Papers
40
Total Citations
481
H-Index
9
About
Emanuele Lindo Secco is a prominent researcher whose work spans prosthetics, surgical robotics, neural control systems, and human-machine interfaces. His most influential contribution, "Principal Components Analysis Based Control of a Multi-DOF Underactuated Prosthetic Hand" (2010, 127 citations), established a landmark framework for simplifying the complex control of dexterous prosthetic hands, demonstrating how dimensionality-reduction techniques can make multi-degree-of-freedom prostheses more intuitive and clinically viable. Building on this foundation, Secco pioneered bio-inspired control strategies using PCA and neural networks to replicate natural hand movement, work that continues to shape myoelectric prosthetics research. His contributions extend into surgical robotics through force-sensing solutions for the EU-funded STIFF-FLOP flexible manipulator project, producing multiple well-cited sensor designs that address critical challenges in minimally invasive surgery. Secco has also advanced brain-computer interface applications, developing EEG-driven prosthetic control systems using motor imagery, and explored tactile feedback, soft robotics, and wearable EMG interfaces. Across more than two decades of research, his work reflects a consistent commitment to bridging neuroscience, engineering, and clinical need — making sophisticated robotic and prosthetic technologies more accessible, responsive, and human-centered. His cumulative citation impact underscores his enduring influence across multiple intersecting fields.
Research Focus
Key Achievements
Top Papers
- 1
- 2A three-axial body force sensor for flexible manipulators60 citations · 2014
- 3A Low Cost Eeg Based Bci Prosthetic Using Motor Imagery45 citations · 2016
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- 5
- 6A feedforward neural network controlling the movement of a 3-DOF finger22 citations · 2002
- 7
- 8A Wearable MYO Gesture Armband Controlling Sphero BB-8 Robot13 citations · 2020
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