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Review

Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges

1
Programa en Ingeniería Biomédica (PhD), ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain
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Centro de Estudios e Innovación en Gestión del Conocimiento (CEIEC), Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain
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Department de Matemática Aplicada a las TICs, ETSI Telecomunicación, Universidad Politécnica de Madrid (UPM), Avenida Complutense, 30, 28040 Madrid, Spain
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Facultad de Ciencias Experimentales, Universidad Francisco de Vitoria, 28223 Pozuelo de Alarcón, Spain
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Brain Damage Unit, Hospital Beata María Ana, 28007 Madrid, Spain
*
Authors to whom correspondence should be addressed.
Academic Editors: Alberto Giovanni Bonomi and Jens Muehlsteff
Sensors 2021, 21(12), 4188; https://doi.org/10.3390/s21124188
Received: 20 May 2021 / Revised: 7 June 2021 / Accepted: 16 June 2021 / Published: 18 June 2021
Monitoring 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. View Full-Text
Keywords: Parkinson´s disease; motor fluctuations; sensors; motor symptoms; treatment Parkinson´s disease; motor fluctuations; sensors; motor symptoms; treatment
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MDPI and ACS Style

Barrachina-Fernández, M.; Maitín, A.M.; Sánchez-Ávila, C.; Romero, J.P. Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges. Sensors 2021, 21, 4188. https://doi.org/10.3390/s21124188

AMA Style

Barrachina-Fernández M, Maitín AM, Sánchez-Ávila C, Romero JP. Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges. Sensors. 2021; 21(12):4188. https://doi.org/10.3390/s21124188

Chicago/Turabian Style

Barrachina-Fernández, Mercedes, Ana M. Maitín, Carmen Sánchez-Ávila, and Juan P. Romero 2021. "Wearable Technology to Detect Motor Fluctuations in Parkinson’s Disease Patients: Current State and Challenges" Sensors 21, no. 12: 4188. https://doi.org/10.3390/s21124188

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