Low-power fall detection in home-based environments

Abstract

Fall detection of the elderly becomes more critical in an aging society. However, how to put forward fall detection with reliability and high accuracy while maintaining real-time and energy-efficiency is an important issue. To this end, we design and implement an energy-efficient prototype called Asgard, in which a fall detection algorithm and a hybrid energy-efficient strategy are proposed. The algorithm, which can flexibly track the body change by recovery angle detection, helps to reduce the false positive phenomenon as well as detection time (DT). Results of comprehensive evaluations show the accuracy rate of 96.25%, which is higher than AMD (Advanced Magnitude Detection). More notably, the prototype still has low DT with the aforementioned accuracy. More precisely, with the proposed hybrid energy-efficient algorithm, Asgard functions well for approximately one month using only two AA batteries (1500mAH each).