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Open AccessArticle

A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease

1
Institute of Electronics, Computer and Telecommunication Engineering, National Research Council, 10129 Torino, Italy
2
Department of Electronics, Information and Bioengineering, Politecnico di Milano, 20133 Milano, Italy
3
Department of Neurology and NeuroRehabilitation, Istituto Auxologico Italiano, IRCCS, S. Giuseppe Hospital, 28824 Piancavallo, Italy
4
Department of Neurosciences, University of Turin, 10124 Torino, Italy
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3523; https://doi.org/10.3390/s18103523
Received: 7 September 2018 / Revised: 5 October 2018 / Accepted: 15 October 2018 / Published: 18 October 2018
(This article belongs to the Special Issue Sensors for Gait, Posture, and Health Monitoring)
A home-based, reliable, objective and automated assessment of motor performance of patients affected by Parkinson’s Disease (PD) is important in disease management, both to monitor therapy efficacy and to reduce costs and discomforts. In this context, we have developed a self-managed system for the automated assessment of the PD upper limb motor tasks as specified by the Unified Parkinson’s Disease Rating Scale (UPDRS). The system is built around a Human Computer Interface (HCI) based on an optical RGB-Depth device and a replicable software. The HCI accuracy and reliability of the hand tracking compares favorably against consumer hand tracking devices as verified by an optoelectronic system as reference. The interface allows gestural interactions with visual feedback, providing a system management suitable for motor impaired users. The system software characterizes hand movements by kinematic parameters of their trajectories. The correlation between selected parameters and clinical UPDRS scores of patient performance is used to assess new task instances by a machine learning approach based on supervised classifiers. The classifiers have been trained by an experimental campaign on cohorts of PD patients. Experimental results show that automated assessments of the system replicate clinical ones, demonstrating its effectiveness in home monitoring of PD. View Full-Text
Keywords: Parkinson’s disease; UPDRS; movement disorders; human computer interface; RGB-Depth; hand tracking; automated assessment; machine learning; at-home monitoring Parkinson’s disease; UPDRS; movement disorders; human computer interface; RGB-Depth; hand tracking; automated assessment; machine learning; at-home monitoring
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Ferraris, C.; Nerino, R.; Chimienti, A.; Pettiti, G.; Cau, N.; Cimolin, V.; Azzaro, C.; Albani, G.; Priano, L.; Mauro, A. A Self-Managed System for Automated Assessment of UPDRS Upper Limb Tasks in Parkinson’s Disease. Sensors 2018, 18, 3523.

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