Design of a Multistage Radar-Based Human Fall Detection System

Deep neural networks (DNN) have recently been introduced to the radar-based fall detection system to achieve high detection accuracy. However, such systems generally suffer the limitation of increased computational complexity and thus increased power consumption. In this work, a novel multi-stage radar-based fall detection system is proposed to maintain high accuracy while keeping the power consumption at a low level. The proposed system consists of three stages. In the first stage, named event detection, a simple threshold-based method is adopted to determine whether there is motion existing or not. In the second stage, a shallow neural network called preliminary screening network (PSN) with extremely low computational complexity is proposed to determine whether such activity is fall-like or not. Finally, the last step contains a DNN with heavily computational complexity, named reconstruction-based fall detector (CRFD), which is applied to determine whether such a fall-like motion is a fall or not. By adopting the proposed multi-stage architecture, the part with the highest computation cost—the CRFD would be inactivated most time and thus can significantly reduce the complexity of the overall fall detection system. The experimental results show that compared with the conventional one-stage method, the proposed multi-stage system can not only achieve high fall detection accuracy but also has potential for deployment in a much lower power mode.