The Doppler radar has been widely used in respiration detection. However, most of the existing microwave respiration detection works are intended for either a single human subject in front of the radar or multiple subjects with known positions. In this article, a system based on single-input-multiple-output (SIMO) continuous-wave (CW) radar equipped with adaptive digital beamforming (ADBF) technique is presented to detect the respiration of multiple human subjects at unknown positions simultaneously. A solution based on the modified Capon (m-Capon) direction-of-arrival (DOA) estimation and linear constraint minimal variance (LCMV) ADBF is proposed to automatically find the angles of human subjects. By forming spatially distributed beams toward the subjects of interest, human respiration can be remotely obtained. Furthermore, for the detection of each person's respiration, nulls are also generated at the angles of nearby interfering subjects, which results in high target discrimination capability when multiple human subjects are close to each other. The experimental results show good detection accuracy compared with the reference sensor, which verifies the effectiveness of the developed system for multitarget respiration detection.

Remote techniques for measuring human vital signs have attracted great interests due to the benefits shown in medical monitoring and military applications. Compared with continuous-wave Doppler radar, frequency-modulated continuous-wave (FMCW) radar which can discriminate vital signs from different distances, shows potential for reducing the interferences from other targets and the environment. However, in the state-of-the-art algorithms, only one chirp per frame is utilized for FMCW-based vital sign monitoring. Moreover, the vital signal is extracted from only one range bin of the fast Fourier transform. Which does not make full utilization of the long system idle time, and loses the power distributed on other range bins. By exploiting the relationship between respiration and heartbeat vibrations, an adaptive identification embedded ensemble empirical mode decomposition (EEMD) method for joint-range spectral estimation is proposed to measure the heart rate. First, A multi-chirp processing is presented for a 2- dimensional phase accumulation. Then the phase signals from a sequence of range bins are decomposed with a fast adaptive identification process. Finally, with the identified heartbeat components, we solve a multiple measurement vectors problem to estimate the heart rate. Experimental results showed that, at the detection range from 1 m~2.5 m, the proposed method can robustly distinguish the heartbeat from respiration and its harmonics and accurately estimate the heart rate with a root mean square error less than 6 bpm.

Simultaneous multitarget vital signs measurement has become a hot issue for noncontact vital signs perception. However, there is still challenge in the multitarget heartbeat measurement due to the weakness of heartbeat signal and interference from complex environment. In this article, a new multiple-input–multiple-output (MIMO) continuous-wave (CW) radar system equipped with 2-D digital beamforming (DBF) is presented to measure the respiration and heartbeat of multiple human subjects at unknown positions simultaneously. Through 2-D beam scanning of the whole scene, a 2-D radar image is generated. From the image, the chest motion of multiple targets is accurately located. Then, the vital signs of targets are obtained through forming individual beams focusing on the chests of targets. Moreover, the low intermediate frequency (low-IF) architecture is adopted to minimize the impact of flicker noise in low-frequency amplifier stages. The experimental results demonstrate that the proposed MIMO 2-D imaging radar system can locate chest areas of multiple targets, suppress the clutters, and make vital signs measurement, heartbeat measurement in particular, more robust compared with single-input–multiple-output (SIMO) radar system in complex environment.

Radar-based solutions have attracted great attention in human activity recognition (HAR) for their advantages in accuracy, robustness, and privacy protection. The conventional approaches transform radar signals into feature maps and then directly process them as visual images. While effective, these image-based methods may not be the best solutions in terms of representation efficiency to encode the relevant information for classification. This article proposes a novel HAR method combining sparse theory and PointNet network, with both operations in the time-Doppler (TD) and range-Doppler (RD) domains. First, sparsity-based feature extraction is introduced to use a limited number of sparse solutions to characterize human activities in the form of TD sparse point clouds (TDSP) or dynamic RD sparse point clouds (DRDSP). This new representation is validated by comparing the reconstructed and original signals. Then, PointNet networks are adopted to summarize multidomain features and predict human activity labels by a sparse set of input point clouds. Comprehensive experiments were conducted to demonstrate that the proposed method can yield a higher representation efficiency, classification accuracy, and better generalization capability than existing ones.

Noncontact vital signs detection based on radar has attracted a lot of interest in recent years. Many efforts have been made to make this technology more complete. However, there still exist some challenges, such as vital signs detection of two closely positioned subjects and heart rate estimation with the interference of the high-order respiration harmonics. This article designs a single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar. Based on this system, difference beamforming and orthogonal projection filter (OPF) have been proposed to solve the two challenges. Difference beamforming is able to separate the vital signs of the two subjects even if the angular difference between the two subjects is smaller than the angular resolution of the array antenna. OPF can remove the high-order respiration harmonics without complex parameter adjustment operations and tedious calculations. The experimental results show that the proposed algorithm can successfully separate the vital signs of two closely positioned subjects and estimate the heart rate accurately with the interference of high-order respiration harmonics. The results also verify that the designed SIMO FMCW radar can separate the vital signs of subjects at different distances.

As a noncontact and noninvasive sensor, radar offers attractive modality for human motion recognition in many fields, such as surveillance, smart homes, and assisted living. As a dynamic process, a comprehensive multidomain analysis in stream is crucial for human motion recognition. In this article, a novel spatial–temporal convolution long short-term memory (ST-ConvLSTM) network using dynamic range-Doppler frames (DRDF) based on portable frequency-modulated continuous-wave (FMCW) radar is proposed to address this problem. First, DRDF is introduced to characterize different human motions in time, range, Doppler, and radar cross section (RCS) domains with a time-sequenced range-Doppler maps. Then, ConvLSTM cell is investigated to extract continuous 2-D image features by combining both convolution operation and LSTM cell. Furthermore, a spatial attention module is utilized to emphasize the spatial distribution of human motion in the range-Doppler domain, while a temporal attention module is designed to obtain optimal temporal weights considering behavior continuity. Finally, an extensive radar dataset from 16 volunteers demonstrated its feasibility and superiority in the classification of six typical daily human motions. The contributions of attention modules and its robustness facing individual diversity are also investigated and discussed.