Cargando…
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...
Autores principales: | Zhou, Yuhan, Zia Ur Rehman, Rana, Hansen, Clint, Maetzler, Walter, Del Din, Silvia, Rochester, Lynn, Hortobágyi, Tibor, Lamoth, Claudine J. C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435707/ https://www.ncbi.nlm.nih.gov/pubmed/32717848 http://dx.doi.org/10.3390/s20154098 |
Ejemplares similares
-
Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders
por: Rehman, Rana Zia Ur, et al.
Publicado: (2020) -
The detection of age groups by dynamic gait outcomes using machine learning approaches
por: Zhou, Yuhan, et al.
Publicado: (2020) -
Application of Wearable Inertial Sensors and A New Test Battery for Distinguishing Retrospective Fallers from Non-fallers among Community-dwelling Older People
por: Qiu, Hai, et al.
Publicado: (2018) -
Gait analysis with wearables predicts conversion to Parkinson disease
por: Del Din, Silvia, et al.
Publicado: (2019) -
Dual-Task Elderly Gait of Prospective Fallers and Non-Fallers: A Wearable-Sensor Based Analysis
por: Howcroft, Jennifer, et al.
Publicado: (2018)