397 research outputs found
Simultaneous Monitoring of Multiple People's Vital Sign Leveraging a Single Phased-MIMO Radar
Vital sign monitoring plays a critical role in tracking the physiological
state of people and enabling various health-related applications (e.g.,
recommending a change of lifestyle, examining the risk of diseases).
Traditional approaches rely on hospitalization or body-attached instruments,
which are costly and intrusive. Therefore, researchers have been exploring
contact-less vital sign monitoring with radio frequency signals in recent
years. Early studies with continuous wave radars/WiFi devices work on detecting
vital signs of a single individual, but it still remains challenging to
simultaneously monitor vital signs of multiple subjects, especially those who
locate in proximity. In this paper, we design and implement a time-division
multiplexing (TDM) phased-MIMO radar sensing scheme for high-precision vital
sign monitoring of multiple people. Our phased-MIMO radar can steer the mmWave
beam towards different directions with a micro-second delay, which enables
capturing the vital signs of multiple individuals at the same radial distance
to the radar. Furthermore, we develop a TDM-MIMO technique to fully utilize all
transmitting antenna (TX)-receiving antenna (RX) pairs, thereby significantly
boosting the signal-to-noise ratio. Based on the designed TDM phased-MIMO
radar, we develop a system to automatically localize multiple human subjects
and estimate their vital signs. Extensive evaluations show that under
two-subject scenarios, our system can achieve an error of less than 1 beat per
minute (BPM) and 3 BPM for breathing rate (BR) and heartbeat rate (HR)
estimations, respectively, at a subject-to-radar distance of . The
minimal subject-to-subject angle separation is , corresponding to a
close distance of between two subjects, which outperforms the
state-of-the-art
Vital Sign Monitoring in Dynamic Environment via mmWave Radar and Camera Fusion
Contact-free vital sign monitoring, which uses wireless signals for
recognizing human vital signs (i.e, breath and heartbeat), is an attractive
solution to health and security. However, the subject's body movement and the
change in actual environments can result in inaccurate frequency estimation of
heartbeat and respiratory. In this paper, we propose a robust mmWave radar and
camera fusion system for monitoring vital signs, which can perform consistently
well in dynamic scenarios, e.g., when some people move around the subject to be
tracked, or a subject waves his/her arms and marches on the spot. Three major
processing modules are developed in the system, to enable robust sensing.
Firstly, we utilize a camera to assist a mmWave radar to accurately localize
the subjects of interest. Secondly, we exploit the calculated subject position
to form transmitting and receiving beamformers, which can improve the reflected
power from the targets and weaken the impact of dynamic interference. Thirdly,
we propose a weighted multi-channel Variational Mode Decomposition (WMC-VMD)
algorithm to separate the weak vital sign signals from the dynamic ones due to
subject's body movement. Experimental results show that, the 90
percentile errors in respiration rate (RR) and heartbeat rate (HR) are less
than 0.5 RPM (respirations per minute) and 6 BPM (beats per minute),
respectively
FarSense: pushing the range limit of WiFi-based respiration sensing with CSI ratio of two antennas
International audienceThe past few years have witnessed the great potential of exploiting channel state information retrieved from commodity WiFi devices for respiration monitoring. However, existing approaches only work when the target is close to the WiFi transceivers and the performance degrades significantly when the target is far away. On the other hand, most home environments only have one WiFi access point and it may not be located in the same room as the target. This sensing range constraint greatly limits the application of the proposed approaches in real life. This paper presents FarSense-the first real-time system that can reliably monitor human respiration when the target is far away from the WiFi transceiver pair. FarSense works well even when one of the transceivers is located in another room, moving a big step towards real-life deployment. We propose two novel schemes to achieve this goal: (1) Instead of applying the raw CSI readings of individual antenna for sensing, we employ the ratio of CSI readings from two antennas, whose noise is mostly canceled out by the division operation to significantly increase the sensing range; (2) The division operation further enables us to utilize the phase information which is not usable with one single antenna for sensing. The orthogonal amplitude and phase are elaborately combined to address the "blind spots" issue and further increase the sensing range. Extensive experiments show that FarSense is able to accurately monitor human respiration even when the target is 8 meters away from the transceiver pair, increasing the sensing range by more than 100%. 1 We believe this is the first system to enable through-wall respiration sensing with commodity WiFi devices and the proposed method could also benefit other sensing applications
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