Fall Detection Method Based on Convolutional Neural Network

Abstract

With the improvement of people's living standards and social medical conditions, the average life expectancy is gradually lengthening, and the proportion of the elderly in the global population is also growing. China is a big country with a large population, and the problem of population aging is becoming increasingly prominent and severe. When elderly people live alone at home without others to care for them, falls become the most common and dangerous phenomenon due to the decline of physical function and the influence of certain diseases. Therefore, rapid, efficient and accurate identification and judgment of human falls are of great significance, which can effectively alleviate the threat to the life and health of the elderly and the social medical burden of falls. This paper mainly carried out a fall detection method based on convolutional neural networks, aiming to improve the accuracy of human fall detection through the natural advantages of convolutional neural networks in image recognition, and provide better solutions for medical monitoring and elderly care.