Exploring Smartphone Sensors for Fall and Emergency Detection

Falling, and the fear of falling, is a serious health problem among the elderly. It often results in physical and mental injuries that have the potential to severely reduce their mobility, independence and overall quality of life. Nevertheless, the consequences of a fall can be largely diminished by providing fast assistance. These facts have lead to the development of several automatic fall detection systems. Recently, many researches have focused particularly on smartphone-based applications. In this paper, we study the capacity of smartphone built-in sensors to differentiate fall events from activities of daily living. We explore, in particular, the information provided by the accelerometer, magnetometer and gyroscope sensors. A collection of features is analyzed and the efficiency of different sensor output combinations is tested using experimental data. Based on these results, a new, simple, and reliable algorithm for fall detection is proposed. In addition, we also present the algorithm for an innovative emergency application for smartphones, designated by knock-to-panic (KTP). This ingenious system enables users to simply hit their devices in order to send an alarm signal to an emergency service. This application can not only be seen as a backup in case of failure of the fall detection algorithm, but also as a complete and autonomous emergency system. Moreover, the simple and fast activation of KTP makes it a viable and potentially superior alternative to traditional emergency calls. In fact, it is our belief that KTP can be useful to the general population and not only to elderly persons. The two proposed methods are threshold-based algorithms and are designed to require a low battery power consumption.