Wireless Mobile Communication and Healthcare. Second International ICST Conference, MobiHealth 2010, Ayia Napa, Cyprus, October 18-20, 2010. Revised Selected Papers

Research Article

On Assessing Motor Disorders in Parkinson’s Disease

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  • @INPROCEEDINGS{10.1007/978-3-642-20865-2_5,
        author={Markos Tsipouras and Alexandros Tzallas and Evanthia Tripoliti and Georgios Rigas and Panagiota Bougia and Dimitrios Fotiadis and Sofia Tsouli and Spyridon Konitsiotis},
        title={On Assessing Motor Disorders in Parkinson’s Disease},
        proceedings={Wireless Mobile Communication and Healthcare. Second International ICST Conference, MobiHealth 2010, Ayia Napa, Cyprus, October 18-20, 2010. Revised Selected Papers},
        proceedings_a={MOBIHEALTH},
        year={2012},
        month={5},
        keywords={Parkinson’s disease motor symptoms assessment Levodopa-induced dyskinesia Freezing of Gait accelerometer gyroscope},
        doi={10.1007/978-3-642-20865-2_5}
    }
    
  • Markos Tsipouras
    Alexandros Tzallas
    Evanthia Tripoliti
    Georgios Rigas
    Panagiota Bougia
    Dimitrios Fotiadis
    Sofia Tsouli
    Spyridon Konitsiotis
    Year: 2012
    On Assessing Motor Disorders in Parkinson’s Disease
    MOBIHEALTH
    Springer
    DOI: 10.1007/978-3-642-20865-2_5
Markos Tsipouras1, Alexandros Tzallas1,*, Evanthia Tripoliti1,*, Georgios Rigas1,*, Panagiota Bougia1,*, Dimitrios Fotiadis1,*, Sofia Tsouli1, Spyridon Konitsiotis1,*
  • 1: University of Ioannina
*Contact email: atzallas@cc.uoi.gr, evi@cs.uoi.gr, rigas@cs.uoi.gr, pbougia@cc.uoi.gr, fotiadis@cs.uoi.gr, skonitso@cc.uoi.gr

Abstract

In this paper we propose an automated method for assessing motor symptoms in Parkinson’s disease. Levodopa-induced dyskinesia (LID) and Freezing of Gait (FoG) are detected based on the analysis of signals recorded from wearable devices, i.e. accelerometers and gyroscopes, which are placed on certain positions on the patient’s body. The signals are initially pre-processed and then analyzed, using a moving window, in order to extract features from them. These features are used for LID and FoG assessment. Two classification techniques are employed, decision trees and random forests. The method has been evaluated using a group of patients and the obtained results indicate high classification ability, being 96.11% classification accuracy for FoG detection and 92.59% for LID severity assessment.