Detecting Falls Using Accelerometers by Adaptive Thresholds in Mobile Devices

Abstract

In this preliminary work, we presented an effective and efficient algorithm on adaptive thresholds to automatically recognize falls from acceleration signals collected by a single tri-axial accelerometer in a mobile phone. Initial thresholds depend mainly on their carrying position of mobile phones, and then are adjusted automatically by a self-learning process and a classification module. Our researches are designed for carrying phones in casual ways which has not been done in previous researches. An android-based software is designed for experiments and the results show the efficiency of our method and improvements have been made on detection accuracy after having the learning process.