A wearable fall detection system using deep learning

Abstract

Due to the growing aging of the population and the impact of falls on the health and autonomy of the older people, the development of cost-effective non-invasive automatic fall detection systems (FDS) has gained much attention. This work proposes and analyzes the capability of convolutional deep neural networks to detect fall events based on the measurements captured by wearable tri-axial accelerometers that are transported by the user to characterize the mobility of the body. The study is performed on a long public data repository containing the traces obtained from a wide group of experimental users during the execution of a predetermined set of Activities of the Daily Living (ADLs) and mimicked falls. The system is evaluated in term of accuracy, sensitivity and specificity when the network is alternatively fed with the module of the acceleration and the with the tri-axial components of the acceleration.