Automatic resting tremor assessment in Parkinson’s disease using smartwatches and multitask convolutional neural networks
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , , , , , |
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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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 |
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Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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RCAAP |
institution |
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) |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799132244186497024 |