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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.authorAlcaine, Sheila
dc.contributor.authorMestre, Berta
dc.contributor.authorPrats, Anna
dc.contributor.authorCrespo, M. Cruz
dc.contributor.authorCabestany Moncusí, Joan
dc.contributor.authorBayés, Àngels
dc.contributor.authorCatalà Mallofré, Andreu
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.accessioned2018-02-05T07:51:00Z
dc.date.available2018-02-05T07:51:00Z
dc.date.issued2017
dc.identifier.citationCamps, J., Sama, A., Martin, M., Rodriguez-Martin, D., Perez, C., Alcaine, S., Mestre, B., Prats, A., Crespo, M. Cruz, Cabestany, J., Bayés, À., Catala, A. Deep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers. A: International Workshop on Artificial Neural Networks. "Advances in Computational Intelligence : 14th International Work-Conference on Artificial Neural Networks, IWANN 2017". Cadiz: 2017, p. 344-355.
dc.identifier.isbn978-3-319-59147-6
dc.identifier.urihttp://hdl.handle.net/2117/113689
dc.descriptionThe final publication is available at Springer via https://doi.org/10.1007/978-3-319-59147-6_30
dc.description.abstractFreezing of gait (FOG) is one of the most incapacitating symptoms among the motor alterations of Parkinson’s disease (PD). Manifesting FOG episodes reduce patients’ quality of life and their autonomy to perform daily living activities, while it may provoke falls. Accurate ambulatory FOG assessment would enable non-pharmacologic support based on cues and would provide relevant information to neurologists on the disease evolution. This paper presents a method for FOG detection based on deep learning and signal processing techniques. This is, to the best of our knowledge, the first time that FOG detection is addressed with deep learning. The evaluation of the model has been done based on the data from 15 PD patients who manifested FOG. An inertial measurement unit placed at the left side of the waist recorded tri-axial accelerometer, gyroscope and magnetometer signals. Our approach achieved comparable results to the state-of-the-art, reaching validation performances of 88.6% and 78% for sensitivity and specificity respectively.
dc.format.extent12 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial
dc.subjectÀrees temàtiques de la UPC::Informàtica::Automàtica i control
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshParkinson's disease
dc.subject.lcshSignal processing
dc.subject.otherFreezing of gait
dc.subject.otherParkinson’s disease
dc.subject.otherDeep learning
dc.subject.otherSignal processing
dc.subject.otherInertial measurement unit
dc.titleDeep learning for detecting freezing of gait episodes in Parkinson’s disease based on accelerometers
dc.typeConference report
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacTractament del senyal
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.doi10.1007/978-3-319-59147-6_30
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttps://link.springer.com/chapter/10.1007/978-3-319-59147-6_30
dc.rights.accessOpen Access
local.identifier.drac21871880
dc.description.versionPostprint (author's final draft)
local.citation.authorCamps, J.; Sama, A.; Martin, M.; Rodriguez-Martin, D.; Perez, C.; Alcaine, S.; Mestre, B.; Prats, A.; Crespo, M. Cruz; Cabestany, J.; Bayés, À.; Catala, A.
local.citation.contributorInternational Workshop on Artificial Neural Networks
local.citation.pubplaceCadiz
local.citation.publicationNameAdvances in Computational Intelligence : 14th International Work-Conference on Artificial Neural Networks, IWANN 2017
local.citation.startingPage344
local.citation.endingPage355


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