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dc.contributor.authorBarrachina Fernández, Mercedes
dc.contributor.authorMaitín, Ana María
dc.contributor.authorSánchez Ávila, Carmen
dc.contributor.authorRomero Muñoz, Juan Pablo 
dc.date.accessioned2021-07-29T11:12:47Z
dc.date.available2021-07-29T11:12:47Z
dc.date.issued2021
dc.identifier.issn1424-8220spa
dc.identifier.urihttp://hdl.handle.net/10641/2378
dc.description.abstractMonitoring of motor symptom fluctuations in Parkinson’s disease (PD) patients is currently performed through the subjective self-assessment of patients. Clinicians require reliable information about a fluctuation’s occurrence to enable a precise treatment rescheduling and dosing adjustment. In this review, we analyzed the utilization of sensors for identifying motor fluctuations in PD patients and the application of machine learning techniques to detect fluctuations. The review process followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Ten studies were included between January 2010 and March 2021, and their main characteristics and results were assessed and documented. Five studies utilized daily activities to collect the data, four used concrete scenarios executing specific activities to gather the data, and only one utilized a combination of both situations. The accuracy for classification was 83.56–96.77%. In the studies evaluated, it was not possible to find a standard cleaning protocol for the signal captured, and there is significant heterogeneity in the models utilized and in the different features introduced in the models (using spatiotemporal characteristics, frequential characteristics, or both). The two most influential factors in the good performance of the classification problem are the type of features utilized and the type of model.spa
dc.language.isoengspa
dc.publisherSensorsspa
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectParkinson´s diseasespa
dc.subjectMotor fluctuationsspa
dc.subjectSensorsspa
dc.subjectMotor symptomsspa
dc.subjectTreatmentspa
dc.titleWearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges.spa
dc.typearticlespa
dc.description.versionpost-printspa
dc.rights.accessRightsopenAccessspa
dc.description.extent900 KBspa
dc.identifier.doi10.3390/s21124188spa
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/12/4188spa


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Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España