Simulated Unobtrusive Falls Detection With Multiple Persons

One serious issue related to falls among the elderly living at home or in a residential care facility is the “long lie” scenario, which involves being unable to get up from the floor after a fall for 60 min or more. This research uses a simulated environment to investigate the potential effectiveness of using wireless ambient sensors (dual-technology (microwave/infrared) motion detectors and pressure mats) to track the movement of multiple persons and to unobtrusively detect falls when they occur, therefore reducing the rate of occurrence of “long lie” scenarios. A path-finding algorithm (A*) is used to simulate the movement of one or more persons through the residential area. For analysis, the sensor network is represented as an undirected graph, where nodes in the graph represent sensors, and edges between nodes in the graph imply that these sensors share an overlapping physical region in their area of sensitivity. A second undirected graph is used to represent the physical adjacency of the sensors (even where they do not overlap in their monitored regions). These graphical representations enable the tracking of multiple subjects/groups within the environment, by analyzing the sensor activation and adjacency profiles, hence allowing individuals/groups to be isolated when multiple persons are present, and subsequently monitoring falls events. A falls algorithm, based on a heuristic decision tree classifier model, was tested on 15 scenarios, each including one or more persons; three scenarios of activity of daily living, and 12 different types of falls (four types of fall, each with three postfall scenarios). The sensitivity, specificity, and accuracy of the falls algorithm are 100.00%, 77.14%, and 89.33%, respectively.