The increase in the number world population of elderly citizens, as well as those
who live in solitude, needs an immediate solution with an intelligent monitoring system
at home. In this work, we present an intelligent fall-detection system based on IoT
to monitor the elderly with your privacy-protected. Currently, fall detection has
attracted significant research attention and deep learning has shown promising performance
in this task using conventional cameras. However, these traditional methods pose a
risk of the leakage of personal privacy. This work proposes a novel fall-detection
system that uses a continuous-wave Doppler radar sensor to acquisition the elderly
movements and sends this information thought the internet to a server with deep learning
using a convolutional neural network (CNN) that identifies the fall. The radar sensor
is inexpensive, completely camera-free, and collects no personally identifiable information,
thereby allaying privacy concerns. Additionally, unlike traditional cameras, it has
environmental robustness and dark/light-independence. The proposed system obtained
99.9% accuracy in detecting falls by using the GoogleNet convolutional neural network.
The proposed system is also capable of detecting other types of movements in addition
to those tested, including the detection of diseases such as COVID-19 through the
cough movement.
