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dc.contributor.authorRodríguez Martín, Daniel Manuel
dc.contributor.authorPérez López, Carlos
dc.contributor.authorSamà Monsonís, Albert
dc.contributor.authorCatalà Mallofré, Andreu
dc.contributor.authorMoreno Aróstegui, Juan Manuel
dc.contributor.authorCabestany Moncusí, Joan
dc.contributor.authorMestre, Berta
dc.contributor.authorAlcaine, Sheila
dc.contributor.authorPrats, Anna
dc.contributor.authorCruz Crespo, Mari
dc.contributor.authorBayés, Àngels
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria Electrònica
dc.date.accessioned2017-05-23T09:03:20Z
dc.date.available2017-05-23T09:03:20Z
dc.date.issued2017-04-11
dc.identifier.citationRodriguez-Martin, D., Perez, C., Sama, A., Catala, A., Moreno, J., Cabestany, J., Mestre, B., Alcaine, S., Prats, A., Cruz, M., Bayés, À. A waist-worn inertial measurement unit for Parkinson’s disease long-term monitoring. "Sensors", 11 Abril 2017, vol. 17, núm. 4, p. 1-28.
dc.identifier.issn1424-8220
dc.identifier.urihttp://hdl.handle.net/2117/104742
dc.description.abstractInertial measurement units (IMUs) are devices used, among other fields, in health applications, since they are light, small and effective. More concretely, IMUs have demonstrated to accurately monitor motor symptoms of Parkinson’s disease (PD). In this sense, most of previous works have attempted to assess PD symptoms through IMUs in controlled environments or short tests. This paper presents the design of an IMU called 9x3 that aims to assess PD symptoms, enabling the possibility to perform a map of patients’ symptoms at their homes during long periods of time. The designed device is able to acquire and store raw inertial data for artificial intelligence algorithmic training purposes. Furthermore, the presented IMU also enables the real-time execution of the developed and embedded learning models. Results show the great flexibility of the 9x3, capable of storing inertial information and algorithm outputs, sending messages to external devices. This paper also presents the results of detecting freezing of gait and brad kinetic gait in 12 patients, with sensitivity and specificity above 80%. Additionally, the system enables working 23.09 days (at waking hours) with a 1200mAh battery sampling at 50 Hz, opening up the possibility to be employed at other applications like wellbeing and sports.
dc.format.extent28 p.
dc.language.isoeng
dc.rightsAttribution 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Aparells mèdics::Biosensors
dc.subject.lcshParkinson's disease
dc.subject.lcshPatient monitoring
dc.subject.otherInertial Measurement Unit
dc.subject.otherParkinson’s Disease
dc.subject.otherMonitoring
dc.subject.otherInertial Data Capture
dc.subject.otherAlgorithm
dc.titleA waist-worn inertial measurement unit for Parkinson’s disease long-term monitoring
dc.typeArticle
dc.subject.lemacParkinson, Malaltia de
dc.subject.lemacMonitoratge de pacients -- Aparells i accessoris
dc.contributor.groupUniversitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma
dc.identifier.doi10.3390/s17040827
dc.description.peerreviewedPeer Reviewed
dc.relation.publisherversionhttp://www.mdpi.com/1424-8220/17/4/827/htm
dc.rights.accessOpen Access
local.identifier.drac20570161
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.authorRodriguez-Martin, D.; Perez, C.; Sama, A.; Catala, A.; Moreno, J.; Cabestany, J.; Mestre, B.; Alcaine, S.; Prats, A.; Cruz, M.; Bayés, À.
local.citation.publicationNameSensors
local.citation.volume17
local.citation.number4
local.citation.startingPage1
local.citation.endingPage28


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Attribution 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution 3.0 Spain