Papers
8
Total Citations
948
H-Index
7
About
Matteo Cognolato is a leading researcher in the field of prosthetic hand control, with a particular focus on electromyography (sEMG)-based pattern recognition, multimodal sensing, and machine learning. His work sits at the intersection of biomedical engineering, robotics, and human-computer interaction, addressing one of rehabilitation engineering's most persistent challenges: making robotic hand prostheses natural and reliable enough for everyday use. Cognolato's most influential contribution, a 2016 paper on deep learning with convolutional neural networks applied to sEMG data, has amassed over 660 citations and has become a foundational reference for researchers developing movement classification systems for prosthetic limbs. His subsequent work rigorously examined the repeatability of grasp recognition — a critical but often overlooked dimension of prosthetic control robustness — and proposed a quantitative taxonomy of human hand grasps to better ground prosthetics research in measurable, reproducible science. Perhaps most innovatively, Cognolato pioneered a multimodal approach by integrating gaze tracking, computer vision, and inertial sensors alongside sEMG signals, demonstrated through datasets like Megane Pro. This forward-thinking paradigm, reflected in papers accumulating nearly 80 additional citations, has meaningfully expanded the toolkit available to engineers striving to restore functional independence for upper-limb amputees.
Research Focus
Key Achievements
Top Papers
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- 3A quantitative taxonomy of human hand grasps78 citations · 2019
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