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

Beitrag in einer Fachzeitschrift
(Originalarbeit)


Details zur Publikation

Autor(en): Klucken J, Barth J, Kugler P, Schlachetzki J, Henze T, Marxreiter F, Kohl Z, Steidl R, Hornegger J, Eskofier B, Winkler J
Zeitschrift: PLoS ONE
Verlag: Public Library of Science
Jahr der Veröffentlichung: 2013
Band: 8
Heftnummer: 2
ISSN: 1932-6203


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. © 2013 Klucken et al.



FAU-Autoren / FAU-Herausgeber

Barth, Jens
Lehrstuhl für Informatik 5 (Mustererkennung)
Eskofier, Björn Prof. Dr.
Stiftungs-Juniorprofessur für Sportinformatik (Digital Sports)
Hornegger, Joachim Prof. Dr.-Ing.
Lehrstuhl für Informatik 5 (Mustererkennung)
Kohl, Zacharias PD Dr.
Molekular-Neurologische Abteilung in der Neurologischen Klinik
Kugler, Patrick
Lehrstuhl für Informatik 5 (Mustererkennung)
Winkler, Jürgen Prof. Dr.
Professur für Molekulare Neurologie


Autor(en) der externen Einrichtung(en)
Astrum IT GmbH


Zitierweisen

APA:
Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., Marxreiter, F.,... Winkler, J. (2013). Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease. PLoS ONE, 8(2). https://dx.doi.org/10.1371/journal.pone.0056956

MLA:
Klucken, Jochen, et al. "Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease." PLoS ONE 8.2 (2013).

BibTeX: 

Zuletzt aktualisiert 2018-16-10 um 23:20