A Low-Cost Device for the Monitoring and Detection of Falls in Older Adults

Abstract

According to the World Health Organization (WHO), falls are the second cause of death due to accidental injuries, and older adults are the ones who suffer the most from them. In Ecuador, there are about 1,300,000 older adults, and falls are a major problem for their quality of life. For this reason, in this article, we present a low-cost prototype system for the monitoring and detection of falls, with the aim of providing support for the care of older adults. This tool is based on a module that applies computer vision and image processing techniques, convolutional neural networks (CNNs) and Web and mobile applications. They allow the monitoring and control of falls. To test the operation of the system, tests were carried out with fifteen volunteers. It was determined that the system managed to correctly detect 80% of fall-related events.