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The ageing society brings attention to daily elderly care through sensingtechnologies. The future smart home is expected to enable in-home dailymonitoring, such as fall detection, for seniors in a non-invasive,non-cooperative, and non-contact manner. The mmWave radar is a promisingcandidate technology for its privacy-preserving and non-contact manner.However, existing solutions suffer from low accuracy and robustness due toenvironment dependent features. In this paper, we present FADE(\underline{FA}ll \underline{DE}tection), a practical fall detection radarsystem with enhanced accuracy and robustness in real-world scenarios. The keyenabler underlying FADE is an interacting multiple model (IMM) state estimatorthat can extract environment-independent features for highly accurate andinstantaneous fall detection. Furthermore, we proposed a robust multiple-usertracking system to deal with noises from the environment and other humanbodies. We deployed our algorithm on low computing power and low powerconsumption system-on-chip (SoC) composed of data front end, DSP, and ARMprocessor, and tested its performance in real-world. The experiment shows thatthe accuracy of fall detection is up to 95\%.

Environment-independent mmWave Fall Detection with Interacting Multiple Model