Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/342756
Type: Artigo
Title: Parkinson's disease EMG signal prediction using neural networks
Author: Zanini, R.A.
Colombini, E.L.
De Castro, M.C.F.
Abstract: This paper proposes a comparison between different neural network models, using multilayer perceptron (MLPs) and recurrent neural network (RNN) models, for predicting Parkinson's disease electromyography (EMG) signals, to anticipate resulting resting tremor patterns. The experimental results indicate that the proposed models can adapt to different frequencies and amplitudes of tremor, and provide reasonable predictions for both EMG envelopes and EMG raw signals. Therefore, one could use these models as input for a control strategy for functional electrical stimulation (FES) devices used on tremor suppression, by dynamically predicting and improving FES control parameters based on tremor forecast.
Subject: Doença de Parkinson
Redes neurais recorrentes
Country: Estados Unidos
Editor: Institute of Electrical and Electronics Engineers
Rights: Fechado
Identifier DOI: 10.1109/SMC.2019.8914553
Address: https://ieeexplore.ieee.org/document/8914553
Date Issue: 2019
Appears in Collections:IC - Artigos e Outros Documentos

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