Fall detection system based on accelerometer/gyroscope data fusion

Abstract

Falls are one of the major causes of death among the elderly worldwide. Faced with this public health challenge, many researchers have designed continuous monitoring systems to detect this type of incident at an earlier time, in order to facilitate immediate treatment and reduce potentially serious consequences. In this paper, we present a solution for the continuous monitoring of elderly or disabled people. We developed a fall detection system based on the fusion of data from a gyroscope and an accelerometer of a wearable inertial station. Two algorithms were developed: the first uses only the data from the gyroscope, while the second is based on the fusion of gyroscopic and accelerometric data. The evaluation of the system took into account several types of falls and activities, as well as different categories of people of different gender, age, height and weight, taken from the SISFALL database. The algorithm based on the gyroscope achieved an accuracy of 87.13% and a specificity of 84.27%, while its fusion with the accelerometer improved these performances to 90.76% and 89.92%, respectively, demonstrating the benefit of sensor fusion.