Fall Detection Using Location Sensors and Accelerometers

Abstract

The rapid aging of the world's population is driving the development of pervasive solutions for elder care. These solutions, which often involve fall detection with accelerometers, are accurate in laboratory conditions but can fail in some real-life situations. To overcome this, the authors present the Confidence system, which detects falls mainly with location sensors. A user wears one to four tags. By detecting tag locations with sensors, the system can recognize the user's activity, such as falling and then lying down afterward, as well as the context in terms of the location in the home. The authors used a scenario consisting of events difficult to recognize as falls or nonfalls to compare the Confidence system with accelerometer-based fall-detection methods, some augmented with context data from a location sensor. The methods that used context information were approximately 30 percent more accurate than those that did not. The Confidence system was also successfully validated in a real-life setting with elderly users.