Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks

Detalhes bibliográficos
Autor(a) principal: Sigcha, Luis
Data de Publicação: 2021
Outros Autores: Pavón, Ignacio, Costa, Nélson, Costa, Susana, Gago, Miguel, Arezes, P., López, Juan Manuel, De Arcas, Guillermo
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/72038
Resumo: Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
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spelling Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networksmachine learningwearable sensorsresting tremordeep learningconvolutional neural networksParkinson's diseasemultitaskScience & TechnologyResting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.This research was funded by the following projects: (1) "Tecnologias Capacitadoras para la Asistencia, Seguimiento y Rehabilitacion de Pacientes con Enfermedad de Parkinson". Centro Internacional sobre el envejecimiento, CENIE (codigo 0348_CIE_6_E) Interreg V-A Espana-Portugal (POCTEP). (2) Ecuadorian Government Granth "Becas internacionales de posgrado 2019" of the Secretaria de Educacion Superior, Ciencia, Tecnologia e Innovacion (SENESCYT), received by the author Luis Sigcha.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoSigcha, LuisPavón, IgnacioCosta, NélsonCosta, SusanaGago, MiguelArezes, P.López, Juan ManuelDe Arcas, Guillermo20212021-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/72038engSigcha, L.; Pavón, I.; Costa, N.; Costa, S.; Gago, M.; Arezes, P.; López, J.M.; De Arcas, G. Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 2021, 21, 291. https://doi.org/10.3390/s210102911424-82201424-822010.3390/s2101029133406692https://www.mdpi.com/1424-8220/21/1/291info: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:RCAAP2023-07-21T11:58:33Zoai:repositorium.sdum.uminho.pt:1822/72038Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:48:17.567823Repositório 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 Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
title Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
spellingShingle Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
Sigcha, Luis
machine learning
wearable sensors
resting tremor
deep learning
convolutional neural networks
Parkinson's disease
multitask
Science & Technology
title_short Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
title_full Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
title_fullStr Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
title_full_unstemmed Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
title_sort Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
author Sigcha, Luis
author_facet Sigcha, Luis
Pavón, Ignacio
Costa, Nélson
Costa, Susana
Gago, Miguel
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
author_role author
author2 Pavón, Ignacio
Costa, Nélson
Costa, Susana
Gago, Miguel
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
author2_role author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Sigcha, Luis
Pavón, Ignacio
Costa, Nélson
Costa, Susana
Gago, Miguel
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
dc.subject.por.fl_str_mv machine learning
wearable sensors
resting tremor
deep learning
convolutional neural networks
Parkinson's disease
multitask
Science & Technology
topic machine learning
wearable sensors
resting tremor
deep learning
convolutional neural networks
Parkinson's disease
multitask
Science & Technology
description Resting tremor in Parkinson’s disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients’ quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients’ daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients’ daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-01-01T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://hdl.handle.net/1822/72038
url https://hdl.handle.net/1822/72038
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Sigcha, L.; Pavón, I.; Costa, N.; Costa, S.; Gago, M.; Arezes, P.; López, J.M.; De Arcas, G. Automatic Resting Tremor Assessment in Parkinson’s Disease Using Smartwatches and Multitask Convolutional Neural Networks. Sensors 2021, 21, 291. https://doi.org/10.3390/s21010291
1424-8220
1424-8220
10.3390/s21010291
33406692
https://www.mdpi.com/1424-8220/21/1/291
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.publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute
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
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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