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