Vital signs, such as respiration rate (RR) and heart rate (HR), are essential for human health status assessment. The radar could detect RR and HR in a non-contact manner by sensing the repetitive chest wall movement caused by cardiopulmonary activity, which is attractive due to the more comfortable experience and better privacy protection. However, as the chest wall displacement caused by heartbeat is much smaller than that caused by respiration, the weak heartbeat compo-nent may be easily overwhelmed by respiration harm-onics, noise and clutter, making it difficult to achieve accurate and reliable HR detection. In order to tackle this challenge, in this paper, we design mmRH, a system can accurately estimate RR as well as HR using a frequency modulated continuous wave (FMCW) millimeter-wave (mm-wave) radar, which consists of four functional modules. In the vital sign signal extraction module, the range bin corresponding to the body part with strong vital signs is determined to extract the vital sign signal, eliminating the clutter interference. The differential enhancement module is proposed to enhance the heartbeat component by the first order temporal difference, reducing the impacts of respiration harmonics and noise on HR estimation. In the signal decomposition module, the respiration and heartbeat signals are separated via wavelet packet decomposition (WPD) to further suppress respiration harmonics and high-frequency noise of the heartbeat signal. In the vital sign rate reconstruction module, the sparse spectrum reconstruction (SSR) of vital sign rates is mapped to an adaptive filter and zero attracting sign exponentially forgetting least mean square (ZA-SEFLMS) algorithm is proposed to achieve high-resolution sparse spectrum for accurate and reliable RR and HR estimation. The effects of different settings, including the distance between the human subject and the radar, the sensor position and the user heterogeneity, are investigated by extensive experiments. The results indicate that the designed mmRH could effectively suppress the respiration harmonics, noise and clutter interference, and bring a significant improvement in RR and HR detection accuracy compared with existing methods.

Blood pressure (BP) is a vital sign that could provide crucial information for the prevention, diagnosis and treatment of many diseases. Most of the existing BP detection solutions require physical contact between human skin and sensor, which may be frustrated due to uncomfortable experience and extra burden. Recently, millimeter-wave (mm-wave) radar based BP detection has attracted more attention, but it is confronted with the challenges on noise interference, fine-grained arterial pulse signal recovery and shortage of annotated samples for training BP estimation model. In this paper, we develop mmRBP, a contactless and continuous BP detection system with a single frequency modulated continuous wave (FMCW) mm-wave radar. mmRBP consists of three modules. The arterial pulse motion extraction module is designed to mitigate the noise interference, in which the range fast Fourier transform (range-FFT) and digital beamforming (DBF) are applied to scan the space and the direction with the strongest arterial pulse motion is identified. Then, the arterial pulse signal translation module is devised to transform the mm-wave reflections to fine-grained arterial pulse signal by the convolutional neural network (CNN) based translation filter. Finally, the BP estimation module is designed to address the issue of shortage of annotated radar training samples, where a deep transfer learning (DTL) model is employed to transfer the knowledge of the source network trained by the public BP dataset to the target network trained by the collected radar dataset. Results of extensive experiments show that the error of mmRBP is 0.87±6.12 mmHg (Mean error±Standard Deviation) and 0.59±3.78 mmHg for systolic BP (SBP) and diastolic BP (DBP), which satisfy both the British Hypertension Society (BHS) and the Association for the Advancement of Medical Instrumentation (AAMI) BP measurement standards.

Blood pressure (BP) measurement is essential for assessing the health status of the cardiovascular system. Compared with the invasive approach of inserting a catheter into an artery, non-invasive solutions usually rely on wearable devices, which may be frustrated due to discomfort and inconvenience. In this paper we develop a novel continuous and contactless BP measurement system (CCBP) to measure BP according from the reflective pulse transit time (RPTT), which is acquired from a wrist pulse signal with a single-frequency-modulated continuous-wave millimeter-wave radar. CCBP consists of three functional modules. In the pulse motion extraction module, range-gating and beamforming techniques are utilized to isolate the signal from surrounding clutter and the direction of the reflection coming from pulse motion is identified to extract the phase signal with a high signal-to-noise ratio. In the pulse signal translation module, a convolutional neural network (CNN)-based translation filter is devised to transform the extracted phase signal to a fine-grained pulse signal. To prevent overfitting, three data augmentation approaches are designed to train the CNN-based filters, including overlapping, stretching and noising. In the BP estimation module, the RPTT is calculated from the pulse signal to estimate BP: the systolic BP (SBP) and diastolic BP (DBP) can be calculated from the RPTT using the BP estimation algorithm. Results of the experiment satisfy both the British Hypertension Society and the Association for the Advancement of Medical Instrumentation BP measurement standards, with 67.7% of errors being less than 5 mmHg and −1.30 ± 6.17 mmHg (mean error ± standard deviation) accuracy for SBP, and 66.2% and −3.10 ± 4.93 mmHg for DBP.