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

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 s...

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Autores principales: Klucken, Jochen, Barth, Jens, Kugler, Patrick, Schlachetzki, Johannes, Henze, Thore, Marxreiter, Franz, Kohl, Zacharias, Steidl, Ralph, Hornegger, Joachim, Eskofier, Bjoern, Winkler, Juergen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576377/
https://www.ncbi.nlm.nih.gov/pubmed/23431395
http://dx.doi.org/10.1371/journal.pone.0056956
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author Klucken, Jochen
Barth, Jens
Kugler, Patrick
Schlachetzki, Johannes
Henze, Thore
Marxreiter, Franz
Kohl, Zacharias
Steidl, Ralph
Hornegger, Joachim
Eskofier, Bjoern
Winkler, Juergen
author_facet Klucken, Jochen
Barth, Jens
Kugler, Patrick
Schlachetzki, Johannes
Henze, Thore
Marxreiter, Franz
Kohl, Zacharias
Steidl, Ralph
Hornegger, Joachim
Eskofier, Bjoern
Winkler, Juergen
author_sort Klucken, Jochen
collection PubMed
description 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.
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spelling pubmed-35763772013-02-21 Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease Klucken, Jochen Barth, Jens Kugler, Patrick Schlachetzki, Johannes Henze, Thore Marxreiter, Franz Kohl, Zacharias Steidl, Ralph Hornegger, Joachim Eskofier, Bjoern Winkler, Juergen PLoS One Research Article 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. Public Library of Science 2013-02-19 /pmc/articles/PMC3576377/ /pubmed/23431395 http://dx.doi.org/10.1371/journal.pone.0056956 Text en © 2013 Klucken et al http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Klucken, Jochen
Barth, Jens
Kugler, Patrick
Schlachetzki, Johannes
Henze, Thore
Marxreiter, Franz
Kohl, Zacharias
Steidl, Ralph
Hornegger, Joachim
Eskofier, Bjoern
Winkler, Juergen
Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title_full Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title_fullStr Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title_full_unstemmed Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title_short Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease
title_sort unbiased and mobile gait analysis detects motor impairment in parkinson's disease
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3576377/
https://www.ncbi.nlm.nih.gov/pubmed/23431395
http://dx.doi.org/10.1371/journal.pone.0056956
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