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A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease

  • Ilaria Bortone
  • Gianpaolo Francesco Trotta
  • Antonio Brunetti
  • Giacomo Donato Cascarano
  • Claudio Loconsole
  • Nadia Agnello
  • Alberto Argentiero
  • Giuseppe Nicolardi
  • Antonio Frisoli
  • Vitoantonio Bevilacqua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)

Abstract

The most common methods used by neurologist to evaluate Parkinson’s Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator

for the progression of PD. In this paper, we propose an innovative set of features derived from three-dimensional Gait Analysis in order to classify motor signs of motor impairment in PD and differentiate PD patients from healthy subjects or patients suffering from other neurological diseases. We consider kinematic data from Gait Analysis as Gait Variables Score (GVS), Gait Profile Score (GPS) and spatio-temporal data for all enrolled patients. We then carry out experiments evaluating the extracted features using an Artificial Neural Network (ANN) classifier. The obtained results are promising with the best classifier score accuracy equal to 95.05%.

Keywords

Parkinson’s disease Gait analysis Artificial neural network Classification 

Notes

Acknowledgments

This work was partially supported by the Italian Ministry of Education University and Research under the Framework “Social Innovation” (DD 84 Ric, March 2nd 2012) with the Grant PON04a3_00097.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ilaria Bortone
    • 1
  • Gianpaolo Francesco Trotta
    • 2
  • Antonio Brunetti
    • 3
  • Giacomo Donato Cascarano
    • 3
  • Claudio Loconsole
    • 3
  • Nadia Agnello
    • 4
  • Alberto Argentiero
    • 4
  • Giuseppe Nicolardi
    • 5
  • Antonio Frisoli
    • 1
  • Vitoantonio Bevilacqua
    • 3
  1. 1.PERCRO LaboratoryTeCIP Institute Scuola Superiore Sant’AnnaPisaItaly
  2. 2.Department of Mechanics, Mathematics and Management EngineeringPolytechnic University of BariBariItaly
  3. 3.Department of Electrical and Information EngineeringPolytechnic University of BariBariItaly
  4. 4.ISBEM S.C.p.ABrindisiItaly
  5. 5.Laboratory of Human Anatomy and Neuroscience, Department of Biological and Environmental Technologies and SciencesUniversity of SalentoLecceItaly

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