A robust fall detection approach based on 4D imaging millimeter-wave radar

Abstract

Falls are the leading cause of injuries and even fatalities among elderly individuals in home environments, resulting in the development of fall detection technology particularly crucial. In this paper, we propose a robust human fall detection method based on the millimeter-wave radar and 4D point cloud imaging technology. The main objective of this method is to detect various types of fall actions in real-time and provide timely alerts to assist the fallen individuals. In our proposed method, we first perform range-FFT and static clutter suppression on the radar echo data. Subsequently, we conduct rangedomain target detection and angle estimation to generate initial point cloud information. Next, we introduce the median absolute deviation (MAD) based outlier removal method to eliminate non-human body outliers from the point cloud. Lastly, we present a suspected fall detection (SFD) method and a secondary fall detection method based on support vector machines (SVM) to maintain high detection accuracy while minimizing false alarms. The experimental results demonstrate that the average detection accuracy of our method for different types of falls is 97.5%, with an average false alarm rate of 0.4%.