Cargando…
Accelerometer-Based Fall Detection Using Machine Learning: Training and Testing on Real-World Falls
Falling is a significant health problem. Fall detection, to alert for medical attention, has been gaining increasing attention. Still, most of the existing studies use falls simulated in a laboratory environment to test the obtained performance. We analyzed the acceleration signals recorded by an in...
Autores principales: | Palmerini, Luca, Klenk, Jochen, Becker, Clemens, Chiari, Lorenzo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7697900/ https://www.ncbi.nlm.nih.gov/pubmed/33202738 http://dx.doi.org/10.3390/s20226479 |
Ejemplares similares
-
Evaluation of Accelerometer-Based Fall Detection Algorithms on Real-World Falls
por: Bagalà, Fabio, et al.
Publicado: (2012) -
Validation of accuracy of SVM-based fall detection system using real-world fall and non-fall datasets
por: Aziz, Omar, et al.
Publicado: (2017) -
The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls
por: Klenk, Jochen, et al.
Publicado: (2016) -
A Wavelet-Based Approach to Fall Detection
por: Palmerini, Luca, et al.
Publicado: (2015) -
FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community
por: Cattelani, Luca, et al.
Publicado: (2015)