  Unintentional falls can cause severe injuries and even death, especially if
no immediate assistance is given. The aim of Fall Detection Systems (FDSs) is
to detect an occurring fall. This information can be used to trigger the
necessary assistance in case of injury. This can be done by using either
ambient-based sensors, e.g. cameras, or wearable devices. The aim of this work
is to study the technical aspects of FDSs based on wearable devices and
artificial intelligence techniques, in particular Deep Learning (DL), to
implement an effective algorithm for on-line fall detection. The proposed
classifier is based on a Recurrent Neural Network (RNN) model with underlying
Long Short-Term Memory (LSTM) blocks. The method is tested on the publicly
available SisFall dataset, with extended annotation, and compared with the
results obtained by the SisFall authors.
