Abstract
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. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates 100 % for sensitivity and 93 % for specificity. Furthermore, evaluation conducted on a public dataset, for comparison with other existing smartphone-based fall detection algorithms, shows the high potential of the proposed method.
Highlights
Statistics and facts related with falls in elderly people is somewhat worrying
The purpose of this paper is to describe a new fall detection algorithm, as well as the evaluation of its performance in collected data and in a public dataset
Results and discussion we do a comparative evaluation of the performance of the fall detection algorithms described in this paper and those presented in [17]
Summary
Approximately one in every three people, over the age of sixty five, experience a fall, at least once a year, and these are the leading cause of hospitalization for this age group [16]. Another very concerning aspect of falls, among the elderly, is their reluctance to seeking treatment after suffering an injury. The economic impact of falls was estimated in 2000 to be $US19 billion in the US only [15] All of this is even more relevant when one considers that the number of old people (above 60 years old) in the world is expected to increase from 841 million in 2013 to more than 2 billion in 2050 [8]. The features generally used for fall detection are the magnitude of the acceleration, posture monitoring, change in orientation, vertical velocity, angular velocity, and angular
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