Deep learning-based fall detection using commodity Wi-Fi

Abstract

As the phenomenon of an aging population gradually becomes common worldwide, the pressure on the elderly has seen a notable increase. To address this challenge, fall detection systems are important in ensuring the safety of the elderly population, particularly those living alone. Wi-Fi sensing, as a privacy-preserving method of perception, can be deployed indoors for detecting human activities such as falls, based on the reflective properties of electromagnetic waves. Signals generated by transmitters experience reflections from various objects within indoor environments, leading to distinct propagation paths. These signals eventually aggregate at the receiver, incorporating details about the objects’ orientation and their activity states. In this study, within practical experimental environments, we collect dataset and utilize deep learning method to classify the falling events.