Cargando…
Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm
The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walking. Whole-body 3D kinematics was recorded and...
Autores principales: | Martelli, Dario, Artoni, Fiorenzo, Monaco, Vito, Sabatini, Angelo Maria, Micera, Silvestro |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962372/ https://www.ncbi.nlm.nih.gov/pubmed/24658093 http://dx.doi.org/10.1371/journal.pone.0092037 |
Ejemplares similares
-
Pre-Impact Detection Algorithm to Identify Tripping Events Using Wearable Sensors
por: Aprigliano, Federica, et al.
Publicado: (2019) -
Ambulatory Assessment of the Dynamic Margin of Stability Using an Inertial Sensor Network
por: Guaitolini, Michelangelo, et al.
Publicado: (2019) -
Data-driven body–machine interface for the accurate control of drones
por: Miehlbradt, Jenifer, et al.
Publicado: (2018) -
Hybrid Human-Machine Interface for Gait Decoding Through Bayesian Fusion of EEG and EMG Classifiers
por: Tortora, Stefano, et al.
Publicado: (2020) -
Design and Evaluation of a new mechatronic platform for assessment and prevention of fall risks
por: Bassi Luciani, Lorenzo, et al.
Publicado: (2012)