The ageing society brings attention to daily elderly care through sensing technologies. The future smart home is expected to enable in-home daily monitoring, such as fall detection, for seniors in a non-invasive, non-cooperative, and non-contact manner. The mmWave radar is a promising candidate technology for its privacy-preserving and non-contact manner. However, existing solutions suffer from low accuracy and robustness due to environment dependent features. In this paper, we present FADE (\underline{FA}ll \underline{DE}tection), a practical fall detection radar system with enhanced accuracy and robustness in real-world scenarios. The key enabler underlying FADE is an interacting multiple model (IMM) state estimator that can extract environment-independent features for highly accurate and instantaneous fall detection. Furthermore, we proposed a robust multiple-user tracking system to deal with noises from the environment and other human bodies. We deployed our algorithm on low computing power and low power consumption system-on-chip (SoC) composed of data front end, DSP, and ARM processor, and tested its performance in real-world. The experiment shows that the accuracy of fall detection is up to 95\%.

翻译：老龄化社会使得通过传感技术进行日常老年人护理受到关注。未来的智能家居有望以非侵入、非合作、非接触的方式实现对老年人（如跌倒检测）的居家日常监测。毫米波雷达因其保护隐私和非接触的特点而成为一种有前景的候选技术。然而，现有解决方案因依赖环境特征而导致准确性和鲁棒性较低。本文提出FADE（FAall DEtection），一种在实际场景中具有更高准确性和鲁棒性的实用跌倒检测雷达系统。FADE的关键在于交互多模型（IMM）状态估计器，该估计器能够提取环境无关的特征，用于高精度且瞬时的跌倒检测。此外，我们提出了一个鲁棒的多用户跟踪系统，以处理来自环境和其他人体的噪声。我们将算法部署在由数据前端、DSP和ARM处理器组成的低计算能力、低功耗系统级芯片（SoC）上，并在实际环境中测试其性能。实验表明，跌倒检测的准确率高达95%。