Elderly fall detection based on convolutional neural network

Abstract

With China's entry into an aging society, the phenomenon of the elderly living alone has become an important issue of general concern. Accidental fall is a major factor endangering the safety of the elderly. Therefore, the design of intelligent nursing system with fall detection function is of great significance to ensure the safety of the elderly. For this demand, a fall detection method that can be applied to intelligent nursing equipment is proposed in this paper, which is based on the convolutional neural network (CNN). Since deep learning requires a large number of samples, however, it is not easy to obtain large-scale labelled images, transfer learning technology based on pre-trained model of Inception v3 is applied to construct new CNN model, which is implemented with the commonly used TensorFlow and Keras. The basic layers are transferred from Inception v3 and keep the weights untrainable. The full connection layer, dropout layer and output layer are added for fine-tuned training. A small-scale image data set is established for training and test. Cross entropy loss function and Adam optimization algorithm are used during training. Finally, the experimental results show that the trained model can effectively realize the automatic detection of falls, with an accuracy of 95.38%, which has a certain practical significance.