Non-Contact Fall Detection System Using 4D Imaging Radar for Elderly Safety: Validation Study Based on a Convolutional Neural Network Model

A non-contact fall detection system, which integrates 4D imaging radar sensors with arti-ficial intelligence (AI) technology, is proposed to monitor fall accidents among the elderly. Existing wearable devices may cause discomfort during use, and camera-based systems raise privacy concerns. The solution developed in this study addresses these issues by adopting 4D radar sensors. The radar sensors generate Point Cloud data to enable the system to analyze the positions and postures of the body. Using a CNN model, these pos-tures are classified into standing, sitting, and lying, while criteria based on changes in the speed and position distinguish between falls and slow-lying movements. The Point Cloud data were normalized and organized using zero padding and k-means clustering to en-hance the learning efficiency. The proposed model achieved 98.66% accuracy in posture classification and 95% in fall detection. The monitoring system provides real-time visual representations through a web-based dashboard and Unity-based 3D avatars, along with immediate alerts in case of a fall. In conclusion, this study demonstrates the effectiveness of real-time fall detection technology and highlights the need for further research on mul-ti-sensor integration and application in various indoor environments.