Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing-enabled gesture recognition due to its inherent merits like remote sensing, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “where to look” and “when to look.” To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi channel state information via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a deep residual network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

WiFi connection has been suffering from MAC forgery attacks due to the loose authentication mechanism between access points (APs) and clients. To address this problem, the physical (PHY) layer information-based fingerprint has been adopted for safe WiFi authentication. Since such a fingerprint is constant and unique for each specific network interface card (NIC), it can effectively prevent MAC forgery attacks. However, the PHY layer information-based fingerprint is still vulnerable to malicious attacks as it is extracted from Channel State Information (CSI), and its stability can be affected by the wireless environment. In this paper, we propose a novel undetectable attack framework, called PhyFinAtt, base on which the attacker can undermine the stability of the PHY layer-based authentication fingerprints through human movement and further attack the WiFi authentication protocols. Specifically, we first demonstrate that human movement at a designated location can affect the PHY fingerprint. We then illustrate the impact of human movement on the PHY fingerprint and the relationship between the movement and the channel quality to ensure that the PHY fingerprint is destroyed by the movement in an undetected way without affecting normal communication. Extensive experiments in real-world scenarios show that our proposed attack can effectively disrupt the stability of the PHY fingerprints and significantly degrade the performance of the authentication protocols based on such fingerprints. To the best of our knowledge, this is the first study on effective attacks against the PHY information-based WiFi authentication protocols. Furthermore, we also present a practical defense mechanism without involving any additional equipment to mitigate attacks similar to PhyFinAtt.

Good quality sleep is essential for good health and sleep monitoring becomes a vital research topic. This paper provides a low cost, continuous and contactless WiFi-based vital signs (breathing and heart rate) monitoring method. In particular, we set up the antennas based on Fresnel diffraction model and signal propagation theory, which enhances the detection of weak breathing/heartbeat motion. We implement a prototype system using the off-shelf devices and a real-time processing system to monitor vital signs in real time. The experimental results indicate the accurate breathing rate and heart rate detection performance. To the best of our knowledge, this is the first work to use a pair of WiFi devices and omnidirectional antennas to achieve real-time individual breathing rate and heart rate monitoring in different sleeping postures.

Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

The broad spectrum of applications of WiFi sensing technology, like gait and gesture recognition, has received widespread attention in recent years. Though most WiFi sensing systems may achieve impressive performance, the challenge lies in making good use of the amplitude and phase information of the Channel State Information (CSI) retrieved from commodity WiFi devices to carry out sensing tasks. To address this issue, we develop an attention-based framework to properly track the importance of amplitude and phase information to adaptively extract distinguishing features related to gestures. Specifically, we first use the CSI ratio instead of the original CSI as the basic signal, which not only eliminates most of the noise, but also contains the complete information of the CSI signal corresponding to human motion. Then, we use the self-attention module to learn the coarse attention weights of amplitude and phase information of the CSI ratio. Moreover, the relation-attention module is used to integrate features to further refine the attention weight. In this way, we proposed a framework that can adaptively learn distinctive feature representations and thus facilitate ubiquitous gesture recognition. Extensive experiments demonstrate the effectiveness of method for gesture recognition under various conditions on the open Widar3.0 dataset. The proposed method achieves 99.69% in-domain recognition accuracy, 96.95% cross-location recognition accuracy and 93.71% cross-orientation recognition accuracy, outperforming the state-of-the-art solutions.

With mobile phones becoming increasingly prevalent and embedding high-quality microphones, attackers have the ability to employ these microphones to eavesdrop user’s keyboard input. However, existing work usually assumes that keystroke eavesdropping is performed against known environments and victims, which inevitably makes attack systems lack generalization. To reveal the real threat of the acoustic signal-based attack strategy, this paper proposes a keystroke eavesdropping algorithm called KeystrokeSniffer , which is robust to unknown input environments and unknown victims. In particular, to mimic the real input environment of victims, an environment estimation algorithm is first designed by extracting the timbre-related characteristics to predict the keyboard type and identifying large-size key data from collected unlabeled samples to estimate the 3D microphone coordinates. Then, by imitating unknown environments and victim data, this algorithm achieves effective keystroke eavesdropping with a small training set. By further considering the commonalities of different keystroke habits, a robust feature extraction method that reflects the keystroke location is adopted to reduce the impact of individual input habits. Extensive experimental results using various commodity smartphones indicate that the scheme is capable of predicting keyboard input accurately under different unknown scenarios. Specifically, even when both the victims and keyboards are unknown, KeystrokeSniffer can still achieve high Top-5 accuracy, reaching 79:5% in predicting keystrokes and 96:7% in predicting meaningful words, which demonstrates KeystrokeSniffer has excellent generalization capabilities. By setting different parameter values of various impact factors, e.g., noise and hand length factors, the strong robustness of the system is demonstrated, which proves that KeystrokeSniffer can violate privacy in real situations.