMG)-based gesture recognition, and edge computing for wearable and robotic systems. His work sits at the intersection of biomedical signal processing, machine learning, and embedded systems, with a particular focus on enabling intuitive, real-world control of robotic and prosthetic hands. Benatti's most influential contribution, "An sEMG-Based Human–Robot Interface for Robotic Hands Using Machine Learning and Synergies" (2018, 115 citations), demonstrated how natural, synergy-driven control strategies could be developed for robotic grasping applications in industrial and aerospace contexts. His subsequent research has tackled critical practical challenges, including temporal variability in gesture recognition, on-device incremental learning, and ultra-low-power deployment of deep learning models such as Transformers and Temporal Convolutional Networks on edge microcontrollers. With over 230 cumulative citations, Benatti's portfolio reflects a consistent drive to bridge laboratory-grade performance with real-world deployability. Notably, his more recent work extends into autonomous nano-UAV navigation using heterogeneous RISC-V SoC architectures, showcasing a broadening research vision. For students and researchers in rehabilitation engineering, wearable computing, or human-robot interaction, Benatti's work offers both foundational methodologies and cutting-edge deployment strategies.
Research Focus
Key Achievements
Top Papers
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- 7A Heterogeneous RISC-V Based SoC for Secure Nano-UAV Navigation13 citations · 2024
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- 10A Heterogeneous RISC-V based SoC for Secure Nano-UAV Navigation3 citations · 2024