Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to d...

Full description

Bibliographic Details
Main Authors: Rana Zia Ur Rehman, Yuhan Zhou, Silvia Del Din, Lisa Alcock, Clint Hansen, Yu Guan, Tibor Hortobágyi, Walter Maetzler, Lynn Rochester, Claudine J. C. Lamoth
Format: Article
Language:English
Published: MDPI AG 2020-12-01
Series:Sensors
Subjects:
neurological disorders
machine learning
classification
fall
path signature
gait
Online Access:https://www.mdpi.com/1424-8220/20/23/6992

Internet

https://www.mdpi.com/1424-8220/20/23/6992

Similar Items