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Assessment and Rating of Movement Impairment in Parkinson’s Disease Using a Low-Cost Vision-Based System

  • Domenico Buongiorno
  • Gianpaolo Francesco Trotta
  • Ilaria Bortone
  • Nicola Di Gioia
  • Felice Avitto
  • Giacomo Losavio
  • Vitoantonio Bevilacqua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10956)

Abstract

Assessment and rating of Parkinson’s Disease (PD) are commonly based on the medical observation of several clinical manifestations, including the analysis of motor activities. In particular, medical specialists refer to the Movement Disorder Society – sponsored revision of Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), the most widely used scale for rating PD. The UPDRS scale also considers the observation of some subtle motor phenomena that are either difficult to capture with human eyes or subjectively considerate abnormal. In this scenario, an automatic system able to capture the considered motor exercises and rate the PD severity could be used as a support system for the healthcare sector. In this work, we implemented a simple and low-cost clinical tool that can extract motor features of two main exercises required by the UPDRS scale (the finger tapping and the foot tapping) to classify and rate the PD severity. Sixty two participants were enrolled for the purpose of the present study: thirty three PD patients and twenty nine healthy paired subjects. Results showed that an SVM using the features extracted by both considered exercises was able to classify healthy subjects and PD patients with great performances by reaching 87.1% of accuracy. The results of the classification between mild and moderate PD patients indicated that the foot tapping features were the most representative ones to discriminate (81.0% of accuracy). We can conclude that developed tool can support medical specialists in the assessment and rating of PD patients in a real clinical scenario.

Keywords

Parkinson’s Disease Microsoft Kinect Finger tapping Foot tapping UDPRS Vision system 

Notes

Acknowledgment

This work has been funded from the Italian project ROBOVIR (BRIC-INAIL-2017).

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Domenico Buongiorno
    • 1
  • Gianpaolo Francesco Trotta
    • 1
  • Ilaria Bortone
    • 2
  • Nicola Di Gioia
    • 1
  • Felice Avitto
    • 1
  • Giacomo Losavio
    • 3
  • Vitoantonio Bevilacqua
    • 1
  1. 1.Department of Electrical and Information EngineeringPolytechnic University of BariBariItaly
  2. 2.Institute of Clinical Physiology (IFC)National Research Council (CNR)PisaItaly
  3. 3.Medica Sud S.R.L.BariItaly

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