Indoor tracking plays a critical role in a wide range of applications, yet existing solutions based on cameras, acoustics, or radar often face challenges related to privacy, deployment cost, and environmental sensitivity. WiFi-based methods offer a promising alternative by leveraging existing infrastructure, but most current approaches are active, requiring users to carry dedicated devices—limiting practicality in everyday scenarios. Passive WiFi tracking is more user-friendly, but existing solutions typically rely on complex feature engineering, require large training datasets, and struggle to generalize across different users and environments. In this work, we introduce a novel passive tracking system that requires only a single-shot training phase. By leveraging location signature based on statistical proximity metrics derived from CSI across multiple distributed WiFi devices, our method enables accurate, scalable, and training-efficient indoor tracking.

WiFi sensing has emerged as a compelling contactless modality for human activity monitoring by capturing fine-grained variations in Channel State Information (CSI). Its ability to operate continuously and non-intrusively while preserving user privacy makes it particularly suitable for health monitoring. However, existing WiFi sensing systems struggle to generalize in real-world settings, largely due to datasets collected in controlled environments with homogeneous hardware and fragmented, session-based recordings that fail to reflect continuous daily activity.We present CSI-Bench, a large-scale, in-the-wild benchmark dataset collected using commercial WiFi edge devices across 26 diverse indoor environments with 35 real users. Spanning over 461 hours of effective data, CSI-Bench captures realistic signal variability under natural conditions. It includes task-specific datasets for fall detection, breathing monitoring, localization, and motion source recognition, as well as a co-labeled multitask dataset with joint annotations for user identity, activity, and proximity. To support the development of robust and generalizable models, CSI-Bench provides standardized evaluation splits and baseline results for both single-task and multi-task learning. CSI-Bench offers a foundation for scalable, privacy-preserving WiFi sensing systems in health and broader human-centric applications.

Radio-frequency (RF)-based high-resolution human imaging is an emerging area of research fueled by the increasing availability of RF-radar devices. Even though existing works achieve accurate human body reconstruction for pose estimation purposes, human identification with imaging has not been feasible due to its limited resolution. In this work, we present high-resolution neural network (HRNet), a deep neural network based on conditional generative adversarial network architecture, to achieve high-resolution human silhouette images, which can be used for human identification. HRNet uses radar spatial spectrum generated using a modified multiple signal classification algorithm as input and is trained with Kinect images as ground truth. We tested our design using a commodity millimeter-wave radar device operating at 60 GHz. Experiments performed with 12 users in three different environments show that our proposed system can reconstruct human images with 4% mean silhouette difference when compared with Kinect images. Moreover, the system achieved an average classification accuracy of 90.6% for 12 users and 95.0% for seven users in unseen environments; thereby proving robustness to environment changes.

Channel State Information (CSI) is foundational for enabling advanced Wi-Fi sensing applications, yet its efficacy is significantly influenced by environmental factors, hardware variations, and noise. This paper introduces a structured framework designed to rigorously evaluate the quality of CSI, thereby enhancing the performance and reliability of Wi-Fi-based sensing systems. Our evaluation system features a multilayered pipeline, where the first layer assesses fundamental CSI characteristics, including packet loss and amplitude consistency over time, to verify data integrity, and the second layer evaluates the compatibility of CSI with specific applications, such as motion detection. Validation of our framework across various chipset samples demonstrates its utility in improving the accuracy and reliability of CSI-derived sensing and potential in refining data quality for data-driven approaches.

Addressing the pivotal challenge of discerning human and nonhuman activities in smart environments, in this demo, we present a system utilizing commercial WiFi transceivers for precise human and non-human motion differentiation through the walls. This system effectively filters non-human interference in smart home systems by extracting physically and statistically explainable features from ubiquitous WiFi signals. It passively recognizes moving subjects in real time without constraining their movement, even in complex environments. Tailored for edge computing, it ensures minimal resource consumption and generalizes well across various settings. Our long-term field tests confirm a high accuracy rate of 97.34% and a low false alarm rate of 1.75%, underscoring its robustness and readiness for practical deployment. Please find the companion video with the URL: https://youtu.be/6xkJZ_VvL9Q.

Device-free indoor object detection and localization are essential for the success of smart homes. Traditional vision/acoustic/radar-based approaches face operational constraints that limit their effectiveness and scalability. WiFi-based approaches have recently been a promising candidate due to their ubiquity, cost-effectiveness, and privacy-preserving nature. However, most of them show inadequate performance in typical residential settings due to the limited WiFi bandwidth and the resulting low spatial resolution. In this article, we introduce a novel system using commodity WiFi that can accurately determine the specific room where the person is, i.e., room-level localization. The system employs a novel multipath selection technique to concentrate on a limited set of multipaths predominated by the proximate motions to the device. Based on the technique, a spatial feature leveraging multiple antennas to enhance the spatial resolution is proposed for more refined detection coverage. Combining the spatial feature with time- and frequency-domain features, the system is shown to achieve an overall test accuracy of 87.63%, a true positive rate of 89.47%, and a positive predictive value of 88.51%, outperforming state-of-the-art methods by >20% and showing its potential for real-world applications.

Indoor intelligent perception systems have gained significant attention in recent years. However, accurately detecting human presence can be challenging in the presence of non-human subjects such as pets, robots, and electrical appliances, limiting the practicality of these systems for widespread use. In this paper, we propose a novel system (“WI-MOID") that passively and unobtrusively distinguishes moving human and various non-human subjects using a single pair of commodity WiFi transceivers, without requiring any device on the subjects or restricting their movements. WI-MOID leverages a novel statistical electromagnetic wave theory-based multipath model to detect moving subjects, extracts physically and statistically explainable features of their motion, and accurately differentiates human and various non-human movements through walls, even in complex environments. In addition, WI-MOID is suitable for edge devices, requiring minimal computing resources and storage, and is environment-independent, making it easy to deploy in new environments with minimum effort. We evaluate the performance of WI-MOID in five distinct buildings with various moving subjects, including pets, vacuum robots, humans, and fans, and the results demonstrate that it achieves 97.34% accuracy and 1.75% false alarm rate for identification of human and non-human motion, and 95.98% accuracy in unseen environments without model tuning, demonstrating its robustness for ubiquitous use.

Indoor target detection through motion sensing based on Wi-Fi signals has gained much attention recently. However, most of the existing motion detection approaches can only detect motion in a large coverage area without knowing the distance of the target motion from the transmitter (Tx)/receiver (Rx). Passive positioning techniques can provide the location of a target, which, however, requires high deployment efforts without robust performance. In this article, we present a novel technique for detecting motion in proximity by exploring the physics behind the indoor radio frequency (RF) multipath propagation. We discover that motion in the proximity of the Rx/Tx produces distinct time dispersion over the radio channel at the Rx/Tx side. By exploring two novel metrics and linking them with the distance of the motions to antennas, we are able to precisely distinguish motions in nearby proximity from the motions far away. Extensive experiments in various real-world scenarios demonstrate that the proposed scheme can achieve true positive rates (TPRs) greater than 95% and 99% in distance-based and room-level proximity detection, respectively, while maintaining the corresponding false positive rates (FPRs) less than 5% and 0.5%. The detection delays for a detection distance of 2 m are within 0.6 s, which verifies the responsiveness of the proposed scheme.

Recognizing in-air gestures can enable intelligent human–computer interaction (HCI) applications and facilitate human lives. However, existing sensor/camera-based methods for gesture recognition are either nonubiquitous, intrusive to privacy, or inconvenient to carry around. Contemporary device-free approaches require the person to be in the line of sight and proximity to the sensing device. This article shows that WiFi signals can recognize hand-drawn in-air gestures even when the gesture location is nonline-of-sight/beyond walls to the WiFi transceivers. The proposed GWrite system utilizes the channel state information (CSI) time-series information from commercial WiFi chipsets. GWrite employs a unique approach for performing hand gestures, thus enabling the design of a hand movement model. Using the model and the time-reversal (TR) technique, this work derives a correspondence between the similarity of CSIs and the relative distance moved by the hand. This relation gave rise to unique features, such as the number of segments, angle, and the intersection between segments that can classify a set of gesture shapes consisting of straight-line segments. GWrite achieved an accuracy of 92% on a group of 15 gestures. The proposed approach can be applied to a broader set of gestures, unlike the current systems that function over a limited gesture set.

Keyboard acts as one of the most commonly used mediums for human–computer interaction. Today, massive Internet-of-Things (IoT) devices are designed without a physical keyboard as they go tiny, but are almost all equipped with a wireless module for networks. In this work, we aim to enable a universal virtual keyboard using wireless signals, which would allow a typing interface for tiny IoT devices or serve as a portable alternative to the unwieldy physical keyboards. To this end, we presentmmKey, the first universal virtual keyboard system using a single millimeter-wave (mmWave) radio. By leveraging the unique advantages of mmWave signals,mmKeyconverts any flat surface, with a printed paper keyboard, into an effective typing medium.mmKeyenables concurrent keystrokes and supports multiple keyboard layouts (e.g., computer keyboard, piano keyboard, or phone keypad). We design a novel signal processing pipeline to detect, segment and separate, and finally, recognize keystrokes.mmKeydoes not need any training except for a minimal one-time effort of only three key-presses for keyboard calibration upon the initial setup. We prototypemmKeyusing a commodity 802.11ad/ay chipset, customized to support radar-like operations, and evaluate it with different keyboard layouts under various settings. Experimental results with ten participants demonstrate a keystroke recognition accuracy of >95% for single-key case and >90% for multikey scenario, which leads to a word recognition accuracy of >97%.

Fall is recognized as one of the most frequent accidents among elderly people. Many solutions, either wearable or noncontact, have been proposed for fall detection (FD) recently. Among them, WiFi-based noncontact approaches are gaining popularity due to the ubiquity and noninvasiveness. The existing works, however, usually rely on labor-intensive and time-consuming training before it can achieve a reasonable performance. In addition, the trained models often contain environment-specific information and, thus, cannot be generalized well for new environments. In this article, we propose DeFall, a WiFi-based passive FD system that is independent of the environment and free of prior training in new environments. Unlike previous works, our key insight is to probe the physiological features inherently associated with human falls, i.e., the distinctive patterns of speed and acceleration during a fall. DeFall consists of an offline template-generating stage and an online decision-making stage, both taking the speed estimates as input. In the offline stage, augmented dynamic time-warping (DTW) algorithms are performed to generate a representative template of the speed and acceleration patterns for a typical human fall. In the online phase, we compare the patterns of the real-time speed/acceleration estimates against the template to detect falls. To evaluate the performance of DeFall, we built a prototype using commercial WiFi devices and conducted experiments under different settings. The results demonstrate that DeFall achieves a detection rate above 95% with a false alarm rate lower than 1.50% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios with one single pair of transceivers. Extensive comparison study verifies that DeFall can be generalized well to new environments without any new training.

Motion detection based on WiFi signals has gained much attention recently. However, most of the existing approaches can only detect motion in a large coverage area without knowing how far the target motion happens. In this paper, we propose two robust and responsive features in the frequency dimension, which are sensitive to the distance of motion, and establish the connection between the underlying radio propagation properties and the features. Extensive experiments in various environments demonstrate that the proposed proximity detection scheme can achieve true positive rates greater than 90% and 98% in corridor and room scenarios, respectively, while maintaining the corresponding false positive rates less than 5% and 1%. The responsiveness of the proposed scheme is verified by measured detection delays within 1.5 s for a detection distance of 2 m.

Interests in monitoring and recognizing gait have surged significantly over the past decades. Traditional approaches rely on camera array, floor sensors (e.g., pressure mats), or wearables (e.g., accelerometers), none of which are suitable for continuous and ubiquitous everyday use. In this article, we present GaitWay, the first system that monitors and recognizes an individual's gait through the walls via wireless radios. GaitWay passively and unobtrusively monitors an individual's gait speed by a single pair of commodity WiFi transceivers, without requiring the user to wear any device or walk on a restricted walkway. On this basis, GaitWay automatically identifies stable walking periods, extracts physically plausible and environmentally irrelevant speed features, and accordingly recognizes a subject's gait. Built upon a distinct rich-scattering multipath model, GaitWay can capture one's gait speed when one is $>$ >10 meters away behind the walls. We conduct experiments in a typical indoor space and perform eight sessions of data collection with 11 subjects across six months, resulting in $>$ >5,000 gait instances. The results show that GaitWay achieves a median 0.12 m/s and 90%tile 0.35 m/s error in speed estimation, with a mean error of 3.36 cm in stride lengths. Further, it achieves a verification rate of 90.4% and a recognition rate of 81.2% for five users and 69.8% for 11 users, confirming its comfort and accuracy for continuous and ubiquitous use.

Fall detection systems based on WiFi signals are gaining popularity recently. However, most of the existing works relying on training are environment-dependent. In this paper, we propose DeFall, a novel WiFi-based environment-independent fall detection system by leveraging the features inherently associated with human falls - the patterns of speed and acceleration over time. The system consists of an offline template-generating stage and an online decision-making stage. In the offline stage, the speed of human falls is first estimated based on a statistical modeling about the Channel State Information (CSI). Dynamic Time Warping (DTW) based algorithms are applied to generate a representative template for typical human falls. Then fall event is detected in the online stage by evaluating the similarity between the patterns of realtime speed/acceleration estimates and the representative template. Extensive experiment results show that with a single pair of WiFi transceivers, the proposed system can achieve a detection rate of 96% and a false alarm rate smaller than 1.5% under both line-of-sight (LOS) and non-LOS (NLOS) scenarios.