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

• Main Authors: Claudia Ferraris, Roberto Nerino, Antonio Chimienti, Giuseppe Pettiti, Nicola Cau, Veronica Cimolin, Corrado Azzaro, Giovanni Albani, Lorenzo Priano, Alessandro Mauro
• Format: Article
• Language: English
• Published: MDPI AG 2018-10-01
• Series: Sensors
• Subjects: Parkinson’s disease; UPDRS; movement disorders; human computer interface; RGB-Depth; hand tracking; automated assessment; machine learning; at-home monitoring
• Online Access: http://www.mdpi.com/1424-8220/18/10/3523

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.