title: Unbiased and mobile gait analysis detects motor impairment in Parkinson's disease.
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: Department of Molecular Neurology, University Hospital Erlangen, Erlangen, Germany. jochen.klucken@uk-erlangen.de
authors: Jochen
authors: J
authors: Klucken
authors: Jens
authors: J
authors: Barth
authors: Patrick
authors: P
authors: Kugler
authors: Johannes
authors: J
authors: Schlachetzki
authors: Thore
authors: T
authors: Henze
authors: Franz
authors: F
authors: Marxreiter
authors: Zacharias
authors: Z
authors: Kohl
authors: Ralph
authors: R
authors: Steidl
authors: Joachim
authors: J
authors: Hornegger
authors: Bjoern
authors: B
authors: Eskofier
authors: Juergen
authors: J
authors: Winkler
mesh_terms: D000368:Aged; D016022:Case-Control Studies; D005188:False Negative Reactions; D005260:Female; D005684:Gait; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000175:diagnosis* / Q000503:physiopathology; D010363:Pattern Recognition, Automated; D012680:Sensitivity and Specificity; D012720:Severity of Illness Index; D014202:Tremor