Fine-grained vital sign monitoring in emergency care is crucial for accurately assessing patient conditions, predicting disease progression, and formulating effective rescue plans. In non-hospital settings, limited equipment often necessitates manual observation of respiration and heartbeat, which can lead to significant errors. Contactless monitoring using wireless signals offers a promising alternative. Unlike traditional systems that require stationary devices for contactless sensing, handheld devices are more practical for rescuers during emergency care. However, sensing performance can be severely compromised by involuntary hand movements. Previous research has achieved respiration monitoring with handheld devices, but the randomness of hand motion still prevents reliable heartbeat monitoring. In this paper, we first demonstrate that the key to mitigating the effects of device motion lies in accurately estimating the motion direction. We then introduce a novel method that uses two static objects, i.e., corner reflectors, to precisely estimate the random motion direction of the device. These reflectors can be quickly and easily deployed by the rescuer before initiating vital sign monitoring, enabling a more thorough elimination of device motion effects. Comprehensive experiments validate the effectiveness of our solution using mmWave radar. Real-world tests demonstrate that our system can accurately monitor both respiration and heartbeat with handheld devices, significantly enhancing emergency medical response by improving the accuracy and feasibility of vital sign monitoring in urgent situations.

Most existing RF-based vital sign monitoring systems either assume that a human subject is stationary or discard measurements when motion is detected in order to output reliable respiration rates and heart rates. Such an assumption greatly limits the usability of these systems in practice. Even during sleep, one can undergo various body states including turns and involuntary twitches in light sleep, motionlessness during deep sleep, or abnormal limb movements due to sleep disorders such as restless legs syndrome. In this work, we develop SlpRoF, a low-cost contact-free system using a commercial-off-the-shelf UWB radar that achieves high temporal coverage and high accuracy in vital sign monitoring during sleep. By classifying body states into the motionless state, limb movement state, and torso movement state, and extracting vital signs during the first two states, it directly increases effective reporting periods over nights. By analyzing high-order harmonics and leveraging spatial diversity in captured signals from multiple on-body areas, it improves the accuracy of heart rate estimations and thus indirectly increases temporal coverage through reliable assessments. Experiment results show that SlpRoF is able to achieve an average median absolute error (MAE) of 0.44 beats per minute (bpm) in respiration rates, 1.55 s in respiration intervals, and 0.9 bpm for heart rates, respectively.

Couples sharing a bed is a common practice in households, making sleep monitoring in multi-person scenarios a significant area of research. The high sensitivity of mmWave makes it a promising tool for monitoring vital signs during sleep. However, implementing mmWave-based solutions for such settings poses two major challenges. First, the range and angular resolution of commercial mmWave radar are still inadequate for handling such close-proximity scenarios where individuals share the same bed. Second, even with sufficient angular resolution, aligning the main lobe with one target usually faces strong interference from other targets located in the sidelobes. To address these challenges, we leverage antenna pattern nulls---directions with zero gain---to mitigate interference and overcome resolution limitations. Implementing null steering on practical mmWave radar, however, is challenging due to the human body being an extended target that spans a range of angles, as well as hardware imperfections that render traditional algorithms ineffective. In this paper, we present mmNull, the first null steering system designed for commercial mmWave radars. mmNull models the human body as an extended target rather than a single point, derives a closed-form solution to maximize target gain while suppressing interferences, and employs a heuristic algorithm to address hardware imperfections. Comprehensive experiments show that mmNull achieves a more than 5x improvement in accuracy compared to current state-of-the-art (SOTA) methods for monitoring respiration rate, heart rate, and heart rate variability (HRV) in multi-person scenarios.

Wireless sensing has demonstrated its potential in numerous applications, whereas its ubiquity is often constrained by the limited signal coverage around the transceiver, hindering practical deployment in large-scale environments. In this paper, we introduce an innovative wireless sensing approach that lever ages the extensive coverage of Global Navigation Satellite Sys tem (GNSS) signals. Conventionally, GNSS signals are employed in remote sensing to monitor gradual changes in Earth's surface, such as sea level and snow depth. However, their ability to detect highly dynamic target motions, such as human respiration, gestures, and intrusions, remains largely unexplored. The core challenge comes from the absence of a proper satellite sensing model guiding high-precision applications, as well as the inter ference issue caused by the high-dynamic satellite movements and unique GNSS signal errors. To address these challenges, we propose a comprehensive satellite Fresnel zone model that provides insights into the reflection characteristics of GNSS sensing signals by considering the extensive travel distances and diverse satellite orientations. Additionally, we introduce a novel GNSS signal enhancement framework named FineSat to mitigate signal interference. Specifically, high-order polynomial fitting technique is proposed to dynamically eliminate interfer ence from satellite movements, and a novel signal differential module is proposed to cancel unique GNSS signal errors. We implement our system on commercial devices and evaluate its performance in three specific applications, namely respiration monitoring, gesture recognition, and intrusion detection. Our experiments demonstrate a mean absolute error of 0.42 bpm in respiration monitoring. The accuracy of gesture recognition and intrusion detection reaches 96.5% and 98.6%, respectively, with basic Support Vector Machine classifiers, and improves to 99.0% and 100.0%, respectively, when the deep neural network, ResNet classifiers, is used.

Reliable cardiac monitoring in daily work environments could support applications such as stress assessment and mental workload tracking. Radar sensing provides a promising contactless solution by capturing subtle chest motion without requiring wearable devices. However, many existing methods assume quasi-static conditions and experience substantial performance degradation in office environments because of self-induced interference, including body motion and the often-overlooked effect of irregular respiration. To address these challenges, we analyze the temporal structure of cardiac mechanical activity and identify two key properties: short-duration impulsiveness and short-term quasi-periodicity. Guided by these properties, we propose a signal-processing paradigm for reliable cardiac sensing under dynamic interference. Specifically, we design a bidirectional wavelet transform to extract pulse-like cardiac events from complex radar signals, a periodicity-guided multi-scale fusion strategy to retain rhythmically consistent components, and an adaptive spatial selection method to identify reliable chest reflections. We implement this paradigm as a real-time prototype system, DailyBeat, using a commercial mmWave radar. Experiments across multiple participants and office activities demonstrate that DailyBeat recovers detailed cardiac mechanical waveforms and accurately estimates heart rate, inter-beat intervals, and a QT-related mechanical surrogate, enabling fine-grained and unobtrusive cardiac monitoring in daily office environments.