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dc.contributor.authorCamps Sereix, Julià
dc.contributor.authorSamà Monsonís, Albert
dc.contributor.authorMartín Muñoz, Mario
dc.contributor.authorRodríguez Martín, Daniel Manuel
dc.contributor.authorPérez López, Carlos
dc.contributor.authorMoreno Aróstegui, Juan Manuel
dc.contributor.authorCabestany Moncusí, Joan
dc.contributor.authorCatalà Mallofré, Andreu
dc.contributor.authorAlcaine, Sheila
dc.contributor.authorMestre, Berta
dc.contributor.authorPrats, Anna
dc.contributor.authorCrespo, M. Cruz
dc.contributor.authorCounihan, Timothy
dc.contributor.authorBrowne, Patrick
dc.contributor.authorQuinlan, Leo R.
dc.contributor.authorÓLaighin, Gearóid
dc.contributor.authorSweeney, Dean
dc.contributor.authorLewy, Hadas
dc.contributor.authorVainstein, Gabriel
dc.contributor.authorCosta, Alberto
dc.contributor.authorAnnicchiarico, Roberta
dc.contributor.authorBayés, Àngels
dc.contributor.authorRodríguez Molinero, Alejandro
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2017-11-14T13:38:04Z
dc.date.available2020-01-01T01:25:49Z
dc.date.issued2017-10-16
dc.identifier.citationCamps, J., Sama, A., Martin, M., Rodriguez-Martin, D., Perez, C., Moreno, J., Cabestany, J., Catala, A., Alcaine, S., Mestre, B., Prats, A., Crespo, M. Cruz, Counihan, T., Browne, P., Quinlan, L., ÓLaighin, G., Sweeney, D., Lewy, H., Vainstein, G., Costa, A., Annicchiarico, R., Bayés, À., Rodríguez, A. Deep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit. "Knowledge-based systems", 16 Octubre 2017, vol. 139, p. 119-131.
dc.identifier.issn0950-7051
dc.identifier.urihttp://hdl.handle.net/2117/110570
dc.description.abstractAmong Parkinson’s disease (PD) motor symptoms, freezing of gait (FOG) may be the most incapacitating. FOG episodes may result in falls and reduce patients’ quality of life. Accurate assessment of FOG would provide objective information to neurologists about the patient’s condition and the symptom’s characteristics, while it could enable non-pharmacologic support based on rhythmic cues. This paper is, to the best of our knowledge, the first study to propose a deep learning method for detecting FOG episodes in PD patients. This model is trained using a novel spectral data representation strategy which considers information from both the previous and current signal windows. Our approach was evaluated using data collected by a waist-placed inertial measurement unit from 21 PD patients who manifested FOG episodes. These data were also employed to reproduce the state-of-the-art methodologies, which served to perform a comparative study to our FOG monitoring system. The results of this study demonstrate that our approach successfully outperforms the state-of-the-art methods for automatic FOG detection. Precisely, the deep learning model achieved 90% for the geometric mean between sensitivity and specificity, whereas the state-of-the-art methods were unable to surpass the 83% for the same metric.
dc.format.extent13 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshBiomedical engineering
dc.subject.lcshParkinson's disease
dc.subject.lcshPatient monitoring
dc.subject.otherDeep learning
dc.subject.otherSignal processing
dc.subject.otherFreezing of gait
dc.subject.otherParkinson’s disease
dc.subject.otherWearable device
dc.titleDeep learning for freezing of gait detection in Parkinson’s disease patients in their homes using a waist-worn inertial measurement unit
dc.typeArticle
dc.subject.lemacEnginyeria biomèdica
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacMonitoratge de pacients
dc.contributor.groupUniversitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma
dc.contributor.groupUniversitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic
dc.identifier.doi10.1016/j.knosys.2017.10.017
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.sciencedirect.com/science/article/pii/S0950705117304859
dc.rights.accessOpen Access
local.identifier.drac21589959
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/287677/EU/Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease/REMPARK
local.citation.authorCamps, J.; Sama, A.; Martin, M.; Rodriguez-Martin, D.; Perez, C.; Moreno, J.; Cabestany, J.; Catala, A.; Alcaine, S.; Mestre, B.; Prats, A.; Crespo, M. Cruz; Counihan, T.; Browne, P.; Quinlan, L.; ÓLaighin, G.; Sweeney, D.; Lewy, H.; Vainstein, G.; Costa, A.; Annicchiarico, R.; Bayés, À.; Rodríguez, A.
local.citation.publicationNameKnowledge-based systems
local.citation.volume139
local.citation.startingPage119
local.citation.endingPage131


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Attribution-NonCommercial-NoDerivs 3.0 Spain
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