AI-Powered Non-Contact Fall Detection System Using 4D Imaging Radar for Elderly Safety (Preprint)
Sejong Ahn, Museong Choi, Jinseok Kim, Sung-Taek Chung · 2025
BACKGROUND The aging population is increasing due to declining birth rates and advancements in medical technology, leading to a rise in single-person elderly households. While factors such as spousal loss, children's independence, and the pursuit of autonomy enable independent living, they also raise concerns about vulnerability during emergencies. Additionally, health issues such as reduced muscle strength, impaired balance, weakened vision, and arthritis significantly increase the risk of falls, which can result in severe injuries, decreased mobility, and mental health challenges. Consequently, fall prevention and management have become essential for maintaining and improving the quality of life in the elderly. The growing demand for technological solutions to enhance elderly safety has driven research into smart healthcare services that provide real-time monitoring and immediate fall response. OBJECTIVE A non-contact fall detection system, which integrates 4D imaging radar sensors with artificial 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. METHODS 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 postures 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 enhance the learning efficiency. RESULTS 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. CONCLUSIONS In conclusion, this study demonstrates the effectiveness of real-time fall detection technology and highlights the need for further research on multi-sensor integration and application in various indoor environments.