As reported by the World Health Organization, falls are a severe medical and financial issue; they represent the second leading cause of unintentional injury death, after road traffic injuries. Therefore, in recent years, the interest in realizing fall detection systems is considerably increased. Although the overall architecture of such systems in terms of its basic components is consolidated, the definition of an effective method to detect falls is a challenging problem due to several difficulties arising when the system has to work in the real environment. A very recent research trend is focused on the realization of fall detection systems running directly on a smartphone, so as to avoid the inconvenience of buying and carrying additional devices. In this paper we propose a novel smartphone-based fall detection system that considers falls as anomalies with respect to a model of normal activities. Our method is compared with other very recent approaches in the state of the art and it is proved to be suitable to work on a smartphone placed in the trousers pocket. This result is confirmed both from the achieved accuracy and the required hardware resources.
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