An Integrated Approach to Fall Detection and Fall Risk Estimation Based on RGB-Depth and Inertial Sensors

Abstract

Population ageing is a growing phenomenon, especially in Europe, so researchers are developing Active and Assisted Living solutions to promote ageing in place of elderly people. One of the most critical issues is represented by falls, and the development of fall risk estimation and fall detection tools can increase safety of elderly. The aim of this work is to develop fall risk estimation and fall detection tools using data extracted from wearable and vision-based sensors. First, the synchronization issue between heterogeneous data captured by different sensors is addressed, and a straightforward synchronization procedure based on time delays affecting the samples is provided. Then, fall detection algorithms and a fall risk estimation tool based on Timed Up and Go (TUG) test, exploiting wearable Inertial Measurement Units (IMUs) and an RGB-Depth sensor (Microsoft Kinect) are proposed. The fall detection tool is evaluated on 11 healthy adults simulating 4 different falls and performing 4 activities of daily living, while the TUG is tested on 20 healthy subjects. Encouraging preliminary results are obtained with data acquired in laboratory environment. The tools are privacy preserving since only depth and skeleton information captured by Kinect are processed.