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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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Bibliografski detalji
Glavni autori: Hussein Naser, Hashim A. Hashim
Format: Artigo
Jezik:Inglês
Izdano: Elsevier 2024-12-01
Serija:Systems and Soft Computing
Teme:
Online pristup:http://www.sciencedirect.com/science/article/pii/S2772941924000735
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