Deep Learning Radar Design for Breathing and Fall Detection

The automated detection of people having a fall is particularly important for the elderly in indoor care situations. Privacy concerns, and regulations that prohibit cameras in indoor environments, mean that optical sensing must give way, at least in some situations, to less explicit sensing such as radar. Currently, fall detection using radar Doppler signatures has limitations. We demonstrate a radar-based technique that detects breathing and other movements seamlessly, and can detect a fall after it has happened, i.e., even when the person is static. Using a low-cost, ceiling-mounted radar at low microwave frequencies (sub-6GHz), our results show it is possible to remotely localize a person within a few cm. The sensor system includes a small neural network model that can distinguish a person from other moving objects in an indoor environment. This can reduce false alarms of the fall detection and hence improve system reliability in real-world deployment. In our experiments, the neural network differentiates a person from a pet (an example of a complex moving entity) with an accuracy exceeding 95%. This sensor system demonstration paves a way forward for general indoor fall detection, extending to the well-being of our elderly through real-time, ongoing monitoring of their breathing and other activities of daily living.