Environment-independent mmWave Fall Detection with Interacting Multiple
  Model
Xuyao Yu Jiazhao Wang Wenchao Jiang
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\%.