Parkinson's disease EMG signal prediction using neural networks

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dc.contributor.authorZANINI, R. A.
dc.contributor.authorCOLOMBINI, E. L.
dc.contributor.authorCastro, M.C.F.
dc.contributor.authorOrcidhttps://orcid.org/0000-0002-2751-0014
dc.date.accessioned2022-01-12T21:56:09Z
dc.date.available2022-01-12T21:56:09Z
dc.date.issued2019-10-05
dc.description.abstract© 2019 IEEE.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.
dc.description.firstpage2446
dc.description.lastpage2453
dc.description.volume2019-October
dc.identifier.citationZANINI, R. A.; COLOMBINI, E. L.; CASTRO, M. C. F. Parkinson's disease EMG signal prediction using neural networks. Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics, p. 2446 - 2453, oct. 2019.
dc.identifier.doi10.1109/SMC.2019.8914553
dc.identifier.urihttps://repositorio.fei.edu.br/handle/FEI/3705
dc.relation.ispartofConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
dc.rightsAcesso Restrito
dc.titleParkinson's disease EMG signal prediction using neural networks
dc.typeArtigo de evento
fei.scopus.citations15
fei.scopus.eid2-s2.0-85076792642
fei.scopus.subjectControl parameters
fei.scopus.subjectControl strategies
fei.scopus.subjectDifferent frequency
fei.scopus.subjectFunctional electrical stimulation
fei.scopus.subjectNeural network model
fei.scopus.subjectParkinson's disease
fei.scopus.subjectRaw signals
fei.scopus.subjectRecurrent neural network (RNN)
fei.scopus.updated2025-02-01
fei.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076792642&origin=inward
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