Heart rate variability (HRV), which measures the fluctuation of heartbeat intervals, has been considered as an important indicator for general health evaluation. To alleviate the user burden and explore the usability for long-term health monitoring, noncontact methods for HRV monitoring have drawn tremendous attention. In this article, we present mmHRV, the first contact-free multiuser HRV monitoring system using commercial millimeter-wave (mmWave) radio. The design of mmHRV consists of two key components. First, we develop a calibration-free target detector to identify each user’s location. Second, a heartbeat signal extractor is devised, which can optimize the decomposition of the phase of the channel information modulated by the chest movement and, thus, estimate the heartbeat signal. The exact time of heartbeats is estimated by finding the peak location of the heartbeat signal while the interbeat intervals (IBIs) can be further derived for evaluating the HRV metrics of each target. We evaluate the system performance and the impact of different settings, including the distance between human and the device, user orientation, incidental angle, and blockage. Experimental results show that mmHRV can measure the HRV accurately with a median IBI estimation error of 28 ms (with respect to 96.16% accuracy). In addition, the root-mean-square error (RMSE) measured in the nonline-of-sight (NLOS) scenarios is 31.71 ms based on the experiments with 11 participants. The performance of the multiuser scenario is slightly degraded compared with the single-user case; however, the median error of the 3-user case is within 52 ms for all three tested locations.

As automobiles have become an essential part to facilitate our daily life, advanced driver assistance systems (ADASs) have been gaining more and more interest in assisting drivers to enhance both safety and convenience. To respond timely in case of an emergency, ADAS needs to keep track of the driver’s health/consciousness, which is generally achieved by monitoring the driver’s vital signs, including respiration rate (RR), heart rate (HR), and heart rate variability (HRV). However, most of the state-of-art solutions need to assume that the human is stationary, which does not hold in practical driving scenarios. To tackle the problem, we propose a novel system, which can estimate driver’s RR, HR, and interbeat intervals (IBIs) in the presence of driver’s motion artifacts using commercial millimeter-wave (mmWave) radio. The system consists of two key components. First, to extract the reflection signals containing vital signals, the motion artifacts are first removed by a novel motion compensation module, followed by the periodicity check to identify the components with vital signals. Second, the respiration and heartbeat signals are reconstructed by jointly optimizing the decomposition of all the extracted compound vital signals over different range-azimuth bins. We evaluate the system performance in a real driving environment and investigate the impact of different parameters, including the device locations, pavement conditions, and motion types. The experimental results show that the proposed system can achieve a median error of 0.16 respiration per minute (RPM), 0.82 beat per minute (BPM), and 46 ms for RR, HR, and IBI estimations, corresponding to the relative accuracy of 99.17%, 98.94%, and 94.11%, respectively.

Driving safety has been attracting more and more interest due to the unprecedented proliferation of vehicles and the subsequent increase of traffic accidents. As such the research community has been actively seeking for solutions that can make the vehicles more intelligent and thus improve driving safety in everyday life. Among all the existing approaches, in-vehicle sensing has become a great preference by monitoring the drivers health, emotion, attention, etc., which can offer rich information to the advanced driving assistant systems (ADAS) to respond accordingly and thus reduce injuries as much/early as possible. There have been many significant developments in the past few years on in-vehicle sensing. The goal of this paper is to provide a comprehensive review of the motivation, applications, state-of-the-art developments, and possible future interests in this research area. According to the application scenarios, we group the existing works into five categories, including occupancy detection, fatigue detection, distraction/inattention detection, driver authentication, and vital sign monitoring, review the fundamental techniques adopted, and present their limitations for further improvement. Finally, we discuss several future trends for enhancing current capabilities and enabling new opportunities for in-vehicle sensing.

Child presence detection (CPD) is becoming a regulatory requirement for car manufacturers to save children’s lives when they are left alone in unattended vehicles. However, most of the existing solutions require dedicated devices and suffer from limited accuracy and coverage. In this article, we build WiCPD, the first-of-its-kind in-car CPD system using commodity Wi-Fi, which can cover the entire interior of a car with no blind spot. First, we introduce a statistical electromagnetic model which accounts for the impact of motion on all the multipaths inside a car, followed by a motion statistics metric indicating the ambient motion intensity and a signal-to-noise-ratio (SNR) boosting scheme to extract the minute chest movement. Then, we design a unified CPD framework consisting of three target detector modules, including a motion target detector to detect a child in motion/awake, a stationary target detector to detect a stationary/sleeping child, and a transition target detector to detect a sleeping child with sporadic motion who is missed by both the motion and stationary target detectors. We implement a real-time WiCPD system by using commercial Wi-Fi chipsets, deploy it over 20 different cars, and collect data for multiple children aging from 4 to 50 months. The results show that WiCPD can achieve 100% detection rate within 8 s when the child is awake/in-motion and 96.56% detection rate within 20 s for a static/sleeping child. Extensive experiments also demonstrate that WiCPD can be easily deployed in minutes without calibration and enjoys very low CPU and memory consumption, thus promising a practical candidate for CPD applications.

High-accuracy target localization and tracking have been widely used in the modern navigation system. However, most of the methods such as GPS are highly dependent on time measurement accuracy, which prevents them from achieving high accuracy in practice. Time-reversal (TR) based technique has been shown to be able to achieve centimeter accuracy localization by fully utilizing the focusing effect brought by the massive multipaths naturally existing in a rich scattering environment such as indoor scenarios. By investigating a similar statistical property, this paper develops a novel high-accuracy target localization method by using massive MIMO to provide massive signal components. We first observe that the statistical autocorrelation of the received energy physically focuses into a beam around the receiver exhibiting a sinc-like distribution in far-field scenario. By leveraging such a distribution of the focusing beam, an effective way to estimate the relative moving speed of the target with respect to a single base station is proposed. We also obtain the absolute moving speed and subsequently track the target accurately by associating the speed estimation results and geometrical relationship of multiple stations. The theoretical analysis on the error in the speed and localization estimation validated by numerical simulation results show that the proposed system can achieve decimeter accuracy for target localization and tracking.

Through-the-wall radar (TWR) can detect and localize hidden targets behind obstacles, which has been widely applied in varieties of civil and military applications. However, wall reflections are usually stronger than those of the targets, making it very challenging to extract the target information. In this article, an adaptive wall clutter mitigation method is proposed. The discrete prolate spheroidal sequence (DPSS) basis is first used to model the wall clutter due to its superiority in modeling the spatially extended property of the walls. Then, by incorporating the compressed sensing (CS) technique, we propose a block adaptive subspace pursuit (SP) algorithm to estimate the support of the wall, which is further sifted by leveraging the correlation of the wall clutter over different antenna positions. Adaptively, the proposed algorithm can realize the mitigation of wall clutter and separate the target echo data without wall parameters or other prior information, which greatly improves the robustness in practice. Extensive simulations and real-world experiments commendably validate the effectiveness and superiority of the proposed method.