Heart rate (HR) is a crucial physiological signal that can be used to monitor health and fitness. Traditional methods for measuring HR require wearable devices, which can be inconvenient or uncomfortable, especially during sleep and meditation. Noncontact HR detection methods employing microwave radar can be a promising alternative. However, they usually use high-gain antennas and require the sensor to face the user’s chest or back, making them difficult to integrate into a portable device and unsuitable for sleep and meditation tracking applications. This study presents a novel approach for noncontact HR detection using a miniaturized Soli radar chip embedded in a Google Nest Hub. The chip has a 6.5 mm × 5 mm × 0.9 mm dimension and can be easily integrated into various devices. Our approach utilizes advanced signal processing and machine learning techniques to extract HRs from radar signals. The approach is validated on a sleep dataset (62 users, 498 hours) and a meditation dataset (114 users, 1131 minutes). The approach achieves a mean absolute error (MAE) of 1.69 bpm and a mean absolute percentage error (MAPE) of 2.67% on the sleep dataset. On the meditation dataset, the approach achieves an MAE of 1.05 bpm and a MAPE of 1.56%. The recall rates for the two datasets are 88.53% and 98.16%, respectively. This study represents the first application of the noncontact HR detection technology to sleep and meditation tracking, offering a promising alternative to wearable devices for HR monitoring during sleep and meditation.View details
In this paper, a simple Doppler-range processing (DRP) algorithm was first presented to mitigate two well-known problems encountered in high-speed moving target detection using automotive radar, i.e., range/Doppler migration and velocity ambiguity. DRP algorithm can achieve full range and velocity resolutions, as well as attaining coherent integration gains, while requiring a computational complexity comparable to that of the conventional range-Doppler processing (RDP) approach. Moreover, it can also automatically resolve the velocity ambiguity problems. We then introduce a data-adaptive spotlighting (DAS) algorithm to detect weak targets buried by shadow sidelobes of nearby strong targets with folded velocities. The effectiveness of the proposed algorithms are demonstrated by numerical examples.View details
IEEE Transactions on Aerospace and Electronics Systems (2021)
The two well-known problems of high-speed moving target detection in linear frequency-modulated continuous wave (LFMCW) automotive radar applications are range/Doppler migration and velocity ambiguity. We introduce a simple Doppler-Range Processing (DRP) algorithm to mitigate the problems by first performing Doppler processing via fast Fourier transform (FFT) across slow-time chirps, followed by range processing via FFT along Doppler migration lines over fast-time samples. The proposed DRP algorithm can achieve the same range and velocity resolutions, as well as full coherent integration gains, as the conventional Range-Doppler processing (RDP) method in the static trivial case, with comparable computational complexities. We prove that the proposed DRP method can automatically resolve the velocity ambiguity and we also analyze its velocity ambiguity resolving capability in relation to the radar bandwidth and the number of chirps with a CPI. We further present a data-adaptive spotlighting (DAS) algorithm for detecting weak targets shadowed by strong targets or clutter. The effectiveness of the proposed algorithms are demonstrated by numerical examples.View details
Radio technologies are appealing for unobtrusive and remote monitoring of human activities. Radar based human activity recognition proves to be a success, for example, Project Soli developed by Google. However, it is expensive to scale up for multi-user environments. In this paper, we propose a solution—the HoloTag system—which circumvents the multi-channel-radar scaling problem through the use of a quasi-virtual ultra-low-cost UHF RFID array over which a holographic projection of its environment is measured and used to both localize and monitor the health of several targets. The method is first described in detail, before the image reconstruction process, employing known beamforming algorithms—Delay & Sum, and Capon—is shown and its scaling properties simulated. Then, the idiosyncrasies of the implementation of HoloTag using low-cost Off-The-Shelf hardware are explained, before its ability to simultaneously measure the breathing rates and positions of multiple real and synthetic targets with accuracies of better than 0.8 bpm and 20 cm is demonstrated.View details
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