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

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

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 devi...

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Bibliographic Details
Main Authors: Yuhan Zhou, Rana Zia Ur Rehman, Clint Hansen, Walter Maetzler, Silvia Del Din, Lynn Rochester, Tibor Hortobágyi, Claudine J. C. Lamoth
Format: Article
Language:English
Published: MDPI AG 2020-07-01
Series:Sensors
Subjects:
gait analysis
machine learning
inertial measurement units
neurological disorders
falls
Online Access:https://www.mdpi.com/1424-8220/20/15/4098

Internet

https://www.mdpi.com/1424-8220/20/15/4098

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