Objective assessment of motor symptoms of Parkinson's disease through non-contact sensors

Detalhes bibliográficos
Ano de defesa: 2020
Autor(a) principal: Oliveira, Fábio Henrique Monteiro
Orientador(a): Não Informado pela instituição
Banca de defesa: Não Informado pela instituição
Tipo de documento: Tese
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal de Uberlândia
Brasil
Programa de Pós-graduação em Engenharia Elétrica
Programa de Pós-Graduação: Não Informado pela instituição
Departamento: Não Informado pela instituição
País: Não Informado pela instituição
Palavras-chave em Português:
Link de acesso: 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.