A self-managed system for automated assessment of UPDRS upper limb tasks in Parkinson’s disease

Claudia Ferraris, Roberto Nerino, Antonio Chimienti, Giuseppe Pettiti, Nicola Cau, Veronica Cimolin, Corrado Azzaro, Giovanni Albani, Lorenzo Priano, Alessandro Mauro

Research output: Contribution to journalArticle

Abstract

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.

Original languageEnglish
Article number3523
JournalSensors (Switzerland)
Volume18
Issue number10
DOIs
Publication statusPublished - Oct 18 2018

Keywords

  • At-home monitoring
  • Automated assessment
  • Hand tracking
  • Human computer interface
  • Machine learning
  • Movement disorders
  • Parkinson’s disease
  • RGB-depth
  • UPDRS

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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  • Cite this

    Ferraris, C., Nerino, R., Chimienti, A., Pettiti, G., Cau, N., Cimolin, V., Azzaro, C., Albani, G., Priano, L., & Mauro, A. (2018). A self-managed system for automated assessment of UPDRS upper limb tasks in Parkinson’s disease. Sensors (Switzerland), 18(10), [3523]. https://doi.org/10.3390/s18103523