Fall Detection System Using Millimeter-Wave Radar Based on Neural Network and Information Fusion

Abstract

Falls are fatal for the elderly, and timely detection after falls is crucial. As a contactless device, the radar sensor can monitor users’ falls with the advantage of not revealing their privacy. Today, the use of radar for fall detection has made a significant progress. However, most current methods cannot be used in real complex scenes. They usually collect fewer types of actions, and the ratio of the number of nonfall samples to the number of fall samples is small, which is not consistent with the real-life scene. In addition, the classifiers are usually trained and tested on the same environments and the same people, which cannot be easily extended to new environments and new people. We designed a robust fall detection system based on the frequency-modulated continuous-wave (FMCW) radar to solve these issues. The system detects the moment of human movement and calculates the range–velocity map, range–horizontal angle map, and range–vertical angle map of the radar signals, and creates three neural networks for these three signal maps. The stacking method of ensemble learning is used to fuse the time–space–velocity features extracted by the three neural networks to identify falls. The method was trained and tested on a data set consisting of ten scenes, 21 subjects, 52 nonfall action types, and 12 fall action types. The results show that on the test set containing only new environments and new subjects, the recall is 0.983 and the precision is 0.975.