Bradykinesia detection in Parkinson’s disease using smartwatches’ inertial sensors and deep learning methods
Autor(a) principal: | |
---|---|
Data de Publicação: | 2022 |
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/1822/83144 |
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. |
id |
RCAP_6dd94fe91830425ca758402dbc0b7824 |
---|---|
oai_identifier_str |
oai:repositorium.sdum.uminho.pt:1822/83144 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
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 |
_version_ |
1817545346416377856 |