Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Tipo de documento: | Tese |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFU |
Texto Completo: | https://repositorio.ufu.br/handle/123456789/29174 http://doi.org/10.14393/ufu.te.2020.393 |
Resumo: | The diagnosis and evaluation of the severity of Parkinson's disease (PD) is a task that has been performed through clinical evaluation and use of subjective scales. Over the years several studies have reported results and technologies with the purpose of making the characterization of PD more objective. In this perspective, we have identified the possibility of using non-contact capacitive sensors to record the motor activity of the hand and wrist. Another identified challenge is related to the quantification of the severity of motor symptoms of PD. In this study, we present the use of an innovative tool, t-Distributed Stochastic Neighbor Embedding (t-SNE), for the reduction and visualization of information. The use of this tool allowed the visualization of data in a two-dimensional space and an improvement of the performance of classifiers responsible for estimating the severity of the disease. In order to evaluate the use of capacitive sensors and signal processing tools, data from neurologically healthy individuals and people with PD were collected. In the end, our contributions are the following: (i) development and evaluation of a technology for recording motor signals of hand and wrist activities, based on capacitive contactless sensors; (ii) comparative evaluation among several tools for signal processing, in order to objectively evaluate the motor symptoms of PD. |
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Objective assessment of motor symptoms of Parkinson's disease through non-contact sensorsAvaliação objetiva dos sintomas motores da doença de Parkinson por meio de sensores sem contatoParkinson's diseaseNon-contact capacitive sensorInertial sensorHand motor taskCNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICACNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAOCNPQ::ENGENHARIAS::ENGENHARIA ELETRICAThe diagnosis and evaluation of the severity of Parkinson's disease (PD) is a task that has been performed through clinical evaluation and use of subjective scales. Over the years several studies have reported results and technologies with the purpose of making the characterization of PD more objective. In this perspective, we have identified the possibility of using non-contact capacitive sensors to record the motor activity of the hand and wrist. Another identified challenge is related to the quantification of the severity of motor symptoms of PD. In this study, we present the use of an innovative tool, t-Distributed Stochastic Neighbor Embedding (t-SNE), for the reduction and visualization of information. The use of this tool allowed the visualization of data in a two-dimensional space and an improvement of the performance of classifiers responsible for estimating the severity of the disease. In order to evaluate the use of capacitive sensors and signal processing tools, data from neurologically healthy individuals and people with PD were collected. In the end, our contributions are the following: (i) development and evaluation of a technology for recording motor signals of hand and wrist activities, based on capacitive contactless sensors; (ii) comparative evaluation among several tools for signal processing, in order to objectively evaluate the motor symptoms of PD.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível SuperiorCNPq - Conselho Nacional de Desenvolvimento Científico e TecnológicoFAPEMIG - Fundação de Amparo a Pesquisa do Estado de Minas GeraisTese (Doutorado)O diagnóstico e avaliação da gravidade da doença de Parkinson (DP) é uma tarefa que tem sido realizada por meio de avaliação clínica e uso de escalas subjetivas. Ao longo dos anos, vários estudos relataram resultados e tecnologias com o objetivo de tornar a caracterização da DP mais objetiva. Nesta perspectiva, identificamos a possibilidade de usar sensores capacitivos sem contato para registrar a atividade motora da mão e punho. Outro desafio identificado está relacionado à quantificação da gravidade dos sintomas motores da DP. Neste estudo, apresentamos o uso de uma ferramenta inovadora, Embutimento Estocástico de Vizinho Distribuído (t-PND), para redução e visualização de informações. O uso dessa ferramenta permitiu a visualização de dados em um espaço bidimensional e uma melhora no desempenho dos classificadores responsáveis por estimar a gravidade da doença. Para avaliar o uso de sensores capacitivos e ferramentas de processamento de sinais, foram coletados dados de indivíduos neurologicamente saudáveis e pessoas com DP. No final, nossas contribuições são as seguintes: (i) desenvolvimento e avaliação de uma tecnologia para registrar sinais motores de atividades de mãos e pulsos, com base em sensores capacitivos sem contato; (ii) avaliação comparativa entre várias ferramentas para processamento de sinais, a fim de avaliar objetivamente os sintomas motores da DP.Universidade Federal de UberlândiaBrasilPrograma de Pós-graduação em Engenharia ElétricaAndrade, Adriano de Oliveirahttp://lattes.cnpq.br/1229329519982110Peretta, Igor Santoshttp://lattes.cnpq.br/6826511824160198Pereira, Adriano Alveshttp://lattes.cnpq.br/7340105957340705Peres, André Salles Cunhahttp://lattes.cnpq.br/8282650074944458Vieira, Marcus Fragahttp://lattes.cnpq.br/4153462617460766Oliveira, Fábio Henrique Monteiro2020-04-13T12:50:37Z2020-04-13T12:50:37Z2020-04-03info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdfOLIVEIRA, Fábio Henrique Monteiro. Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors. 2020. 88 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2020. DOI http://doi.org/10.14393/ufu.te.2020.393.https://repositorio.ufu.br/handle/123456789/29174http://doi.org/10.14393/ufu.te.2020.393enghttp://creativecommons.org/licenses/by-nc-nd/3.0/us/info:eu-repo/semantics/openAccessreponame:Repositório Institucional da UFUinstname:Universidade Federal de Uberlândia (UFU)instacron:UFU2020-04-14T06:12:24Zoai:repositorio.ufu.br:123456789/29174Repositório InstitucionalONGhttp://repositorio.ufu.br/oai/requestdiinf@dirbi.ufu.bropendoar:2020-04-14T06:12:24Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU)false |
dc.title.none.fl_str_mv |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors Avaliação objetiva dos sintomas motores da doença de Parkinson por meio de sensores sem contato |
title |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
spellingShingle |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors Oliveira, Fábio Henrique Monteiro Parkinson's disease Non-contact capacitive sensor Inertial sensor Hand motor task CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
title_short |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
title_full |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
title_fullStr |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
title_full_unstemmed |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
title_sort |
Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors |
author |
Oliveira, Fábio Henrique Monteiro |
author_facet |
Oliveira, Fábio Henrique Monteiro |
author_role |
author |
dc.contributor.none.fl_str_mv |
Andrade, Adriano de Oliveira http://lattes.cnpq.br/1229329519982110 Peretta, Igor Santos http://lattes.cnpq.br/6826511824160198 Pereira, Adriano Alves http://lattes.cnpq.br/7340105957340705 Peres, André Salles Cunha http://lattes.cnpq.br/8282650074944458 Vieira, Marcus Fraga http://lattes.cnpq.br/4153462617460766 |
dc.contributor.author.fl_str_mv |
Oliveira, Fábio Henrique Monteiro |
dc.subject.por.fl_str_mv |
Parkinson's disease Non-contact capacitive sensor Inertial sensor Hand motor task CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
topic |
Parkinson's disease Non-contact capacitive sensor Inertial sensor Hand motor task CNPQ::ENGENHARIAS::ENGENHARIA BIOMEDICA CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA |
description |
The diagnosis and evaluation of the severity of Parkinson's disease (PD) is a task that has been performed through clinical evaluation and use of subjective scales. Over the years several studies have reported results and technologies with the purpose of making the characterization of PD more objective. In this perspective, we have identified the possibility of using non-contact capacitive sensors to record the motor activity of the hand and wrist. Another identified challenge is related to the quantification of the severity of motor symptoms of PD. In this study, we present the use of an innovative tool, t-Distributed Stochastic Neighbor Embedding (t-SNE), for the reduction and visualization of information. The use of this tool allowed the visualization of data in a two-dimensional space and an improvement of the performance of classifiers responsible for estimating the severity of the disease. In order to evaluate the use of capacitive sensors and signal processing tools, data from neurologically healthy individuals and people with PD were collected. In the end, our contributions are the following: (i) development and evaluation of a technology for recording motor signals of hand and wrist activities, based on capacitive contactless sensors; (ii) comparative evaluation among several tools for signal processing, in order to objectively evaluate the motor symptoms of PD. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-04-13T12:50:37Z 2020-04-13T12:50:37Z 2020-04-03 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
OLIVEIRA, Fábio Henrique Monteiro. Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors. 2020. 88 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2020. DOI http://doi.org/10.14393/ufu.te.2020.393. https://repositorio.ufu.br/handle/123456789/29174 http://doi.org/10.14393/ufu.te.2020.393 |
identifier_str_mv |
OLIVEIRA, Fábio Henrique Monteiro. Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors. 2020. 88 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Federal de Uberlândia, Uberlândia, 2020. DOI http://doi.org/10.14393/ufu.te.2020.393. |
url |
https://repositorio.ufu.br/handle/123456789/29174 http://doi.org/10.14393/ufu.te.2020.393 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/us/ info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
http://creativecommons.org/licenses/by-nc-nd/3.0/us/ |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Engenharia Elétrica |
publisher.none.fl_str_mv |
Universidade Federal de Uberlândia Brasil Programa de Pós-graduação em Engenharia Elétrica |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFU instname:Universidade Federal de Uberlândia (UFU) instacron:UFU |
instname_str |
Universidade Federal de Uberlândia (UFU) |
instacron_str |
UFU |
institution |
UFU |
reponame_str |
Repositório Institucional da UFU |
collection |
Repositório Institucional da UFU |
repository.name.fl_str_mv |
Repositório Institucional da UFU - Universidade Federal de Uberlândia (UFU) |
repository.mail.fl_str_mv |
diinf@dirbi.ufu.br |
_version_ |
1805569615468691456 |