A Real-Time Algorithm to Detect Falls in the Elderly

Falls in the elderly are a major health and economic problem. Hence, reliable fall detection is of great importance. Many fall detection systems have been developed but this topic is still under reach. In this work, a smart sensor based on a tri-axial digital accelerometer and a microcontroller Wi-Fi module are used to detect changes in acceleration that occur when a human being is falling. A novel real-time algorithm to distinguish between falls and daily activities is proposed. The prototype can easily connect to the internet of things thus expanding its applications. The experimental results have demonstrated high sensitivity and specificity on human fall detection.