Development and clinical validation of inertial sensor-based gait-clustering methods in Parkinson’s disease

Journal of NeuroEngineering and Rehabilitation

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[1] 0000 0001 2107 3311, grid.5330.5, Machine Learning and Data Analytics Lab, Department of Computer Science, Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Carl-Thiersch-Straße 2b, 91052, Erlangen, Germany;0000 0001 2292 8254, grid.6734.6, Control Systems Group, Department of Electrical Engineering and Computer Science, Technische Universität Berlin (TUB), Einsteinufer 17, 10587, Berlin, Germany;Department of Molecular Neurology, University Hospital Erlangen, Friedrich-Alexander University Erlangen-Nürnberg (FAU), Schwabachanlage 6, 91054, Erlangen, Germany;

关键词: Parkinson’s disease;    Gait analysis;    Inertial sensors;    Gait cluster;    Gait phases;    Classification;    Gyroscope;    Accelerometer;   

DOI  :  10.1186/s12984-019-0548-2

来源: publisher

【 摘 要 】

BackgroundGait symptoms and balance impairment are characteristic indicators for the progression in Parkinson’s disease (PD). Current gait assessments mostly focus on straight strides with assumed constant velocity, while acceleration/deceleration and turning strides are often ignored. This is either due to the set up of typical clinical assessments or technical limitations in capture volume. Wearable inertial measurement units are a promising and unobtrusive technology to overcome these limitations. Other gait phases such as initiation, termination, transitioning (between straight walking and turning) and turning might be relevant as well for the evaluation of gait and balance impairments in PD.MethodIn a cohort of 119 PD patients, we applied unsupervised algorithms to find different gait clusters which potentially include the clinically relevant information from distinct gait phases in the standardized 4x10 m gait test. To clinically validate our approach, we determined the discriminative power in each gait cluster to classify between impaired and unimpaired PD patients and compared it to baseline (analyzing all straight strides).ResultsAs a main result, analyzing only one of the gait clusters constant, non-constant or turning led in each case to a better classification performance in comparison to the baseline (increase of area under the curve (AUC) up to 19% relative to baseline). Furthermore, gait parameters (for turning, constant and non-constant gait) that best predict motor impairment in PD were identified.ConclusionsWe conclude that a more detailed analysis in terms of different gait clusters of standardized gait tests such as the 4x10 m walk may give more insights about the clinically relevant motor impairment in PD patients.

【 授权许可】

CC BY