This paper reviews recent advances in biomedical and healthcare applications of Doppler radar that remotely detects heartbeat and respiration of a human subject. In the last decade, new front-end architectures, baseband signal processing methods, and system-level integrations have been proposed by many researchers in this field to improve the detection accuracy and robustness. The advantages of noncontact detection have drawn interests in various applications, such as energy smart home, baby monitor, cardiopulmonary activity assessment, and tumor tracking. While many of the reported systems were bench-top prototypes for concept verification, several portable systems and integrated radar chips have been demonstrated. This paper reviews different architectures, baseband signal processing, and system implementations. Validations of this technology in a clinical environment will also be discussed.

The complex signal demodulation and the arctangent demodulation are studied for random body movement cancellation in quadrature Doppler radar noncontact vital sign detection. This technique can be used in sleep apnea monitor, lie detector, and baby monitor to eliminate the false alarm caused by random body movement. It is shown that if the dc offset of the baseband signal is accurately calibrated, both demodulation techniques can be used for random body movement cancellation. While the complex signal demodulation is less likely to be affected by a dc offset, the arctangent demodulation has the advantage of eliminating harmonic and intermodulation interference at high carrier frequencies. When the dc offset cannot be accurately calibrated, the complex signal demodulation is more favorable. Ray-tracing model is used to show the effects of constellation deformation and optimum/null detection ambiguity caused by the phase offset due to finite antenna directivity. Experiments have been performed using 4-7 GHz radar to verify the theory.

This paper reviews recent progress of portable short-range noncontact microwave radar systems for motion detection, positioning, and imaging applications. With the continuous advancements of modern semiconductor technologies and embedded computing, many functionalities that could only be achieved by bulky radar systems in the past are now integrated into portable devices with integrated circuit chips and printed circuits boards. These portable solutions are able to provide high motion detection sensitivity, excellent signal-to-noise ratio, and satisfactory range detection capability. Assisted by on-board signal processing algorithms, they can play important roles in various areas, such as health and elderly care, veterinary monitoring, human-computer interaction, structural monitoring, indoor tracking, and wind engineering. This paper reviews some system architectures and practical implementations for typical wireless sensing applications. It also discusses potential future developments for the next-generation portable smart radar systems.

This paper focuses on the exploitation of linear-frequency-modulated continuous-wave (LFMCW) radars for noncontact range tracking of vital signs, e.g., respiration. Such short-range system combines hardware simplicity and tracking precision, thus outperforming other remote-sensing approaches in the addressed biomedical scenario. A rigorous mathematical analysis of the operating principle of the LFMCW radar in the context of vital-sign monitoring, which includes the explanation of key aspects for the maintenance of coherence, is detailed. A precise phase-based range-tracking algorithm is also presented. Exhaustive simulations are carried out to confirm the suitability and robustness against clutter, noise, and multiple scatterers of the proposed radar architecture, which is subsequently implemented at the prototype level. Moreover, live data from real experiments associated to a metal plate and breathing subjects are obtained and studied.

This paper presents a hybrid radar system that incorporates a linear frequency-modulated continuous-wave (FMCW) mode and an interferometry mode for indoor human localization and life activity monitoring applications. The unique operating principle and signal processing method allow the radar to work at two different modes for different purposes. The FMCW mode is responsible for range detection while the interferometry mode is responsible for life activities (respiration, heart beat, body motion, and gesture) monitoring. Such cooperation is built on each mode's own strength. Beam scanning is employed to determine azimuth information, which enables the system to plot 360° 2-D maps on which the room layout and objects' location can be clearly identified. Additionally, the transmitted chirp signal is coherent in phase, which is very sensitive to physiological motion and allows the proposed technique to distinguish human from nearby stationary clutters even when the human subjects are sitting still. Hence, the proposed radar is able to continuously track the location of individuals and monitor their life activities regardless of the complex indoor environment. A series of experiments have been carried out to demonstrate the proposed versatile life activity monitoring system.

Utilizing microwave continuous-wave Doppler radars to wirelessly detect mechanical vibrations have been attracting more and more interests in recent years. In this paper, aiming to solve the null point and nonlinear issues in small-angle approximation-based Doppler radar sensors and eliminate the codomain restriction in the arctangent demodulation approach, we propose and investigate an extended differentiate and cross-multiply (DACM) algorithm. With an additional accumulator, the noise performance of the original DACM algorithm is improved. Moreover, the amplitude information of the vibration can be directly retrieved from accumulation without involving any distance-dependent issue. Experimental validations show that the proposed algorithm can fully recover the vibration patterns with the measured noncalibrated amplitude agreeing well with the known precalibrated data. Application examples of mechanical fault detection and human vital sign detection are demonstrated, showing a wide range of potential applications of this algorithm.