Fall detection for elderly person care using convolutional neural networks

Abstract

Falls are one of the major leading causes of mortality for elderly people living alone at home, which can lead to severe injuries. Fall detection is the most important health care issue for the elderly. In computer vision domain, significant breakthrough technologies such as deep learning have been obtained for over five years. Deep learning belongs to computational methods that allow an algorithm to program itself by learning from training data. Convolutional neural networks (CNNs), a specific type of deep learning, have set the state-of-the-art image classification performance in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in recent years. In this paper, we present the use of convolutional neural networks for fall detection in video surveillance environment. CNN is directly applied to each frame image in the video to learn human shape deformation features that describe different postures of the human and determine if a fall occurs. Experimental results show that our proposed approach runs in real-time and achieves average accuracy of 99.98% for 10-fold cross-validation for fall detection. It is shown that the implemented CNN-based fall detection approach can be a promising solution for detecting falls.