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.

Figure 1. Embedded gait analysis using intelligent technology (eGaIT) concept.

A: Shoe equipped with biosensors. B: Exemplary raw signal data from accelerometer with some automated computed features. C: Pattern recognition includes feature extraction from biosensor signals followed by selection, and classification of subgroups. Different pattern recognition algorithms were created: A_PD distinguishes between patients and controls, is generated in a training population and validated in an independent validation population. A_H&Y and A_UPDRS classify PD subgroups and include all samples.

Figure 2. Single feature changes of gait characteristics in PD.

Individual feature show significant differences between PD and controls (representative examples A, two-feature blots B), but groups overlap substantially (*: p≤0.001, T-test).