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

ISBN: 9784901122160

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.

http://eprints.bournemouth.ac.uk/29421/

Source: Scopus

Activity Recognition for Indoor Fall Detection Using Convolutional Neural Network

Authors: Adhikari, K., Bouchachia, H., Nait-Charif, H. and IEEE

Journal: PROCEEDINGS OF THE FIFTEENTH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS - MVA2017

Pages: 81-84

http://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.

http://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

http://eprints.bournemouth.ac.uk/29421/

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7981294

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.

http://eprints.bournemouth.ac.uk/29421/

Source: BURO EPrints