Abstract
In the context of the ageing global population, researchers and scientists have tried to find solutions to many challenges faced by older people. Falls, the leading cause of injury among elderly, are usually severe enough to require immediate medical attention; thus, their detection is of primary importance. To this effect, many fall detection systems that utilize wearable and ambient sensors have been proposed. In this study, we compare three newly proposed data fusion schemes that have been applied in human activity recognition and fall detection. Furthermore, these algorithms are compared to our recent work regarding fall detection in which only one type of sensor is used. The results show that fusion algorithms differ in their performance, whereas a machine learning strategy should be preferred. In conclusion, the methods presented and the comparison of their performance provide useful insights into the problem of fall detection.
Highlights
It is an indisputable fact that the global population is steadily ageing, which presents local communities with new challenges
These three methods are compared with our previous work [32], in which we consider an extension of threshold-based algorithm (TBA) based on the use of a single accelerometer
Fall detection systems constitute an important solution to the ageing population problem
Summary
It is an indisputable fact that the global population is steadily ageing, which presents local communities with new challenges. Governments adopt policies that help individuals live longer and healthier. Not everyone can reap the benefits of such strategies. Many are afflicted by health conditions and medical problems, such as falls. According to World Health Organization (WHO), “a fall is defined as an event which results in a person coming to rest inadvertently on a lower level” [1]. Falls may be caused by chronic diseases (arthritis) and visual impairment or hazards in the living environment and dangerous activities
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