title: Timed Up-and-Go phase segmentation in Parkinson's disease patients using unobtrusive inertial sensors.
abstract: A widely accepted functional motor test for measuring basic mobility capabilities is the `Timed Up-and-Go' (TUG) test. Although several basic mobility tasks are included, only the total time is used as outcome parameter. It has been shown that timings of sub-phases can be used as relevant clinical parameters for the assessment of Parkinson's disease patients. A variety of systems and methods have been proposed for instrumenting the TUG test, but only limited information has been published regarding phase classification. In this paper an automated TUG phase classification methodology is proposed and validated in a study with 16 Parkinson's disease patients. Statistical, signal energy, chronological and gait features were extracted from acceleration and orientation signals of shoe mounted inertial measurement units. The phases `sit to walk', `walking', `first turn', `second turn' and `turn to sit' were segmented in a two stage classifier approach. Strides were used for a separation of the walking phase and classifiers like NaiveBayes, k-Nearest-Neighbor, Support Vector Machine (SVM) and Random Forest for the final phase segmentation. SVM performed best with a mean sensitivity of 81.80% over all phases. Additionally, the impact of UPDRS and Hoehn & Yahr ratings on the phase times was assessed. The proposed methodology could be used to analyze gait parameters of sub-phases like stride length, stride time, foot clearance, heel-strike or toe-off angle for an improved assessment of Parkinson's disease patients.
authors.forename: Samuel
authors.initials: S
authors.lastname: Reinfelder
authors.forename: Roland
authors.initials: R
authors.lastname: Hauer
authors.forename: Jens
authors.initials: J
authors.lastname: Barth
authors.forename: Jochen
authors.initials: J
authors.lastname: Klucken
authors.forename: Bjoern M
authors.initials: BM
authors.lastname: Eskofier
mesh_terms: D000054:Acceleration; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology*; D010827:Physiology / Q000295:instrumentation*; D060388:Support Vector Machine; D013647:Task Performance and Analysis; D013997:Time Factors; D014741:Video Recording; D016138:Walking