Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/1822/64876 |
Resumo: | Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system). |
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Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensorsIMUaccelerometerconvolutional neural networksLSTMconsecutive windowsdenoising autoencodertime distributedspectral representationScience & TechnologyFreezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).This work has been supported by: (1) FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. (2) Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2). ETSI Industriales. Universidad Politécnica de Madrid. The authors would like to thank Technical Research Centre for Dependency Care and Autonomous Living (CETpD) for sharing the data of freezing of gait.Multidisciplinary Digital Publishing InstituteUniversidade do MinhoSigcha, LuisCosta, NélsonPavón, IgnacioCosta, Susana Raquel PintoArezes, P.López, Juan ManuelDe Arcas, Guillermo2020-03-292020-03-29T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/1822/64876engSigcha, L.; Costa, N.; Pavón, I.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors. Sensors 2020, 20, 1895.1424-82201424-822010.3390/s2007189532235373https://www.mdpi.com/1424-8220/20/7/1895info: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:RCAAP2023-07-21T12:45:02Zoai:repositorium.sdum.uminho.pt:1822/64876Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T19:42:49.706716Repositório 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 |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
title |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
spellingShingle |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors Sigcha, Luis IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation Science & Technology |
title_short |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
title_full |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
title_fullStr |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
title_full_unstemmed |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
title_sort |
Deep learning approaches for detecting freezing of gait in Parkinson’s disease patients through on-body acceleration sensors |
author |
Sigcha, Luis |
author_facet |
Sigcha, Luis Costa, Nélson Pavón, Ignacio Costa, Susana Raquel Pinto Arezes, P. López, Juan Manuel De Arcas, Guillermo |
author_role |
author |
author2 |
Costa, Nélson Pavón, Ignacio Costa, Susana Raquel Pinto Arezes, P. López, Juan Manuel De Arcas, Guillermo |
author2_role |
author author author author author author |
dc.contributor.none.fl_str_mv |
Universidade do Minho |
dc.contributor.author.fl_str_mv |
Sigcha, Luis Costa, Nélson Pavón, Ignacio Costa, Susana Raquel Pinto Arezes, P. López, Juan Manuel De Arcas, Guillermo |
dc.subject.por.fl_str_mv |
IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation Science & Technology |
topic |
IMU accelerometer convolutional neural networks LSTM consecutive windows denoising autoencoder time distributed spectral representation Science & Technology |
description |
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson’s disease (PD). The occurrence of FOG reduces the patients’ quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms’ evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients’ homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system). |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-03-29 2020-03-29T00:00:00Z |
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 |
http://hdl.handle.net/1822/64876 |
url |
http://hdl.handle.net/1822/64876 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Sigcha, L.; Costa, N.; Pavón, I.; Costa, S.; Arezes, P.; López, J.M.; De Arcas, G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson’s Disease Patients through On-Body Acceleration Sensors. Sensors 2020, 20, 1895. 1424-8220 1424-8220 10.3390/s20071895 32235373 https://www.mdpi.com/1424-8220/20/7/1895 |
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 |
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 |
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1799132983047749632 |