Respiratory rate is an important vital sign that can be used to determine human physiological state. In recent years, Wi-Fi-based contactless respiration monitoring has drawn significant attention due to the prevalence of wireless local area network (WLAN) infrastructure. Most existing approaches to respiration monitoring perform well in controlled environments, without the presence of additional moving individuals in the area of interest. A few recent studies have attempted to reduce the impact of other people moving in the vicinity of the target individual. However, these approaches exhibit notable limitations, such as restricting the number of interfering individuals to one, or requiring a direct wired connection between the Wi-Fi transmitter and receiver for synchronization. To address these issues, in this study, we develop a contactless respiration monitoring system using commodity Wi-Fi devices, which we name RoSense. Through a series of empirical studies, we observe that the channel state information (CSI) for subcarriers is significantly affected by the presence of interfering individuals, but a small subset retain relatively clear signal patterns linked to the target’s respiration. Leveraging these findings, RoSense employs a signal power-based subcarrier selection strategy to identify high-quality subcarriers. The selected subcarriers are then aligned to enhance signal gain and fused to complement the weaker periodic parts. Additionally, RoSense periodically detects the quality of subcarriers, selecting the most effective subcarriers to maximize the contribution of high-quality ones. Extensive experiments were performed in real-world settings with 10 volunteers to verify the feasibility and effectiveness of RoSense. Our results demonstrate that RoSense is able to achieve robust respiration monitoring by suppressing the impact of interfering individuals.

Recent years have witnessed considerable efforts in repurposing ubiquitous wireless communication signals for non-contact sensing.$\bf{NearLink}$is a new-generation short-range wireless communication protocol, which is designed to address the high-quality network connectivity requirements of low power, low latency and high reliability. Given these notable advantages, NearLink has great potential for widespread application in various Internet of Things (IoT) areas. However, NearLink-based wireless sensing has not yet been explored. To bridge this gap, this work explores for the first time the sensing potential and opportunities of NearLink. Specifically, we systematically investigate the sensing capability of NearLink through answering two key questions: (1) How can NearLink's communication-oriented signals be adapted for sensing tasks? (2) How can sensing performance be enhanced under multipath interference and low-power constraints? We prototype the NearLink sensing system-NearSense, and take the respiration detection as a case study to demonstrate its effectiveness. Extensive experiments demonstrate that NearSense can achieve an average detection rate of 98% and a false alarm rate below 1.5% in case of various real-life challenging interference. We believe this work opens up new directions for the new-generation wireless sensing towards high-quality network connections.

The widespread deployment of WiFi infrastructure has facilitated the development of Time-of-Flight (ToF) based sensing applications. ToF estimation, however, is a challenging task due to the complexity of multipath effect. In this paper, we propose a phase difference based method for ToF estimation and uncover the potential of signal cancellation to mitigate the impact of multipath and noise on phase differences among subcarriers. To separate the moving target path from the complex multipath for ToF estimation, we suggest employing specific elimination methods tailored to the characteristics of different signal components. For dynamic multipath, we observe that when a given subcarrier propagates along two paths to the receiver, with path lengths differing by half a wavelength, the phase difference introduced by these two paths cancels each other out. Therefore, we propose two metrics to identify signals that satisfy this condition, utilizing both frequency diversity and spatial diversity. Additionally, we propose leveraging time diversity to eliminate the static multipath component and reduce the impact of noise. We implemented the methods with off-the-shelf WiFi devices and achieved mean errors of 15.36 cm and 21.05 cm for distance estimation in outdoor and indoor scenarios, outperforming state-of-the-art ToF estimation method by 50% error reduction.

WiFi-based device-free motion tracking systems track persons without requiring them to carry any device. Existing work has explored signal parameters such as time-of-flight (ToF), angle-of-arrival (AoA), and Doppler-frequency-shift (DFS) extracted from WiFi channel state information (CSI) to locate and track people in a room. However, they are not robust due to unreliable estimation of signal parameters. ToF and AoA estimations are not accurate for current standards-compliant WiFi devices that typically have only two antennas and limited channel bandwidth. On the other hand, DFS can be extracted relatively easily on current devices but is susceptible to the high noise level and random phase offset in CSI measurement, which results in a speed-sign-ambiguity problem and renders ambiguous walking speeds. This paper proposes WiTraj, a device-free indoor motion tracking system using commodity WiFi devices. WiTraj improves tracking robustness from three aspects: 1) It significantly improves DFS estimation quality by using the ratio of the CSI from two antennas of each receiver, 2) To better track human walking, it leverages multiple receivers placed at different viewing angles to capture human walking and then intelligently combines the best views to achieve a robust trajectory reconstruction, and, 3) It differentiates walking from in-place activities, which are typically interleaved in daily life, so that non-walking activities do not cause tracking errors. Experiments show that WiTraj can significantly improve tracking accuracy in typical environments compared to existing DFS-based systems. Evaluations across 9 participants and 3 different environments show that the median tracking error$<2.5\%$for typical room-sized trajectories.

Recent advances in wireless sensing techniques have made it possible to recognize hand gestures using channel state information (CSI) in commodity WiFi devices. Existing WiFi-based gesture recognition systems mainly use learning-based pattern recognition methods to recognize different gestures, however, these methods fail to work well when the locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and/or the hand gesturing size change, leading to inconsistent signal patterns caused by those factors. Although some recent efforts have been made to address the so-called “domain-dependent” gesture recognition problem, they either require prior knowledge on initial locations of the hand and WiFi devices or need to train several classifiers for the specific domains. Different from the state-of-the-art methods, we construct two distinct features from a hand-oriented view (rather than from a transceiver’s view), namely, the dynamic phase vector (DPV) and motion rotation variable (MRV), which are quite consistent in characterizing a big set of handwriting gestures, despite significant change in locations of transceivers, the relative location and orientation of the hand with respect to transceivers, and the drawing sizes. We further incorporate a hierarchical sensing framework and develop HandGest—a real-time handwriting gesture recognition system using commodity WiFi devices, to precisely recognize a great number of “in-the-air” handwritings based on the aforementioned two domain-independent features and a pipeline of specific features. Extensive experiments have been done in practical settings with 20 volunteers, evaluation results demonstrate that HandGest outperforms state-of-the-art methods on a large number of handwritings with different transceivers’ location, different initial hand locations and orientations, as well as different drawing sizes. Given its superior performance, we believe that HandGest paves a new way to enhance the real-world practicality of WiFi-based gesture recognition.

Estimated Time of package Arrival (ETA) is an essential task for Alibaba E-commerce platforms like Taobao and Tmall, which may influence the user experiences of one billion customers. The main challenge in ETA prediction of Alibaba platforms is learning from high-dimensional categorical attributes, which is equally important to obtain appropriate representations for each feature, and describe the proximity among them. Although recent supervised end-to-end methods have achieved great improvements, the unsupervised embedding method for categorical attributes has not been well-studied yet, especially when dealing with large-scale sparse datasets.