  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\%.
