Online Fall Detection using Recurrent Neural Networks
This work addresses fall detection for elderly or at-risk individuals to enable timely assistance, but it is incremental as it applies a standard deep learning method to a known dataset.
The paper tackled the problem of detecting falls in real-time using wearable devices by developing a classifier based on a Recurrent Neural Network with LSTM blocks, achieving results comparable to existing benchmarks on the SisFall dataset.
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.