Fall detection using RF sensor networks

The number of people aged 65 and over continues to rapidly increase, leading to a greater need for technologies to assist in caring for an aging population. Among these technologies are fall detection systems, since falling is a major concern for the elderly. In this paper we present a method of detecting falls using radio tomographic imaging. A two-level array of RF sensor nodes is deployed around the perimeter of a room, and the shadowing losses in the signals relayed between sensors is used to detect a person's horizontal and vertical position. Training data is used to provide a relationship between the attenutation measured as a function of height and a person's pose, which is then used in a hidden Markov model. During system operation, a forward algorithm estimates the most likely current state at each time. If the time between a standing pose and a lying down pose is too short, the system detects a fall. Using a collected experimental test set, we show that the system can distinguish falls from controlled lying down actions (e.g., sitting on the floor) with 100% reliability and no false alarms.