Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network
- 10 November 2022
- book chapter
- conference paper
- Published by Springer Science and Business Media LLC
Abstract
No abstract availableKeywords
This publication has 20 references indexed in Scilit:
- Elderly Fall Detection Using Wearable Sensors: A Low Cost Highly Accurate AlgorithmIEEE Sensors Journal, 2019
- Research and Implementation of Two-Layer Fall Detection AlgorithmPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Fall detection monitoring systems: a comprehensive reviewJournal of Ambient Intelligence and Humanized Computing, 2017
- Combining wristband-type devices and smartphones to detect fallsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world fallsEuropean Review of Aging and Physical Activity, 2016
- A Wearable Fall Detector for Elderly People Based on AHRS and Barometric SensorIEEE Sensors Journal, 2016
- Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer LearningIEEE Transactions on Medical Imaging, 2016
- A Machine Learning Framework for Gait Classification Using Inertial Sensors: Application to Elderly, Post-Stroke and Huntington’s Disease PatientsSensors, 2016
- Human fall detection with smartphonesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014
- Design of fall detection system with floor pressure and infrared imagePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2010