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Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease

1
Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Strasse 2b, D-91052 Erlangen, Germany
2
Department of Molecular Neurology, University Hospital Erlangen, Schwabachanlage 6, D-91054 Erlangen, Germany
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(14), 3103; https://doi.org/10.3390/s19143103
Received: 23 May 2019 / Revised: 9 July 2019 / Accepted: 9 July 2019 / Published: 13 July 2019
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Abstract

Mobile gait analysis systems using wearable sensors have the potential to analyze and monitor pathological gait in a finer scale than ever before. A closer look at gait in Parkinson’s disease (PD) reveals that turning has its own characteristics and requires its own analysis. The goal of this paper is to present a system with on-shoe wearable sensors in order to analyze the abnormalities of turning in a standardized gait test for PD. We investigated turning abnormalities in a large cohort of 108 PD patients and 42 age-matched controls. We quantified turning through several spatio-temporal parameters. Analysis of turn-derived parameters revealed differences of turn-related gait impairment in relation to different disease stages and motor impairment. Our findings confirm and extend the results from previous studies and show the applicability of our system in turning analysis. Our system can provide insight into the turning in PD and be used as a complement for physicians’ gait assessment and to monitor patients in their daily environment. View Full-Text
Keywords: Parkinson’s disease; pathological gait; turning analysis; wearable sensors; mobile gait analysis Parkinson’s disease; pathological gait; turning analysis; wearable sensors; mobile gait analysis
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Haji Ghassemi, N.; Hannink, J.; Roth, N.; Gaßner, H.; Marxreiter, F.; Klucken, J.; Eskofier, B.M. Turning Analysis during Standardized Test Using On-Shoe Wearable Sensors in Parkinson’s Disease. Sensors 2019, 19, 3103.

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