Roberto Meattini
University of Bologna, University of Modena and Reggio Emilia, Marconi University
Papers
26
Total Citations
469
H-Index
11
About
Roberto Meattini is a robotics researcher whose work sits at the intersection of human-robot interaction, wearable assistive technology, and electromyography-based control systems. His research primarily focuses on leveraging surface electromyography (sEMG) signals to create intuitive, naturalistic interfaces between humans and robotic devices — a challenge with profound implications for rehabilitation, industrial automation, and assistive technology. Meattini's most influential contribution, "An sEMG-Based Human–Robot Interface for Robotic Hands Using Machine Learning and Synergies" (2018, 115 citations), demonstrated how muscle synergies combined with machine learning could enable natural teleoperation of robotic hands. His complementary work on soft exosuits powered by Twisted String Actuators — accumulating over 85 citations — pioneered lightweight, wearable solutions for elbow assistance and rehabilitation. Together, these works establish him as a key voice in embodied human-machine collaboration. Beyond hardware, Meattini has made significant algorithmic contributions, including deploying temporal convolutional networks on low-power edge microcontrollers for real-time hand kinematics regression, and developing minimally supervised learning methods using soft-DTW neural networks. His 2022 comprehensive review of human-to-robot hand motion mapping methods further cements his role as both an innovator and synthesizer within the field, providing valuable conceptual frameworks for fellow researchers.
Research Focus
Key Achievements
Top Papers
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- 5Human to Robot Hand Motion Mapping Methods: Review and Classification25 citations · 2022
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