GR-Fall: A Fall Detection System with Gait Recognition for Indoor Environments Using SISO mmWave Radar

Fall detection is essential for safeguarding the health of elderly persons, enabling timely alerts to family members or the community. Millimeter-wave (mmWave) radar offers an effective solution, as it is privacy-preserving, non-invasive, and highly sensitive to motion. However, most existing approaches rely on multi-input, multi-output mmWave radar to generate 4D point clouds or range-angle heatmaps, significantly raising device costs. In this paper, we propose GR-Fall, a fall detection system with integrated gait recognition designed for indoor environments using single-input, single-output mmWave radar. To achieve high performance in various environments, we develop a data augmentation algorithm for target heatmaps and a cross-attention-based heatmap fusion framework for efficient fall detection. Furthermore, we introduce an innovative fall alarm mechanism based on joint fall-gait detection. This mechanism activates alerts when a person is detected having difficulty moving after a fall, thus minimizing unnecessary alarms and reducing strain on community resources. To evaluate GR-Fall, we recruit 33 volunteers and collect 5,799 instances across four different environments. Experimental results show that GR-Fall achieves 98.1% precision and 98.7% recall in new environments and with new participants, outperforming other state-of-the-art heatmap-based methods.