A small trial was conducted to examine the feasibility of detecting falls using a combination of ambient passive infrared (PIR) and pressure mat (PM) sensors in a home with multiple occupants. The key tracking method made use of graph theoretical concepts to track each individual in the residence and to monitor them independently for falls. The proposed algorithm attempts to recognize falls where the subject experiences a hard fall on an indoor surface that leads to loss of consciousness or an inability to get up from the floor without assistance, due to severe injuries. The sensitivity, specificity and accuracy of the algorithm in detecting falls are 85.00%, 80.00% and 82.86%, respectively.
A significant portion of government health care funding is spent treating falls-related injuries among older adults. This cost is set to rise due to population aging in developed societies. Wearable sensors systems, often comprised of triaxial accelerometers and/or gyroscopes, have proven useful for real-time falls detection. However, a large percentage of falls occur at home and many of those happen at nighttime, when the person is unlikely to be wearing such an ambulatory monitoring device. It is envisaged that systems utilizing unobtrusive wireless sensors can be employed to survey the living space and identify unusual activity patterns which may indicate that a fall has happened at nighttime. In this study, a nighttime falls detection system designed for a single individual living at home, based on the use of passive infrared and pressure mat sensors, is explored. This paper describes both the sensor and system design, and investigates the feasibility of performing nighttime falls detection through the use of scripted scenarios using a single healthy test volunteer. In addition to normal movement activity, falls with unconsciousness, falls with repeated failed attempts to recover, and falls with successful recovery, are considered. By analyzing the location of sensor activity, periods of sensor inactivity, and unusual sensor activation patterns in uncommon locations, a sensitivity and specificity of 88.89% and 100%, respectively, are obtained (excluding falls followed by complete recovery). This demonstrates a proof-of-principle that nighttime falls detection might be achieved using a low complexity and completely unobtrusive wireless sensor network in the home.
Research shows that older people (aged 65 years and over) suffer many unintentional indoor falls which often lead to severe injuries. As a result of an increasingly aged population in developed countries, a sizable portion of healthcare funding is consumed in the treatment of fall-related injuries and associated long-term care. Detecting falls soon after they occur can be potentially live saving. In addition, early treatment of fall-related injuries can reduce treatment costs by minimizing health deterioration resulting from long periods spent incapacitated on the floor after a fall (a scenario known as a `long lie') and decreasing the number of hospital bed-days required. In this study, a previously proposed unobtrusive nighttime fall detection system based on wireless passive infrared sensors and furniture load sensors is evaluated in a pilot study involving three older subjects, monitored for a combined total of 174 days. No falls occurred during the study. The system reported a false alarm rate of 0.53 falls per day, which is comparable with similar unobtrusive and wearable sensor fall detection solutions.
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