Software simulation of unobtrusive falls detection at night-time using passive infrared and pressure mat sensors

Abstract

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.

Real-Time Human Ambulation, Activity, and Physiological Monitoring: Taxonomy of Issues, Techniques, Applications, Challenges and Limitations

“…This implies the need to design wearable monitoring systems that are comfortable to wear and are independent of location and orientation. Also, subjects may take the device off (e.g., to shower) and may not remember or be inclined to wear it, especially at night time (e.g., going to and from the toilet) and, therefore, may not be wearing it when a fall occurs [ 40 ].…”

Design of an unobtrusive system for fall detection in multiple occupancy residences

“…The PIR sensor (MP Motion Sensor NaPiOn, Panasonic Electric Works Co., Ltd.) is able to detect human movement within the detection range/coverage area of 100 • in the azimuth, ±82 • in the elevation, and up to a distance of about 5 m. The PM acted as a contact switch, producing a binary signal output when a certain range of pressures (about 2-30 psi) was applied to an area of about 0.21 m 2 [18].…”

Use of Information Technology for Falls Detection and Prevention in the Elderly

“…The system is highly accurate and can monitor patients and detect various falls positions. By incorporating this technology into the unobtrusive fall detection system (WSN) described by Ariani et al (2010), the specificity of that system could be improved since the cameras are meant to record whatever is in their view and the likelihood of misinterpreting a standing position for a fall is low. Using cameras in a falls detection system has an added advantage of automatically monitoring people without the need for victims to initiate a call for help.…”

“…When the subject moves around at night-time, such as making a trip from the bedroom to the toilet, it is unlikely that they would remember or even feel an inclination to wear such a device. Ariani et al (2010) investigated the potential usefulness of an unobtrusive fall detection system, based on the use of passive infrared sensors (PIRs) and pressure mats (PMs) that detected falls automatically by recognizing unusual activity sequences in the home environment (Table 1). Table 1 Sensitivity, specificity and accuracy of a sensor based algorithm (Ariani et al 2010) Sensitivity (the ability of the system to detect actual falls when they occur) 100 % Specificity (ability of the system to detect a 'no fall' situation when the subject has not fallen) 66.67 % Accuracy 90.91 %…”