ReFall: Real-Time Fall Detection of Continuous Depth Maps with RFD-Net

Abstract

With the growth in the elderly population, fall detection methods for the elderly are of great significance. In this paper, we propose a deep learning-based method for real-time fall detection continuous depth maps with Residual Fall Detection Network (RFD-Net). Our method incorporates feature extraction with fall detection. In feature extraction part, seven important features that accurately represent the body posture are extracted from the depth maps to reduce the computation load. In the fall detection part, a novel RFD-Net is proposed to recognize body posture for fall detection. Meanwhile, two other networks are developed to compare with RFD-Net. The experimental results show that the extracted features are good representative of the body posture, and our method delivers performance with a fall detection accuracy of 98.51%, which is higher than other related methods.