May 30, 2019 · This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection ...
An Automated Fall Detection System Using Recurrent Neural Networks
www.researchgate.net › ... › Falls
This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on ...
This work studies the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning (DL), ...
Nov 8, 2019 · This work shows a feasibility study about using RNN-based deep learning models to detect both falls and falls' risks in real time using accelerometer signals.
Sep 29, 2020 · In this work, we discuss the design of a software architecture based on recurrent neural networks which can be effective for fall detection while running ...
Missing: Automated | Show results with:Automated
The aim of Fall Detection Systems (FDSs) is to detect an occurring fall. This information can be used to trigger the necessary assistance in case of injury.
An Automated Fall Detection System Using Recurrent Neural Networks ... Authors: Francisco Luna-Perejon; Javier Civit-Masot; Isabel Amaya-Rodriguez; Lourdes Duran- ...
A deep neural network was established to en-hance the capacity of fall detection, which combines convolutional layers and aggregated residual transformation in ...
Apr 13, 2018 · The aim of this work is to study the technical aspects of FDSs based on wearable devices and artificial intelligence techniques, in particular Deep Learning ( ...
Missing: Automated | Show results with:Automated
Jun 16, 2022 · This article proposes a novel human fall detection solution based on the Fast Pose Estimation method.