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.
Highlights
Motor symptoms such as bradykinesia, rigidity, tremor, and postural instability define the diagnosis of Parkinson’s disease (PD) [1]
We developed and validated Embedded Gait Analysis using Intelligent Technology (eGaIT) as an automated embedded gait analysis system using intelligent technology that combines multiparametric analysis using pattern recognition algorithms with unbiased biosensor derived motion data. eGaIT based classification is able to use gait alterations in PD patients corresponding to the commonly used disease stages and scores for motor symptoms in PD
The usage of accelerometer based movement detection is increasing in PD
Summary
Motor symptoms such as bradykinesia, rigidity, tremor, and postural instability define the diagnosis of Parkinson’s disease (PD) [1]. The presence of rigidity and/or tremor defines distinct clinical phenotypes of PD [2]. Motor impairment leads to specific gait characteristic in PD, such as shuffling gait, reduced step length, impaired gait initiation, and reduced gait speed. Gait impairment and consecutively reduced mobility with loss of independency lead to the severe reduction of quality of life in PD patients [3]. Disease progression is categorized using the Hoehn&Yahr (H&Y) staging [5], substantially relaying on the presence and characteristics of motor signs related to independence and quality of life. Gait impairment increases and motor symptoms start to fluctuate, mainly recorded subjectively in patient diaries. Subjective information and rating of motor signs are the basis for the daily clinician’s diagnostic workup and guide therapeutic decisions
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