publication . Article . Other literature type . 2016

Deep learning with convolutional neural networks: a resource for the control of robotic prosthetic hands via electromyography

Manfredo Atzori; Matteo Cognolato; Henning Müller;
Open Access English
  • Published: 07 Sep 2016 Journal: Frontiers in Neurorobotics, volume 10 (issn: 1662-5218, Copyright policy)
  • Publisher: Frontiers Media S.A.
  • Country: Switzerland
Motivation: Natural control methods based on surface electromyography and pattern recognition are promising for hand prosthetics. However, the control robustness offered by scientific research is still not sufficient for many real life applications and commercial prostheses are in the best case capable to offer natural control for only a few movements. Objective: In recent years deep learning revolutionized several fields of machine learning, including computer vision and speech recognition. Our objective is to test its capabilities for the natural control of robotic hands via surface electromyography by providing a baseline on a large number of intact and amput...
Persistent Identifiers
free text keywords: Electromyography, machine learning, prosthetics, deep learning, rehabilitation robotics, Convolutional Neural Networks, Informatique, Neuroscience, Original Research, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, lcsh:RC321-571, Artificial intelligence, business.industry, business, medicine.diagnostic_test, medicine, Convolutional neural network, Computer science
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