Estimating Parkinson’s Disease Stages from Accelerometer Data
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10451/57677 |
Resumo: | Tese de mestrado, Engenharia Informática (Engenharia de Software), 2022, Universidade de Lisboa, Faculdade de Ciências |
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Estimating Parkinson’s Disease Stages from Accelerometer DataDoença de ParkinsonMarchaMachine LearningTeses de mestrado - 2022Departamento de InformáticaTese de mestrado, Engenharia Informática (Engenharia de Software), 2022, Universidade de Lisboa, Faculdade de CiênciasCurrent practices for monitoring disease stage and progression in Parkinson’s Disease still rely on periodic clinical visitations that can be cumbersome and highly stressful for patients and caretakers. Furthermore, the current gold standard for these evaluations is still reliant on observations made by trained clinicians using clinical scales that, in spite of their repeatedly verified validity, are subject to fluctuations due to intra or inter-rater variability. Over the last decade, technological developments in sensors for data collection and data science algorithms have enabled systems and tools for health and tele-medicine applications, along with a battery of research into the objective and continuous monitoring of Parkinson’s Disease. Among such research, gait and it’s characteristics have emerged as reliable markers for the progression of PD. As such, studies leveraging these characteristics for the objective monitoring of the disease have become a common trend in the related literature. A limiting factor in several of these studies is the use of scores and outcomes that, in spite of their high correlation to established clinical scales, are different to those usually used by clinicians, making them harder to interpret and adopt for clinical use. To bridge this gap, several studies have attempted to classify or estimate specific parts of the MDS-UPDRS, the most clinically used scale for the assessment of PD. Recently, the automatic estimation of scores for part 3 of the MDS-UPDRS, which focuses the severity of motor symptoms, have leveraged the use of deep learning techniques for this purpose, with promising results. This work presents a comparison of traditional feature engineered models against those current state of the art deep learning approaches for the prediction of this score. Furthermore, an analysis on different approaches to data collection, feature extraction and model parametrization was also performed, to assess the effect of these different variables on the estimation task. Finally, an analysis of the best configurations of machine learning pipelines for this purpose was also performed to direct further studies. While the optimal models in this study failed to match the performance of those state of the art approaches, the identified limitations and recommendations for future research form a solid foundation for future work on this topic.Guerreiro, TiagoRepositório da Universidade de LisboaLobo, Vitor Marques2023-05-30T10:50:03Z202220222022-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10451/57677enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-08T17:06:29Zoai:repositorio.ul.pt:10451/57677Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:08:13.469857Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
title |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
spellingShingle |
Estimating Parkinson’s Disease Stages from Accelerometer Data Lobo, Vitor Marques Doença de Parkinson Marcha Machine Learning Teses de mestrado - 2022 Departamento de Informática |
title_short |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
title_full |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
title_fullStr |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
title_full_unstemmed |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
title_sort |
Estimating Parkinson’s Disease Stages from Accelerometer Data |
author |
Lobo, Vitor Marques |
author_facet |
Lobo, Vitor Marques |
author_role |
author |
dc.contributor.none.fl_str_mv |
Guerreiro, Tiago Repositório da Universidade de Lisboa |
dc.contributor.author.fl_str_mv |
Lobo, Vitor Marques |
dc.subject.por.fl_str_mv |
Doença de Parkinson Marcha Machine Learning Teses de mestrado - 2022 Departamento de Informática |
topic |
Doença de Parkinson Marcha Machine Learning Teses de mestrado - 2022 Departamento de Informática |
description |
Tese de mestrado, Engenharia Informática (Engenharia de Software), 2022, Universidade de Lisboa, Faculdade de Ciências |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022 2022 2022-01-01T00:00:00Z 2023-05-30T10:50:03Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10451/57677 |
url |
http://hdl.handle.net/10451/57677 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
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RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134636402540544 |