Machine Learning Approach to Detect Falls on Elderly People Using Sound

One of the most notable consequences of aging is the loss of motor function abilities, making elderly people specially susceptible to falls, which is of the most remarkable concerns in elder care. Thus, several solutions have been proposed to detect falls, however, none of them achieved a great success mainly because of the need of wearing a recording device. In this paper, we study the use of sound to detect fall events. The advantage of this approach over the traditional ones is that the subject does not require to wear additional devices to monitor his or her activities. Here, we apply machine learning techniques to process sound simulated the most common type of fall for the elderly, i.e., when the foot collides with an obstacle and the trunk hits the ground before using his/her hands to absorb the fall. The results show that high levels of accuracy can be achieved using only a few signal processing techniques.