Show simple item record

dc.contributor.authorBarrachina Fernández, Mercedes
dc.contributor.authorMaitín, Ana María
dc.contributor.authorSánchez Ávila, Carmen
dc.contributor.authorRomero Muñoz, Juan Pablo
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
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectParkinson´s diseasespa
dc.subjectMotor fluctuationsspa
dc.subjectMotor symptomsspa
dc.titleWearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and
dc.description.extent900 KBspa

Files in this item

2.- Wearable Technology to Detect ...899.4KbPDFView/Open

This item appears in the following Collection(s)

Show simple item record

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