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Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease.
PLoS One. 2013; 8(2):e56956.Plos

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.

Authors+Show Affiliations

Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany. jochen.klucken@uk-erlangen.deNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

23431395

Citation

Klucken, Jochen, et al. "Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease." PloS One, vol. 8, no. 2, 2013, pp. e56956.
Klucken J, Barth J, Kugler P, et al. Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. PLoS One. 2013;8(2):e56956.
Klucken, J., Barth, J., Kugler, P., Schlachetzki, J., Henze, T., Marxreiter, F., Kohl, Z., Steidl, R., Hornegger, J., Eskofier, B., & Winkler, J. (2013). Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. PloS One, 8(2), e56956. https://doi.org/10.1371/journal.pone.0056956
Klucken J, et al. Unbiased and Mobile Gait Analysis Detects Motor Impairment in Parkinson's Disease. PLoS One. 2013;8(2):e56956. PubMed PMID: 23431395.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease. AU - Klucken,Jochen, AU - Barth,Jens, AU - Kugler,Patrick, AU - Schlachetzki,Johannes, AU - Henze,Thore, AU - Marxreiter,Franz, AU - Kohl,Zacharias, AU - Steidl,Ralph, AU - Hornegger,Joachim, AU - Eskofier,Bjoern, AU - Winkler,Juergen, Y1 - 2013/02/19/ PY - 2012/10/12/received PY - 2013/01/16/accepted PY - 2013/2/23/entrez PY - 2013/2/23/pubmed PY - 2013/8/16/medline SP - e56956 EP - e56956 JF - PloS one JO - PLoS One VL - 8 IS - 2 N2 - 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. SN - 1932-6203 UR - https://www.unboundmedicine.com/medline/citation/23431395/Unbiased_and_mobile_gait_analysis_detects_motor_impairment_in_Parkinson's_disease_ L2 - https://dx.plos.org/10.1371/journal.pone.0056956 DB - PRIME DP - Unbound Medicine ER -