Cargando…
Detection of Gait Abnormalities for Fall Risk Assessment Using Wrist-Worn Inertial Sensors and Deep Learning
Falls are a significant threat to the health and independence of elderly people and represent an enormous burden on the healthcare system. Successfully predicting falls could be of great help, yet this requires a timely and accurate fall risk assessment. Gait abnormalities are one of the best predic...
Autores principales: | Kiprijanovska, Ivana, Gjoreski, Hristijan, Gams, Matjaž |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571106/ https://www.ncbi.nlm.nih.gov/pubmed/32961750 http://dx.doi.org/10.3390/s20185373 |
Ejemplares similares
-
How Accurately Can Your Wrist Device Recognize Daily Activities and Detect Falls?
por: Gjoreski, Martin, et al.
Publicado: (2016) -
Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning
por: Stankoski, Simon, et al.
Publicado: (2022) -
Predicting a Fall Based on Gait Anomaly Detection: A Comparative Study of Wrist-Worn Three-Axis and Mobile Phone-Based Accelerometer Sensors
por: Kocuvan, Primož, et al.
Publicado: (2023) -
Calibration-Free Gait Assessment by Foot-Worn Inertial Sensors
por: Laidig, Daniel, et al.
Publicado: (2021) -
Towards smart glasses for facial expression recognition using OMG and machine learning
por: Kiprijanovska, Ivana, et al.
Publicado: (2023)