Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods

Detalhes bibliográficos
Autor(a) principal: Sigcha, Luis
Data de Publicação: 2022
Outros Autores: Domínguez, Beatriz, Borzì, Luigi, Costa, Nélson Bruno Martins Marques da, Costa, Susana Raquel Pinto, Arezes, P., López, Juan Manuel, De Arcas, Guillermo, Pavón, Ignacio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: https://hdl.handle.net/1822/83144
Resumo: Bradykinesia is the defining motor symptom of Parkinson’s disease (PD) and is reflected as a progressive reduction in speed and range of motion. The evaluation of bradykinesia severity is important for assessing disease progression, daily motor fluctuations, and therapy response. However, the clinical evaluation of PD motor signs is affected by subjectivity, leading to intra- and inter-rater variability. Moreover, the clinical assessment is performed a few times a year during pre-scheduled follow-up visits. To overcome these limitations, objective and unobtrusive methods based on wearable motion sensors and machine learning (ML) have been proposed, providing promising results. In this study, the combination of inertial sensors embedded in consumer smartwatches and different ML models is exploited to detect bradykinesia in the upper extremities and evaluate its severity. Six PD subjects and seven age-matched healthy controls were equipped with a consumer smartwatch and asked to perform a set of motor exercises for at least 6 weeks. Different feature sets, data representations, data augmentation methods, and ML models were implemented and combined. Data recorded from smartwatches’ motion sensors, properly augmented and fed to a combination of Convolutional Neural Network and Random Forest model, provided the best results, with an accuracy of 0.86 and an area under the curve (AUC) of 0.94. Results suggest that the combination of consumer smartwatches and ML classification methods represents an unobtrusive solution for the detection of bradykinesia and the evaluation of its severity.
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spelling Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methodsParkinson’s diseaseBradykinesiaWearablesInertial sensorsArtificial intelligenceDeep learningScience & TechnologyBradykinesia is the defining motor symptom of Parkinson’s disease (PD) and is reflected as a progressive reduction in speed and range of motion. The evaluation of bradykinesia severity is important for assessing disease progression, daily motor fluctuations, and therapy response. However, the clinical evaluation of PD motor signs is affected by subjectivity, leading to intra- and inter-rater variability. Moreover, the clinical assessment is performed a few times a year during pre-scheduled follow-up visits. To overcome these limitations, objective and unobtrusive methods based on wearable motion sensors and machine learning (ML) have been proposed, providing promising results. In this study, the combination of inertial sensors embedded in consumer smartwatches and different ML models is exploited to detect bradykinesia in the upper extremities and evaluate its severity. Six PD subjects and seven age-matched healthy controls were equipped with a consumer smartwatch and asked to perform a set of motor exercises for at least 6 weeks. Different feature sets, data representations, data augmentation methods, and ML models were implemented and combined. Data recorded from smartwatches’ motion sensors, properly augmented and fed to a combination of Convolutional Neural Network and Random Forest model, provided the best results, with an accuracy of 0.86 and an area under the curve (AUC) of 0.94. Results suggest that the combination of consumer smartwatches and ML classification methods represents an unobtrusive solution for the detection of bradykinesia and the evaluation of its severity.Part of this research was funded by the project “Tecnologías Capacitadoras para la Asistencia, Seguimiento y Rehabilitación de Pacientes con Enfermedad de Parkinson”. Centro Internacional sobre el envejecimiento, CENIE (código 0348_CIE_6_E) Interreg V-A España-Portugal (POCTEP); and (2) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020.Multidisciplinary Digital Publishing Institute (MDPI)Universidade do MinhoSigcha, LuisDomínguez, BeatrizBorzì, LuigiCosta, Nélson Bruno Martins Marques daCosta, Susana Raquel PintoArezes, P.López, Juan ManuelDe Arcas, GuillermoPavón, Ignacio2022-11-242022-11-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/1822/83144engSigcha, L.; Domínguez, B.; Borzì, L.; Costa, N.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G.; Pavón, I. Bradykinesia Detection in Parkinson’s Disease Using Smartwatches’ Inertial Sensors and Deep Learning Methods. Electronics 2022, 11, 3879. https://doi.org/10.3390/electronics112338792079-929210.3390/electronics112338793879https://www.mdpi.com/2079-9292/11/23/3879info: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:RCAAP2024-05-11T07:31:22Zoai:repositorium.sdum.uminho.pt:1822/83144Portal AgregadorONGhttps://www.rcaap.pt/oai/openairemluisa.alvim@gmail.comopendoar:71602024-05-11T07:31:22Repositório 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 Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
title Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
spellingShingle Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
Sigcha, Luis
Parkinson’s disease
Bradykinesia
Wearables
Inertial sensors
Artificial intelligence
Deep learning
Science & Technology
title_short Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
title_full Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
title_fullStr Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
title_full_unstemmed Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
title_sort Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
author Sigcha, Luis
author_facet Sigcha, Luis
Domínguez, Beatriz
Borzì, Luigi
Costa, Nélson Bruno Martins Marques da
Costa, Susana Raquel Pinto
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
Pavón, Ignacio
author_role author
author2 Domínguez, Beatriz
Borzì, Luigi
Costa, Nélson Bruno Martins Marques da
Costa, Susana Raquel Pinto
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
Pavón, Ignacio
author2_role author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Sigcha, Luis
Domínguez, Beatriz
Borzì, Luigi
Costa, Nélson Bruno Martins Marques da
Costa, Susana Raquel Pinto
Arezes, P.
López, Juan Manuel
De Arcas, Guillermo
Pavón, Ignacio
dc.subject.por.fl_str_mv Parkinson’s disease
Bradykinesia
Wearables
Inertial sensors
Artificial intelligence
Deep learning
Science & Technology
topic Parkinson’s disease
Bradykinesia
Wearables
Inertial sensors
Artificial intelligence
Deep learning
Science & Technology
description Bradykinesia is the defining motor symptom of Parkinson’s disease (PD) and is reflected as a progressive reduction in speed and range of motion. The evaluation of bradykinesia severity is important for assessing disease progression, daily motor fluctuations, and therapy response. However, the clinical evaluation of PD motor signs is affected by subjectivity, leading to intra- and inter-rater variability. Moreover, the clinical assessment is performed a few times a year during pre-scheduled follow-up visits. To overcome these limitations, objective and unobtrusive methods based on wearable motion sensors and machine learning (ML) have been proposed, providing promising results. In this study, the combination of inertial sensors embedded in consumer smartwatches and different ML models is exploited to detect bradykinesia in the upper extremities and evaluate its severity. Six PD subjects and seven age-matched healthy controls were equipped with a consumer smartwatch and asked to perform a set of motor exercises for at least 6 weeks. Different feature sets, data representations, data augmentation methods, and ML models were implemented and combined. Data recorded from smartwatches’ motion sensors, properly augmented and fed to a combination of Convolutional Neural Network and Random Forest model, provided the best results, with an accuracy of 0.86 and an area under the curve (AUC) of 0.94. Results suggest that the combination of consumer smartwatches and ML classification methods represents an unobtrusive solution for the detection of bradykinesia and the evaluation of its severity.
publishDate 2022
dc.date.none.fl_str_mv 2022-11-24
2022-11-24T00:00:00Z
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/1822/83144
url https://hdl.handle.net/1822/83144
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Sigcha, L.; Domínguez, B.; Borzì, L.; Costa, N.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G.; Pavón, I. Bradykinesia Detection in Parkinson’s Disease Using Smartwatches’ Inertial Sensors and Deep Learning Methods. Electronics 2022, 11, 3879. https://doi.org/10.3390/electronics11233879
2079-9292
10.3390/electronics11233879
3879
https://www.mdpi.com/2079-9292/11/23/3879
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 (MDPI)
publisher.none.fl_str_mv Multidisciplinary Digital Publishing Institute (MDPI)
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
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection 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
repository.mail.fl_str_mv mluisa.alvim@gmail.com
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