Volume 9937 LNCS , 2016. Paper abstract bibtex
© Springer International Publishing AG 2016. The loss of motor function in the elderly makes this population group prone to accidental falls. Actually, falls are one of the most notable concerns in elder care. Not surprisingly, there are several technical solutions to detect falls, however, none of them has achieved great acceptance. The popularization of smart watches provides a promising tool to address this problem. In this work, we present a solution that applies machine learning techniques to process the output of a smart watch accelerometer, being able to detect a fall event with high accuracy. To this end, we simulated the two most common types of falls in elders, gathering acceleration data from the wrist, then applied that data to train two classifiers. The results show high accuracy and robust classifiers able to detect falls.