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Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease

Author

Listed:
  • Jochen Klucken
  • Jens Barth
  • Patrick Kugler
  • Johannes Schlachetzki
  • Thore Henze
  • Franz Marxreiter
  • Zacharias Kohl
  • Ralph Steidl
  • Joachim Hornegger
  • Bjoern Eskofier
  • Juergen Winkler

Abstract

Motor impairments are the prerequisite for the diagnosis in Parkinson's disease (PD). The cardinal symptoms (bradykinesia, rigor, tremor, and postural instability) are used for disease staging and assessment of progression. They serve as primary outcome measures for clinical studies aiming at symptomatic and disease modifying interventions. One major caveat of clinical scores such as the Unified Parkinson Disease Rating Scale (UPDRS) or Hoehn&Yahr (H&Y) staging is its rater and time-of-assessment dependency. Thus, we aimed to objectively and automatically classify specific stages and motor signs in PD using a mobile, biosensor based Embedded Gait Analysis using Intelligent Technology (eGaIT). eGaIT consist of accelerometers and gyroscopes attached to shoes that record motion signals during standardized gait and leg function. From sensor signals 694 features were calculated and pattern recognition algorithms were applied to classify PD, H&Y stages, and motor signs correlating to the UPDRS-III motor score in a training cohort of 50 PD patients and 42 age matched controls. Classification results were confirmed in a second independent validation cohort (42 patients, 39 controls). eGaIT was able to successfully distinguish PD patients from controls with an overall classification rate of 81%. Classification accuracy increased with higher levels of motor impairment (91% for more severely affected patients) or more advanced stages of PD (91% for H&Y III patients compared to controls), supporting the PD-specific type of analysis by eGaIT. In addition, eGaIT was able to classify different H&Y stages, or different levels of motor impairment (UPDRS-III). In conclusion, eGaIT as an unbiased, mobile, and automated assessment tool is able to identify PD patients and characterize their motor impairment. It may serve as a complementary mean for the daily clinical workup and support therapeutic decisions throughout the course of the disease.

Suggested Citation

  • Jochen Klucken & Jens Barth & Patrick Kugler & Johannes Schlachetzki & Thore Henze & Franz Marxreiter & Zacharias Kohl & Ralph Steidl & Joachim Hornegger & Bjoern Eskofier & Juergen Winkler, 2013. "Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease," PLOS ONE, Public Library of Science, vol. 8(2), pages 1-9, February.
  • Handle: RePEc:plo:pone00:0056956
    DOI: 10.1371/journal.pone.0056956
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    References listed on IDEAS

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    1. Bjoern Eskofier & Martin Kraus & Jay Worobets & Darren Stefanyshyn & Benno Nigg, 2012. "Pattern classification of kinematic and kinetic running data to distinguish gender, shod/barefoot and injury groups with feature ranking," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 15(5), pages 467-474.
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    Cited by:

    1. Robert J Ellis & Yee Sien Ng & Shenggao Zhu & Dawn M Tan & Boyd Anderson & Gottfried Schlaug & Ye Wang, 2015. "A Validated Smartphone-Based Assessment of Gait and Gait Variability in Parkinson’s Disease," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-22, October.
    2. Christiana Ossig & Florin Gandor & Mareike Fauser & Cecile Bosredon & Leonid Churilov & Heinz Reichmann & Malcolm K Horne & Georg Ebersbach & Alexander Storch, 2016. "Correlation of Quantitative Motor State Assessment Using a Kinetograph and Patient Diaries in Advanced PD: Data from an Observational Study," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-13, August.
    3. Ana Patrícia Rocha & Hugo Miguel Pereira Choupina & Maria do Carmo Vilas-Boas & José Maria Fernandes & João Paulo Silva Cunha, 2018. "System for automatic gait analysis based on a single RGB-D camera," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-24, August.
    4. Milla Juutinen & Cassia Wang & Justin Zhu & Juan Haladjian & Jari Ruokolainen & Juha Puustinen & Antti Vehkaoja, 2020. "Parkinson’s disease detection from 20-step walking tests using inertial sensors of a smartphone: Machine learning approach based on an observational case-control study," PLOS ONE, Public Library of Science, vol. 15(7), pages 1-19, July.

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