sEMG-based hand gestures classification using a semi-supervised multi-layer neural networks with Autoencoder

This work presents a semi-supervised multilayer neural network (MLNN) with an Autoencoder to develop a classification model for recognizing hand gestures from electromyographic (EMG) signals. Using a Myo armband equipped with eight non-invasive surface-mounted biosensors, raw surface EMG (sEMG) sens...

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Detalles Bibliográficos

Autores principales:	Hussein Naser, Hashim A. Hashim
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2024-12-01
Colección:	Systems and Soft Computing
Materias:	Electromyographic signals Neural Networks Classification Myo armband Autoencoder
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2772941924000735
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