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dc.contributor.CRUESPUNIVERSIDADE ESTADUAL DE CAMPINASpt_BR
dc.identifier.isbn978-1-7281-4569-3; 978-1-7281-4570-9pt_BR
dc.contributor.authorunicampZanini, Rafael Anicet-
dc.contributor.authorunicampColombini, Esther Luna-
dc.typeArtigopt_BR
dc.titleParkinson's disease EMG signal prediction using neural networkspt_BR
dc.contributor.authorZanini, R.A.-
dc.contributor.authorColombini, E.L.-
dc.contributor.authorDe Castro, M.C.F.-
dc.subjectDoença de Parkinsonpt_BR
dc.subjectRedes neurais recorrentespt_BR
dc.subject.otherlanguageParkinson diseasept_BR
dc.subject.otherlanguageRecurrent neural networkspt_BR
dc.description.abstractThis 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.pt_BR
dc.relation.ispartofIEEE international conference on systems, man, and cybernetics. Conference proceedingspt_BR
dc.publisher.cityNew York, NYpt_BR
dc.publisher.countryEstados Unidospt_BR
dc.publisherInstitute of Electrical and Electronics Engineerspt_BR
dc.date.issued2019-
dc.date.monthofcirculationNov.pt_BR
dc.language.isoengpt_BR
dc.description.firstpage2446pt_BR
dc.description.lastpage2453pt_BR
dc.rightsFechadopt_BR
dc.sourceScopuspt_BR
dc.identifier.issn1062-922Xpt_BR
dc.identifier.eissn2577-1655pt_BR
dc.identifier.doi10.1109/SMC.2019.8914553pt_BR
dc.identifier.urlhttps://ieeexplore.ieee.org/document/8914553pt_BR
dc.date.available2020-06-05T14:06:14Z-
dc.date.accessioned2020-06-05T14:06:14Z-
dc.description.provenanceSubmitted by Bruna Maria Campos da Cunha (bcampos@unicamp.br) on 2020-06-05T14:06:14Z No. of bitstreams: 0. Added 1 bitstream(s) on 2020-09-03T11:55:48Z : No. of bitstreams: 1 2-s2.0-85076792642.pdf: 617323 bytes, checksum: 709d1085f66e13d28b09403862a9644e (MD5)en
dc.description.provenanceMade available in DSpace on 2020-06-05T14:06:14Z (GMT). No. of bitstreams: 0 Previous issue date: 2019en
dc.identifier.urihttp://repositorio.unicamp.br/jspui/handle/REPOSIP/342756-
dc.description.conferencenomeIEEE International Conference on Systems, Man and Cyberneticspt_BR
dc.contributor.departmentSem informaçãopt_BR
dc.contributor.departmentDepartamento de Sistemas de Informaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.contributor.unidadeInstituto de Computaçãopt_BR
dc.subject.keywordForecastingpt_BR
dc.subject.keywordFunctional electric stimulationpt_BR
dc.subject.keywordMultilayer neural networkspt_BR
dc.subject.keywordNeurodegenerative diseasespt_BR
dc.subject.keywordSignal filtering and predictionpt_BR
dc.subject.keywordControl parameterspt_BR
dc.subject.keywordControl strategiespt_BR
dc.subject.keywordDifferent frequencypt_BR
dc.subject.keywordFunctional electrical stimulationpt_BR
dc.subject.keywordNeural network modelpt_BR
dc.subject.keywordRaw signalspt_BR
dc.identifier.source2-s2.0-85076792642pt_BR
dc.creator.orcidorcid.org/0000-0002-8981-6844pt_BR
dc.creator.orcidorcid.org/0000-0003-0467-3133pt_BR
dc.type.formArtigo de eventopt_BR
dc.identifier.articleid8914553pt_BR
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