Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands-Reference-Cited by-同舟云学术

Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

Published:2016-09-07 Issue: Volume:10 Page:
ISSN:1662-5218
Container-title:Frontiers in Neurorobotics
language:
Short-container-title:Front. Neurorobot.

Author:

Atzori Manfredo,Cognolato Matteo,Müller Henning

Funder

Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung

Publisher

Frontiers Media SA

Subject

Artificial Intelligence,Biomedical Engineering

Reference65 articles.

1. Natural control capabilities of robotic hands by hand amputated subjects;Atzori,2014a

2. Electromyography data for non-invasive naturally-controlled robotic hand prostheses;Atzori;Sci. Data,2014b

3. Classification of hand movements in amputated subjects by sEMG and accelerometers;Atzori,2014c

4. Effect of clinical parameters on the control of myoelectric robotic prosthetic hands;Atzori;J. Rehabil. Res. Dev.,2016

5. Characterization of a benchmark database for myoelectric movement classification;Atzori;IEEE Trans. Neural Syst. Rehabil. Eng.,2015

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