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Noncontact healthy status sensing using low-power digital-IF Doppler radar

Noncontact healthy status sensing using low-power digital-IF Doppler radar

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Health status sensing is of great significance in early disease prevention, clinical treatment and back-end home care. Vital signs can not only provide physiological information but also reflect various health statuses of human subjects. Our emphasis is on discovering inner relationships between the Doppler radar-based noncontact vital sign detection and the health status of human subjects. The custom-designed low-power digital intermediate frequency (digital-IF) continuous-wave (CW) Doppler radar has been designed to capture vital signs with high accuracy and robustness. Then the compressed sensing (CS)-based method is proposed to enhance the resolution of the vital sign spectrum, and the synchrosqueezing transform (SST)-based algorithm is used to extract the instantaneous vital signs. Based on the digital-IF Doppler radar and the advanced signal processing algorithms, several health status sensing modules, including the breathing disorder recognition and sleep-stage estimation, have been realized by using advanced machine learning techniques. Laboratory and clinical experiments demonstrate the effectiveness of noncontact health status sensing using the proposed radar system.

Chapter Contents:

  • 8.1 Digital-IF CW Doppler radar
  • 8.1.1 RF layer
  • 8.1.2 IF layer
  • 8.1.3 Baseband layer
  • 8.2 Advanced signal processing algorithms for physiological signal extraction
  • 8.2.1 CS and stepwise ANM
  • 8.2.1.1 Sparse reconstruction
  • 8.2.1.2 MMV model of heartbeat detection
  • 8.2.1.3 Stepwise ANM
  • 8.2.1.4 Laboratorial experiments
  • 8.2.2 SST for instantaneous vital sign detection
  • 8.2.2.1 Synchrosqueezing transform
  • 8.2.2.2 Laboratorial experiments
  • 8.3 Noncontact healthy status sensing
  • 8.3.1 Breathing disorder recognition
  • 8.3.1.1 Breathing disorder recognition module
  • 8.3.1.2 Experiments
  • 8.3.2 Sleep-stage estimation
  • 8.3.2.1 Sleep-stage recognition module
  • 8.3.2.2 Experiments
  • References

Inspec keywords: compressed sensing; biomedical measurement; transforms; CW radar; learning (artificial intelligence); feature extraction; radar signal processing; Doppler radar; medical signal processing

Other keywords: breathing disorder recognition; clinical treatment; synchrosqueezing transform; low-power digital intermediate frequency continuous-wave Doppler radar; compressed sensing; early disease prevention; machine learning; noncontact vital sign detection; physiological information; home care; sleep-stage estimation; noncontact health status sensing; vital sign spectrum resolution; signal processing algorithms; low-power digital-IF Doppler radar

Subjects: Radar equipment, systems and applications; Integral transforms; Signal processing and detection; Microwaves and other electromagnetic waves (biomedical imaging/measurement); Integral transforms; Function theory, analysis; Knowledge engineering techniques; Digital signal processing; Patient diagnostic methods and instrumentation; Biology and medical computing

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