Activity recognition for indoor fall detection using convolutional neural network
Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.
Journal: Proceedings of the 15th IAPR International Conference on Machine Vision Applications, MVA 2017
Pages: 81-84
DOI: 10.23919/MVA.2017.7986795
Abstract:Falls are a major health problem in the elderly population. Therefore, a dedicated monitoring system is highly desirable to improve independent living. This paper presents a video based fall detection system in an indoor environment using convolution neural network. Identifying human poses is important in detecting fall events as specific 'change of pose' defines a fall. Knowledge of series of poses is a key to detecting fall or non-fall events. A lying pose which may be considered as an after-fall pose is different from other normal activities such as lying/sleeping on the sofa or crawling. This paper uses Convolutional Neural Networks (CNN) to recognize different poses. Using Kinect, the following image combinations are explored: RGB, Depth, RGB-D and background subtracted RGB-D. We have constructed our own dataset by recording different activities performed by different people in different indoor set-ups. Our results suggest that combining RGB background subtracted and Depth with CNN gives the best possible solution for monitoring indoor video based falls.
https://eprints.bournemouth.ac.uk/29421/
Source: Scopus
Activity Recognition for Indoor Fall Detection Using Convolutional Neural Network
Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.
Journal: PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017
Pages: 81-84
https://eprints.bournemouth.ac.uk/29421/
Source: Web of Science (Lite)
Activity Recognition for Indoor Fall Detection Using Convolutional Neural Network
Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.
Conference: 15th IAPR Conference on Machine Vision Applications (MVA2017)
Dates: 8-12 May 2017
Abstract:Falls are a major health problem in the elderly population.
Therefore, a dedicated monitoring system is highly desirable to improve independent living. This paper presents a video based fall detection system in an indoor environment using convolution neural network.
Identifying human poses is important in detecting fall events as specific ”change of pose” defines a fall. Knowledge of series of poses is a key detecting fall or non-fall events. A lying pose which may be considered as an after-fall pose is different from other normal activities such as standing, sitting, bending or crawling.This paper uses Convolutional Neural Networks (CNN) to recognise different poses. Using Kinect, the following image combinations are explored: RGB, Depth, RGB-D and background subtracted RGBD.
We have constructed our own dataset by recording different activities performed by different people in different indoor set-ups. Our results suggest that combining background subtracted RGB and Depth with CNN gives the best possible solution for monitoring indoor video based falls.
https://eprints.bournemouth.ac.uk/29421/
Source: Manual
Activity recognition for indoor fall detection using convolutional neural network.
Authors: Adhikari, K., Bouchachia, H. and Nait-Charif, H.
Journal: MVA
Pages: 81-84
Publisher: IEEE
ISBN: 978-4-9011-2216-0
https://eprints.bournemouth.ac.uk/29421/
https://ieeexplore.ieee.org/xpl/conhome/7981294/proceeding
Source: DBLP
Activity Recognition for Indoor Fall Detection Using Convolutional Neural Network
Authors: Adhikari, K., Bouchachia, A. and Nait-Charif, H.
Conference: 15th IAPR Conference on Machine Vision Applications (MVA2017)
Abstract:Falls are a major health problem in the elderly population. Therefore, a dedicated monitoring system is highly desirable to improve independent living. This paper presents a video based fall detection system in an indoor environment using convolution neural network. Identifying human poses is important in detecting fall events as specific ”change of pose” defines a fall. Knowledge of series of poses is a key detecting fall or non-fall events. A lying pose which may be considered as an after-fall pose is different from other normal activities such as standing, sitting, bending or crawling.This paper uses Convolutional Neural Networks (CNN) to recognise different poses. Using Kinect, the following image combinations are explored: RGB, Depth, RGB-D and background subtracted RGBD. We have constructed our own dataset by recording different activities performed by different people in different indoor set-ups. Our results suggest that combining background subtracted RGB and Depth with CNN gives the best possible solution for monitoring indoor video based falls.
https://eprints.bournemouth.ac.uk/29421/
Source: BURO EPrints