Classification of Neurological Patients to Identify Fallers Based on Spatial-Temporal Gait Characteristics Measured by a Wearable Device

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ID: 113152
2020
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Abstract
Neurological patients can have severe gait impairments that contribute to fall risks. Predicting falls from gait abnormalities could aid clinicians and patients mitigate fall risk. The aim of this study was to predict fall status from spatial-temporal gait characteristics measured by a wearable device in a heterogeneous population of neurological patients. Participants (n = 384, age 49–80 s) were recruited from a neurology ward of a University hospital. They walked 20 m at a comfortable speed (single task: ST) and while performing a dual task with a motor component (DT1) and a dual task with a cognitive component (DT2). Twenty-seven spatial-temporal gait variables were measured with wearable sensors placed at the lower back and both ankles. Partial least square discriminant analysis (PLS-DA) was then applied to classify fallers and non-fallers. The PLS-DA classification model performed well for all three gait tasks (ST, DT1, and DT2) with an evaluation of classification performance Area under the receiver operating characteristic Curve (AUC) of 0.7, 0.6 and 0.7, respectively. Fallers differed from non-fallers in their specific gait patterns. Results from this study improve our understanding of how falls risk-related gait impairments in neurological patients could aid the design of tailored fall-prevention interventions.
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zhou2020sensorsclassification Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yuhan Zhou;Rana Zia Ur Rehman;Clint Hansen;Walter Maetzler;Silvia Del Din;Lynn Rochester;Tibor Hortobágyi;Claudine J. C. Lamoth;Zhou, Yuhan;Zia Ur Rehman, Rana;Hansen, Clint;Maetzler, Walter;Del Din, Silvia;Rochester, Lynn;Hortobágyi, Tibor;Lamoth, Claudine J. C.;
Journal sensors
Year 2020
DOI
10.3390/s20154098
URL
https://doi.org/10.3390/s20154098
Keywords
Machine learning gait analysis neurological disorders falls inertial measurement units Machine learning gait analysis neurological disorders falls inertial measurement units

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