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.
Falls and their related injuries are a major challenge facing elderly people. One serious issue related to falls among the elderly living at home is the 'long-lie' scenario, which is the inability to get up from the floor after a fall, followed by lying on the floor for 60 minutes, or more. Several studies of accelerometer and gyroscope-based wearable falls detection devices have been cited in the literature. However, when the subject moves around at night-time, such as making a trip from the bedroom to the toilet, it is unlikely that they will remember or even feel an inclination to wear such a device. This research will investigate the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs), that will detect falls automatically by recognizing unusual activity sequences in the home environment; hence, decreasing the number of subjects suffering the 'long-lie' scenario after a fall. A Java-based wireless sensor network (WSN) simulation was developed. This simulation reads the room coordinates from a residential map, a path-finding algorithm (A*) simulates the subject's movement through the residential environment, and PIR and PM sensors respond in a binary manner to the subject's movement. The falls algorithm was tested for four scenarios; one scenario including activities of daily living (ADL) and three scenarios simulating falls. The simulator generates movements for ten elderly people (5 female and 5 male; age: 50-70 years; body mass index: 25.85-26.77 kg/m(2)). A decision tree based heuristic classification model is used to analyze the data and differentiate falls events from normal activities. The sensitivity, specificity and accuracy of the algorithm are 100%, 66.67% and 90.91%, respectively, across all tested scenarios.
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