Recently, Wi-Fi signals have emerged as a powerful tool for contactless sensing. During the sensing process, obtaining target direction information can provide valuable contextual insights for various applications. Existing direction estimation methods typically rely on antenna arrays, which are costly and complex to deploy in real-world scenarios. In this paper, we present WiRainbow, a novel approach that enables single-antenna-based direction awareness for Wi-Fi sensing by leveraging the dispersion effect of frequency-scanning antennas (FSAs), which can naturally steer Wi-Fi subcarriers toward distinct angles during signal transmission. To address key challenges in antenna design and signal processing, we propose a coupled-resonator-based antenna architecture that significantly expands the narrow Field-of-View inherent in conventional FSAs, improving sensing coverage. Additionally, we develop a sensing signal-to-noise-ratio-based signal processing framework that reliably estimates target direction in multipath-rich environments. We prototype WiRainbow and evaluate its performance through benchmark experiments and real-world case studies, demonstrating its ability to achieve accurate, robust, and cost-effective direction awareness for diverse Wi-Fi sensing applications.

Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, highlighting the critical need for accurate and continuous heart health monitoring. Electrocardiograms (ECG), considered as the golden standard for diagnosing and monitoring heart-related conditions, offer precise measurements but require direct skin contact, limiting their practicality for long-term and everyday use. On the other hand, existing RF sensing techniques that analyze signals reflected off the skin struggle to distinguish micro cardiac motions of the heart due to weak motion amplitude and respiration interference at the chest wall. To overcome these limitations, we introduce WiCG, a novel contact-less cardiac motion monitoring system that employs 2.4 GHz Wi-Fi signals to penetrate the chest and detect subtle cardiac movements. A mix-medium Wi-Fi Fresnel zone model is developed to explain the enhanced phase sensitivity of in-body Wi-Fi signals, which is crucial for accurately detecting cardiac motions. By strategically positioning antennas near the heart, WiCG captures ventricular motions effectively. A novel cardiac Doppler method is proposed to suppress phase noise and interference from static paths and extract the time interval between the systole and diastole of the ventricular. Extensive experiments demonstrate that the proposed system can robustly estimate the R-R and Q-T intervals of human cardiac cycles across 21 subjects and different environments with an average accuracy of 99.22% and 92.8%, achieving performance comparable to ECG.

Indoor mmWave-based sensing technologies have garnered substantial interest from both the industrial and academic. Yet, the intrinsic challenge posed by the limited Field-of-View (FOV) of mmWave radars significantly restricts their coverage. This limitation necessitates careful selection of installation positions and orientations to optimize performance, thereby severely curtailing the versatility and widespread adoption of these systems. Traditionally, expanding coverage involved increasing the number of radar units. This paper introduces a novel approach to enhance the FOV by incorporating mobility, achieved by affixing the radar onto a pan-tilt unit capable of rotating along both the horizontal and azimuthal. Nevertheless, the disparity between the pan-tilt and the radar presents significant challenges for accurately rotating the radar's orientation. To mitigate this, we propose an automated calibration algorithm for radar and pan-tilt, ensuring precise calibration. Additionally, we have devised a radar orientation adjustment algorithm intended to automatically align the radar's FOV with the positions of detected objects to facilitate various applications. Through three case studies, we have demonstrated that mmRotation can greatly expand the sensing range, enabling support for multiple applications on a single radar, such as vital signs monitoring and fall detection. Comprehensive experimental results underscore that our system surpasses the current state-of-the-art (SOTA).

In recent years, wireless signals have been extensively investigated for contactless human respiration monitoring. However, most wireless sensing systems encounter challenges when the target exhibits body movements. In this demo, we present a solution to mitigate the impact of body motion on contactless respiration monitoring. By employing novel signal processing techniques, body motion can be first estimated and subsequently eliminated from the signal reflected signal by the chest. We prototype the proposed system using a MIMO mmWave radar. Evaluations in real-world environments demonstrate the effectiveness of the solution.

Wireless signals have been widely utilized for human sensing. However, wireless sensing systems face a fundamental limitation, i.e., the wireless device must keep static during the sensing process. Also, when sensing fine-grained human motions such as respiration, the human target is required to stay stationary. This is because wireless sensing relies on signal variations for sensing. When device is moving or human body is moving, the signal variation caused by the target area (e.g., chest for respiration sensing) is mixed with the signal variation induced by device or other body parts, failing wireless sensing. In this paper, we propose MSense, a general solution to deal with motion interference from wireless device and/or human body, moving wireless sensing one step forward towards real-life adoption. We establish the sensing model by taking both device motion and interfering body motion into consideration. By extracting the effect of body and device motions through pure signal processing, the motion interference can be removed to achieve accurate target sensing. Comprehensive experiments demonstrate the effectiveness of the proposed scheme. The achieved solution is general and can be applied to different sensing tasks involving both periodic and aperiodic motions.

Recent years have witnessed a tremendous amount of interest in wireless sensing, i.e., instead of employing traditional sensors, wireless signal is utilized for sensing purposes. Contact-free wireless sensing has been successfully demonstrated using various RF signals such as WiFi, RFID, LoRa, and mmWave, enabling a large range of applications. However, limited by hardware thermal noise, the granularity of RF sensing is still relatively coarse. In this paper, instead of using the macro signal power/phase for sensing, we propose the first quantum wireless sensing system, which uses the micro energy level of atoms for sensing, improving the sensing granularity by an order of magnitude. The proposed quantum wireless sensing system is capable of utilizing a wide spectrum of frequencies (e.g., 2.4 GHz, 5 GHz and 28 GHz) for sensing. We demonstrate the superior performance of quantum wireless sensing with two widely-used signals, i.e., WiFi and 28 GHz millimeter wave. We show that quantum wireless sensing can push the sensing granularity of WiFi from millimeter level to sub-millimeter level and push the sensing granularity of millimeter wave to micrometer level.

Besides the communication function, various RF signals such as WiFi and RFID have been actively exploited for sensing purposes recently. However, a missing component of existing RF sensing is sensing under device motions. This paper takes the first step to involve device mobility into the ecosystem of RF sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chips in recent years, we propose to integrate the accuracy of UWB sensing with device mobility to support truly ubiquitous RF sensing. This is a challenging task because the motion artifacts from RF devices can easily overwhelm the target motion, such as subtle chest displacement for respiration sensing. In this demo, we propose Mobi2Sense to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using a commodity UWB module. Comprehensive evaluation demonstrates that Mobi2Sense is able to "hear" music and "see" human respiration at high accuracy in the presence of device motions.

Among various wireless sensing modalities, Ultra-Wideband (UWB) exhibits unique advantages such as fine granularity owing to its super large bandwidth (500 MHz - 2 GHz). Though promising, UWB sensing was only demonstrated on dedicated hardware including DW1000 and XETHRU X4 which are not available in existing consumer-level devices. In the last few years, we observed an interesting trend of UWB module being embedded into consumer-level devices such as smartphones and smart watches. However, leveraging UWB module inside consumer-level devices for sensing poses new challenges. One key challenge is that while dedicated UWB hardware can present us with raw physical-layer signal amplitude and phase, only upper-layer distance and angle information can be extracted from consumer-level devices. In this demo, we address the challenges and present the first UWB sensing system hosted on iPhone and Apple Watch without any dedicated hardware components. We show that with just the upper-layer UWB data reported from smartphones, exciting sensing applications such as fine-grained 3D handwriting and multi-target tracking can be realized, pushing RF sensing one step forward towards real-life adoption.

While GPS-based outdoor localization has become a norm, very few indoor localization systems have been deployed and used. In this paper, we share our 5-year experience on the design, development and evaluation of a large-scale WiFi indoor localization system. We address practical challenges encountered to bridge the gap between indoor localization research in the laboratory and system deployment in the wild. The system is currently used in 1469 shopping malls, 393 office buildings and 35 hospitals across 35 cities to provide location service to millions of users on a daily basis. We hope the shared experience can benefit the design of real-world indoor localization systems and the practical problems identified can change the focus of indoor localization research. We released our dataset that contains fingerprints collected from 1469 shopping malls and one office building.

Besides the conventional communication function, wireless signals are actively exploited for sensing purposes recently. However, a missing component of existing wireless sensing is sensing under device motions. This is challenging because device motions can easily overwhelm target motions such as chest displacement used for respiration sensing. This paper takes a first step in the direction of involving device mobility into the ecosystem of wireless sensing. Owning to the miniaturization and low cost of ultra-wideband (UWB) chip in recent years, we propose to integrate the accuracy of UWB sensing with mobility to support truly ubiquitous wireless sensing. We propose Mobi2Sense, a system design to support sensing under device motions. We propose novel signal processing schemes to remove the effect of device motions on sensing and prototype Mobi2Sense using commodity UWB hardware. Real-world applications demonstrate that even in the presence of device motions, fine-grained Mobi2Sense is able to capture subtle target motions to "hear" music, "see" human respiration, and "recognize" multi-target gestures at a high accuracy.