mmWave radar-based human pose estimation is garnering increasing attention in Internet of Things (IoT) applications owing to its robustness under adverse lighting conditions and occlusion scenarios. However, accurately estimating human poses from mmWave signals remains challenging due to two key limitations: the lack of structural consistency when perceiving targets as a unified whole, and the absence of spatiotemporal consistency when tracking individual targets. To tackle these two challenges, this paper introduces RF-PoseR, a dedicated human pose rectifier for mmWave radar-based pose estimation. RF-PoseR serves as a corrective framework that refines the pose outputs generated by mmWave pose estimation systems. Based on the observation that perceptual inconsistencies in radar signals result in severely erroneous joint estimations, we introduce spatial and temporal constraints to enhance pose estimation accuracy. Specifically, RF-PoseR incorporates three core components: (1) unreliable joint removal guided by spatial structure, (2) pose completion leveraging temporal continuity, and (3) cross-modal pre-training utilizing large-scale visual pose datasets. Extensive experiments on mmWave radar pose datasets demonstrate that RF-PoseR significantly enhances the accuracy of poses generated by existing radar-based estimation networks. Furthermore, experiments on visual datasets confirm the method’s broader applicability beyond radar perception tasks.

WiFi indoor localization plays an important role in many real-world applications and has gained widespread attentions from both academia and industry over the past decade. While existing works have already achieved remarkable performance under various practical scenarios, they solely utilize multiple WiFi Access Points (APs) to achieve a higher accuracy and do not deeply explore the intrinsic relationships among them. To further unleash the potential of multiple APs and reach the limits of WiFi indoor localization, in this paper, we propose Qidi, a novel multi-APs collaboration based localization system. To the best of our knowledge, we are the first to explicitly reveal three underlying relationships among multiple spatially distributed APs, i.e., consistency, continuity, and non-uniformity. By combining these three basic principles with the unique characteristics of specific tasks, a series of long-lasting practical challenges, such as automatic phase offset calibration, bilateral angle ambiguity, elevation angle estimation with Uniform Linear Array (ULA) and Non-Line-of-Sight (NLoS), can be efficiently resolved. Extensive experiments in various complex environments are provided to demonstrate that Qidi can achieve 2.4°, 3.2° median errors of joint azimuth and elevation angle estimation, and 0.4mlocalization median error even in 20m×20mexhibition hall. Moreover, a one-month longitudinal evaluation conducted on a real-world deployed WiFi ISAC system further validates the effectiveness and robustness of the proposed localization system.

In recent years, Channel State Information (CSI), recognized for its fine-grained spatial characteristics, has attracted increasing attention in WiFi-based indoor localization. However, despite its potential, CSI-based approaches have yet to achieve the same level of deployment scale and commercialization as those based on Received Signal Strength Indicator (RSSI). A key limitation lies in the fact that most existing CSI-based systems are developed and evaluated in controlled, small-scale environments, limiting their generalizability. To bridge this gap, we explore the deployment of a large-scale CSI-based localization system involving over 400 Access Points (APs) in a real-world building under the Integrated Sensing and Communication (ISAC) paradigm. We highlight two critical yet often overlooked factors: the underutilization of unlabeled data and the inherent heterogeneity of CSI measurements. To address these challenges, we propose a novel CSI-based learning framework for WiFi localization, tailored for large-scale ISAC deployments on the server side. Specifically, we employ a novel graph-based structure to model heterogeneous CSI data and reduce redundancy. We further design a pretext pretraining task that incorporates spatial and temporal priors to effectively leverage large-scale unlabeled CSI data. Complementarily, we introduce a confidence-aware fine-tuning strategy to enhance the robustness of localization results. In a leave-one-smartphone-out experiment spanning five floors and 25, 600m2, we achieve a median localization error of 2.17 meters and a floor accuracy of 99.49%. This performance corresponds to an 18.7% reduction in mean absolute error (MAE) compared to the best-performing baseline.

Physical adversarial attacks (PAAs) pose a serious security threat to millimeter-wave (mmWave) radar systems used in safety-critical applications such as autonomous driving and security checking. These attacks, manipulating radar signals via specially crafted materials, are proven feasible and can cause severe sensing failures; however, effective defenses remain unexplored due to the difficulty of distinguishing adversarial examples from normal environmental objects. This paper presents mmGuard, a physics-based defense framework that addresses this challenge by exploiting a fundamental insight: the engineering process that makes materials adversarial inevitably creates detectable physical signatures. We identify three key domains where adversarial examples show artificial nature: spatial phase discontinuities, anomalous radar cross-section patterns, and violations of natural physico-kinematic relationships. mmGuard systematically captures these signatures through multi-domain feature extraction, enhances their discriminability via neural refinement, and enables efficient per-object attack detection and mitigation compatible with automotive radar update rates. To enable evaluation, we introduce mmAD, comprising over 110,000 annotated radar frames with diverse adversarial examples across realistic deployment scenarios. Experimental results demonstrate that mmGuard achieves over 90% detection accuracy while exhibiting strong in-distribution performance, with few-shot adaptation enabling calibration to unseen settings Case studies further validate that mmGuard can reliably defend against PAAs in real-world settings.

As RF sensing increasingly moves toward real-world deployments, critical concerns emerge around unauthorized model usage and access control. Existing protection approaches-such as watermarking-are typically reactive, task-specific, and ineffective at runtime. This paper presents AuthRF (Authentication with passports for RF sensing models), a novel signal-level passport mechanism that proactively enforces access control by mapping a user-specific passport to phase-compensation weights in the signal processing pipeline. Valid passports yield coherent phase alignment and high-fidelity representations, while invalid or forged ones induce phase distortion that significantly degrades model performance. This design effectively deters unauthorized access, supports scalable multi-user authentication, and enables personalized service provisioning through controlled passport variation. We evaluate AuthRF on six representative RF sensing tasks using both WiFi and radar signals. Experimental results demonstrate its robust protection capabilities and seamless integration with existing sensing pipelines, positioning AuthRF as a practical foundation for secure and commercial-grade RF sensing deployment.

Radar sensing has emerged as a promising solution for the contactless monitoring of Heart Rate Variability (HRV), a crucial indicator of the cardiovascular and autonomic nervous systems. However, due to signal noise and interference that easily obscure heartbeat details, along with variations in heartbeat across different physiological conditions, existing methods remain restricted to laboratory settings with healthy subjects and fail in real-world scenarios involving more complex physiological conditions. In this study, we propose a physiological prior-inspired deep learning framework for robust radar-based HRV monitoring. Specifically, we leverage the prior that internal heartbeats drive movements across the entire torso surface and design a hybrid deep neural network to model the spatio-temporal relationship between full-body radio reflections and heartbeats, effectively mitigating interference. Then, we incorporate the cardiac motion's self-similarity prior to establish a signal augmentation strategy, effectively remodeling the HRV distribution and enhancing performance across diverse physiological conditions. We build and validate our method on a large-scale dataset comprising 7,150 outpatients with complex physiological conditions in real-world scenarios. The experimental results demonstrate that our method achieves a mean IBI error of 19.21 ms, an RMSSD error of 16.23 ms, an SDSD error of 16.70 ms, and a pNN50 error of 7.28%. We further validate the performance by classifying five common cardiac conditions based on HRV results, demonstrating performance comparable to ECG-based methods. These results highlight the great potential of our approach for accurate, contactless HRV monitoring in real-world applications.

Sleep staging, crucial for diagnosing sleep disorders, requires precise recognition of physiological signals within 30-second epochs, a task fundamentally different from managing long-term semantic dependencies in natural language processing (NLP). Our model aims to refine the integration of local and global features for more accurate sleep stage classification. Following the American Academy of Sleep Medicine (AASM) guidelines, it focuses on rigorous intra-epoch feature extraction to ensure reliable identification of sleep stages. Moreover, our approach incorporates a global perspective by analyzing whole-night data, which is essential for handling transitional periods and ambiguities. Existing sequential modeling techniques often overlook the unique requirements of sleep staging, leading to performance declines when epochs extend beyond approximately 200. Our model addresses this by structurally processing local and global information and carefully balancing detailed intra-epoch analysis with an overarching view of sleep cycles through a gating mechanism. This gate mechanism selectively integrates long-term dependencies, optimizing the balance between local accuracy and global context. This approach represents a significant advancement over existing models, offering more accurate, reliable, and clinically relevant sleep staging. Extensive experiments on the SHHS, SleepEDF-20, and SleepEDF-78 datasets demonstrate that our method outperforms state-of-the-art approaches.

Arrhythmias are prevalent cardiac disorders affecting millions worldwide. By analyzing cardiac motion modulated in mmWave reflections, mmWave sensing is emerging as a promising contactless revolution in arrhythmia detection compared to conventional contact-based methods. However, the fundamental bottleneck of existing mmWave sensing methods is their restriction to controlled laboratory settings with small-scale cohorts, limiting generalization to real-world populations. This limitation arises because mmWave signals undergo complex signal transformations during propagation, resulting in an explosion of signal diversity across large populations in real-world scenarios. Such diversity significantly complicates the direct recognition of arrhythmia. In this paper, we theoretically analyze the mechanism and impact of mmWave cardiac signal diversity. Leveraging the inherent transformation properties of mmWave signals, we propose a Diversity-Invariant Contrastive mmWave Sensing framework, which learns invariant features robust to complex signal transformations encountered in real-world scenarios. We evaluate our method in a practical, clinically-oriented scenario involving a large-scale population of 7,338 subjects, achieving an average F1-score of 0.8241 across four common arrhythmias. These results demonstrate that our method effectively bridges the diversity gap, representing a significant step toward practical clinical deployment of contactless arrhythmia detection via mmWave sensing.

Millimeter-wave (mmWave) radar systems with large array have pushed radar sensing into a new era, thanks to their high angular resolution. However, our long-term experiments indicate that array elements exhibit phase drift over time and require periodic phase calibration to maintain high-resolution, creating an obstacle for practical high-resolution mmWave sensing with large array. Unfortunately, existing calibration methods are inadequate for periodic recalibration, either because they rely on artificial references or fail to provide sufficient precision. To address this challenge, we introduce AutoCalib, the first framework designed to automatically and accurately calibrate high-resolution mmWave radars by identifying Ambient Radio Anchors (ARAs)—naturally existing objects in ambient environments that offer stable phase references. AutoCalib achieves calibration by first generating spatial spectrum templates based on theoretical electromagnetic characteristics. It then employs a pattern-matching and scoring mechanism to accurately detect these anchors and select the optimal one for calibration. Extensive experiments across 11 environments demonstrate that AutoCalib is capable of identifying ARAs that existing methods miss due to their focus on strong reflectors. AutoCalib's calibration performance approaches corner reflectors (74% phase error reduction) while outperforming existing methods by 83%. Beyond radar calibration, AutoCalib effectively supports other phase-dependent applications like handheld imaging, delivering 96% of corner reflector calibration performance without artificial references.

The learning-based RF sensing methods typically involve signal processing to transform the RF signals into spectrograms, which are then input into neural networks. However, this approach is suboptimal due to the potential degradation caused by the inherent multi-path interference, resulting in blurred RF spectrograms. To tackle this challenge, we introduce a novelRefocusingTransformerbackbone customized forRFsensing (RF$^{2}$Transformer). Rather than attempting to precisely model interference using the traditional signal processing techniques, the RF$^{2}$Transformer utilizes a self-compensation mechanism to treat the interference-induced phase shifts as learnable parameters. This mechanism learns and compensates for the phase shift caused by interference in the complex feature space to obtain refocused and high-quality feature maps of RF spectrograms, thereby improving the performance of downstream tasks. We show that the RF$^{2}$Transformer is general for various RF sensing tasks by evaluating it on six typical RF sensing tasks using two general RF signals (WiFi and radar). Experimental results indicate that the RF$^{2}$Transformer takes an important step toward learning-based solutions for RF sensing.

Although radio frequency (RF) sensing offers a promising approach for monitoring human cardiac activity, it traditionally requires subjects to remain in a steady-state throughout the monitoring process to avoid motion artifacts—an inherently impractical constraint that hampers real-world adoption. However, existing methods either yield incorrect estimates from motion-affected segments or discard them entirely, leading to fragmented data and incomplete observations. To address the challenge, we introduce MaskSense, a novel framework designed to address motion interference in long-term monitoring. The key insight is that latent patterns within dynamic inter-beat interval (IBI) sequences allow for accurate heartbeat reconstruction from incomplete observations. Leveraging this insight, MaskSense treats motion-affected periods as ”masked” and steady-state periods as ”unmasked”, and employs a contrastive-learning-assisted masked modeling architecture to reconstruct the masked information. Our 400-hour evaluation with 18 participants confirms that MaskSense effectively recovers IBIs in the presence of motion artifacts, paving the way for more natural and unobtrusive RF-based cardiac activity monitoring.

The past few decades have seen significant advancements in WiFi indoor localization leveraging fine-grained Channel State Information (CSI). However, existing methods often require multiple access points (APs) or the target device to share sensing data. This cooperative localization complicates the deployment of practical localization systems. In this paper, we present SniFi, a non-cooperative localization method that seamlessly integrates with existing WiFi infrastructure. Unlike prior methods, SniFi does not require multiple CSI-capable APs or additional user actions. We use a single network interface card (NIC) to sniff WiFi traffic and obtain the angle information needed for localization. For APs that do not support CSI acquisition, we first utilize Beamforming Feedback Information (BFI) to estimate the Angle of Departure (AoD). Then, by analyzing the sniffed packets, we can obtain the CSI and the corresponding Angle of Arrival (AoA) from the target device to the sniffer. During experiments, we also address the challenge of angle ambiguity of the latest Intel AX210 NIC by integrating map constraints and temporal continuity. These techniques together enable us to achieve accurate localization without making changes to existing AP networks. Extensive evaluations across various environments and APs demonstrate that SniFi achieves decimeter-level accuracy in median error.

WiFi-based indoor localization serves as a critical foundation for numerous real-world applications and has attracted widespread attentions over the past decade. Recent advancements have demonstrated the feasibility of achieving decimeter-level accuracy by leveraging Angle of Arrival (AoA) information. However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performance of AoA-based methods. Previous works either relied on labor-intensive manual calibration or involved inaccurate and non-robust automatic calibration, which hinders their widespread use in large-scale deployments. Moreover, to expand the signal coverage and enhance communication performance, the inter-antenna spacing in existing commercial APs typically exceeds the standard half-wavelength. The resulting angle ambiguity problem can mislead target detection results, which has not been well resolved in existing works. To address the above two practical challenges, in this paper, we propose CAFE, a practical WiFi indoor localization system based on theContinuousAngleFocusingEffect. The key insight lies on the fact that the angle information of multiple APs originates from the same client, and thus exhibits highly convergent properties in both the temporal and spatial dimensions. By further exploring the binary nature of phase offset and the periodicity of grating lobes, our approach can efficiently resolve the above two practical challenges. Extensive experiments are provided to demonstrate the effectiveness of the proposed CAFE system, which outperforms state-of-the-art methods by$22.1\%$in median localization error for simple scenarios and by$37.1\%$for complex multipath scenarios.

Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stress levels and behaviors during the night poses a significant challenge in stress monitoring, which remains unsolved. In this paper, we propose mmWave Nighttime Stress monitoring (mmNS), a learning-based end-to-end framework for contactless nighttime stress monitoring. Specifically, this framework incorporates radar signal processing and a self-supervised physiological feature separation strategy, combined with a signal complexity-oriented network design, to effectively extract and encode periodic physiological features for accurate stress level classification. To evaluate the stress monitoring performance of mmNS, we collect a RF-based nighttime stress monitoring dataset, which contains stress data from 10 volunteers. The experimental results demonstrate that our method achieves state-of-the-art stress monitoring performance, about 76% accuracy and 72% F1-score in classifying low, medium and high stress. To our knowledge, this is the first attempt dealing with contactless nighttime stress monitoring.

Human-centric perception with radio frequency (RF) signals has recently entered a new era of end-to-end processing with Transformers. Considering the long-sequence nature of RF signals, the State Space Model (SSM) has emerged as a superior alternative due to its effective long-sequence modeling and linear complexity. However, integrating SSM into RF-based sensing presents unique challenges including the fundamentally different signal representation, distinct frequency responses in different scenarios, and incomplete capture caused by specular reflection. To address this, we carefully devise a dual-branch SSM block that is characterized by adaptively grasping the most informative frequency cues and the assistant spatial information to fully explore the human representations from radar echoes. Based on these two branchs, we further introduce an SSM-based network for handling various downstream human perception tasks, named RFMamba. Extensive experimental results demonstrate the superior performance of our proposed RFMamba across all three downstream tasks. To the best of our knowledge, RFMamba is the first attempt to introduce SSM into RF-based human-centric perception.

Radio sensing has emerged as a promising contactless revolution for cardiac monitoring. However, considering the complexity of radio propagation, extracting stable and clinically meaningful features remains challenging, posing a barrier to scaling this technology for practical and clinical reliable deployment. In this demo, we present RadioCardiogram, a system that leverages AI-powered knowledge transfer from well-established ECG diagnostic paradigms to accurately interpret complex radio signals. It enables all-in-one cardiac function monitoring including heart rate variability analysis, arrhythmia detection, and ECG-aligned waveform reconstruction. The system is implemented in a mobile phone-sized prototype and validated in a large-scale, clinically oriented cohort involving 6,258 outpatient visitors. Results demonstrate performance approaching the gold standard in both HRV monitoring and arrhythmia detection, highlighting a pathway toward effortless continuous, and reliable cardiac health coverage in real-world usage. The demo video is available at the following link.

By analyzing cardiac motion modulated in body reflections, radio signals offer a novel contactless pathway for heartbeat sensing, attracting growing research attention. However, current studies overlooked the unique signal interaction when radio signals are incident at non-normal directions to the torso surface. This missing component significantly limits the effectiveness and results in unreliable performance in practical usage, where normal sensing direction cannot always be guaranteed. In this paper, we aim to answer the questions of what causes this performance degradation and how to solve it. Specifically, we analyze the signal interaction using a fine-grained thoracic motion model and reveal that non-stationary interference, caused by physiologically-driven spatial variation of the body surface, is the key to the problem. Correspondingly, we propose OSense, a framework based on a time-domain optimization method to cancel the non-stationary interference. This framework can be seamlessly integrated into various heartbeat sensing tasks. We validate OSense using a commercial Frequency Modulated Continuous Wave radar across multiple downstream tasks. The results demonstrate that our method effectively eliminates interference and enables direction-robust heartbeat sensing, highlighting its potential for practical cardiac monitoring using radio signals.

The major challenge in learning-based RF sensing is acquiring high-quality large-scale annotated datasets. Unlike visual datasets, RF signals are inherently non-intuitive and non-interpretable, making their annotation both time-consuming and labor-intensive. To address this challenge, we propose RF-URL 2.0, a novel unsupervised representation learning (URL) framework for RF sensing, which enables pre-training on easily collected, large-scale unannotated RF datasets to make downstream tasks solve easier. Existing URL techniques, such as contrastive learning, are primarily designed for natural images and are prone to learn shortcuts rather than meaningful information when applied to RF signals. RF-URL 2.0 is the first framework to overcome these limitations by constructing positive and negative pairs through well-established RF signal processing algorithms. Besides, it introduces a novel signal-model-driven augmentation technique, which augments signal representations by identifying and perturbing physically meaningful parameters of signal processing models. Moreover, the RF-URL 2.0 is carefully designed to take into account the heterogeneity characteristics of different RF signal processing representations. We show the universality of RF-URL 2.0 in three typical RF sensing tasks using two general RF devices (WiFi and radar), including human gesture recognition, 3D pose estimation, and silhouette generation. Extensive experiments on the HIBER and WiDAR 3.0 datasets demonstrate that RF-URL 2.0 takes a significant step toward learning-based solutions for RF sensing.

Millimeter-wave (mmWave) radar imaging has shown remarkable potential in critical applications. While previous researches have explored attacks on high-level radar perception, the vulnerability of low-level radar imaging to adversarial attacks remains largely unexplored. In this work, we introduce mmHide, the first general attack framework on mmWave radar imaging that utilizes neural rendering of meta-materials to hide imaging targets (e.g., handguns). mmHide’s novelty lies in its three-fold approach: (1) an implicit neural rendering network that efficiently represents and optimizes complex 3D meta-material structures, (2) an explicit differentiable forward imaging model that provides physical constraints, and (3) a self-supervised learning strategy that iteratively refines the meta-material design. This unique combination enables mmHide to create an “invisible cloak” for target objects while maintaining plausible imaging results. Extensive real-world experiments demonstrate mmHide’s effectiveness in significantly reducing target visibility while preserving background similarity. A user study confirms its high success rate in deceiving human observers, outperforming existing methods. These findings not only showcase the potential of our approach but also underscore the urgent need for robust defense mechanisms in mmWave imaging systems.

Radio sensing has emerged as a promising solution for monitoring vital signs in a contactless manner. However, most of the existing designs focus on stationary target and struggle with body motion interference. While some efforts have been made to address this issue, the lack of a physical explanation for the motion elimination principle makes them work as a blind signal separation way and thus leaves the body motion elimination problem still as an open challenge. In this paper, we reveal for the first time the existence of “dark pixels”–specific points on the same rigid body parts that share the same body movement but exhibit varying physiological motions, with these variations still preserving the physiological rhythm. By exploiting the inherent relationship between the dark pixels, we propose a cooperative sensing framework, Co-Sense, that can achieve robust radio sensing for non-stationary targets in an explainable way. Through extensive experiments, Co-Sense demonstrates its superiority over existing methods, achieving effective motion cancellation and breath sensing with a median absolute respiratory rate (RR) error of 0.36 respiration per minute (RPM) and breath wave correlation of 0.61 under non-stationary scenarios. The results indicate the great potential of Co-Sense in enhancing the accuracy of vital sign sensing with radio signals, especially in real-world environments where targets are rarely stationary.

Recent advancements have showcased the potential of handheld millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve robust imaging and focusing for handheld mmWave systems. We first formulate the handheld imaging model by integrating multiple priors about mmWave images and handheld phase errors. Furthermore, we transform the optimization processes into an iterative network structure for improved and efficient imaging performance. Extensive experiments demonstrate that IFNet effectively compensates for handheld phase errors and recovers high-fidelity images from severely distorted signals. In comparison with existing methods, IFNet can achieve at least 11.89 dB improvement in average peak signal-to-noise ratio (PSNR) and 64.91% improvement in average structural similarity index measure (SSIM) on a real-world dataset.

Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integratinggroupshuffle andasymmetryaugmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.

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Human tracking plays a crucial role in various wireless sensing applications. However, recent advancements have primarily focused on constrained experimental scenarios with less interference, often involving a few individuals performing actions in an empty space without obstacles. In empirical unconstrained scenarios, such as daily office scenes, severe interference and attenuation caused by chaotic environments is inevitable which results in dramatic performance degradation. In this paper, we introduce TBDNet, which incorporates tracking-before-detect (TBD) from conventional signal processing into learning-based models, achieving impressive tracking performance in unconstrained scenarios. TBDNet follows first-track-then-detect pipeline. It maps input heatmap sequence into high-level frame-wise features to adapt the time-varying intensity distribution and motion pattern of targets. After that, the temporal information is accumulated in feature space to obtain trace proposals. We then predict the accurate positions and probability of traces at each timestamp. To assess the efficiency of TBDNet, we collect and release the first RF-UNIT (RF-based Unconstrained Indoor Tracking) dataset, which comprises 4,030,880 radar heatmaps and the corresponding tracking annotations under 6 different scenarios. To our knowledge, RF-UNIT is the first dataset for RF-based human tracking in unconstrained scenes. We anticipate that TBDNet and the RF-UNIT dataset will significantly contribute to the advancement of RF-based sensing technologies.

Millimeter-wave (mmWave) radar sensing powered by deep learning is now emerging in numerous applications, which are predominantly trained in a supervised manner. However, due to the non-interpretable nature of mmWave signals, labeling the radar data has always been a difficult task. While there have been investigations on unsupervised pre-training for mmWave radar sensing, these methods are tailored to specific signal representations. In this paper, we propose UMIMO, an unsupervised learning framework combining the hardware nature of MIMO radar and deep learning techniques to resolve the challenge raised by the insufficient labeled data. UMIMO leverages the antenna arrays synthesized from multiple transmitting and receiving antennas in mmWave radar to construct positive samples for contrastive learning. To achieve this, we propose the constraints on angular resolution and grating lobes to generate effective signal representations with different synthetic arrays. We conduct experiments using UMIMO on three tasks: contactless ECG monitoring, 3D human pose estimation, and human silhouette generation. All experimental results demonstrate that UMIMO can effectively improve the performance of learning-based mmWave radar sensing in an unsupervised manner.

Intelligent Reflecting Surface (IRS) has emerged as a crucial technology for indoor localization in future wireless networks. However, existing IRS-aided systems that utilize commercial off-the-shelf (COTS) WiFi devices face challenges due to two key factors. First, the passive reflective nature of IRS generates relatively weak reflected signals, which can be easily drowned out by multipath interference and noise. Secondly, existing works require a large number of IRS codebooks for fine-grained scanning of the entire area, resulting in significant time requirements. This paper introduces SRAL-IRS, which enables swift, robust, and accurate IRS-aided indoor localization using commercial WiFi devices. Through intelligent codebook design and theoretical derivation, we reveal the theoretical relationship between the IRS codebook modifications, target position, and variations in the received signal. Subsequently, SRAL-IRS mitigates the impact of environmental and hardware noise by carefully selecting and combining WiFi subcarriers. Finally, by utilizing the received data from multiple codebooks and subcarriers and leveraging the orthogonality between the noise subspace and the IRS-reflected signal subspace, we can achieve precise localization even with a limited number of IRS codebooks. Real-world implementation of SRAL-IRS, utilizing our designed IRS prototype and COTS WiFi devices, validates its feasibility and effectiveness.

Contactless vital signs estimation using mmWave radar has gained significant attention. However, existing studies are built upon the radar being directed facing the thorax to capture fine-grained vital signs, ignoring the angle variation between the radar and thorax in practical deployment. In this paper, we propose a spatial-temporal optimization model to estimate the human body orientations between the radar and thorax through extracting the multi-domain features of reflected signal. By aligning the signal variation captured from different angles, we can realize orientation-free vital sign sensing. The system achieves an average angle estimation error of 13.1°, and a 14.8% discrepancy reduction in terms of the mean absolute error of the signal captured at different angles.

Fall detection using Radio Frequency (RF) signals with deep learning has exhibited significant promise in recent years. However, the costly collection of RF data with falls has hampered the performance of existing methods. While there has been approaches which can generate RF signals using various simulation methods, they rely on human-body modeling based on other modalities. Moreover, the realism of the generated signals is insufficient because these approaches cannot accurately capture the human radar cross section (RCS). In this paper, we propose SimFall, which generates simulated data for RF-based fall detection without overhead for data collection. SimFall first simulates the fall process by manipulating the human body mesh based on practical fall model. Then a grid shooting and bouncing ray (SBR) method is utilized to calculate the accurate RCS. Finally, SimFall computes the original signal and transforms it into different forms that reveal the features of falls. The experimental results demonstrate that the data produced by SimFall effectively enhances the accuracy of the RF-based fall detection network.

Recent advancements have showcased the potential of hand-held millimeter-wave (mmWave) imaging, which applies synthetic aperture radar (SAR) principles in portable settings. However, existing studies addressing handheld motion errors either rely on costly tracking devices or employ simplified imaging models, leading to impractical deployment or limited performance. In this paper, we present IFNet, a novel deep unfolding network that combines the strengths of signal processing models and deep neural networks to achieve imaging and focusing for handheld mmWave systems. By integrating multiple priors and mapping the optimization processes into an iterative network structure, IFNet effectively compensates for phase errors and recovers high-fidelity images from severely distorted signals. Extensive experiments demonstrate that IFNet outperforms state-of-the-art methods, both qualitatively and quantitatively.

Radar sensing has been a promising solution for contactless monitoring of Heart Rate Variability (HRV), an essential indicator of the cardiovascular and autonomic nervous systems. However, existing works neglect heartbeat-driven body surface motions spreading across the entire body with spatial variations, which limits their accuracy in identifying fine-grid consecutive heartbeat timings and overall HRV performance. In this paper, we propose to exploit the entire body reflections and model the inherent spatial-temporal relationship between these reflections and heartbeats by deep neural network for contactless HRV monitoring. Specifically, a hybrid convolution-transformer-based network is designed to convert the complex multi-dimensional spatial-temporal modeling problem into an efficient sequence modeling process. Experimental results demonstrate its superiority over the baseline method, achieving the median IBI estimation error of 12ms (w.r.t. 98.47% accuracy), RMSDD error of 7.3ms, SDRR error of 2.9ms, pNN50 error of 5.5%.

Intrusion detection acts as a key to in-home security, where WiFi-based systems have gained wide attention due to the ubiquitous nature of WiFi signals. While existing methods achieve impressive performance in specific environments, they are susceptible to environmental changes, especially for complex scenarios where outdoor human activities can be mistaken as intrusions. In this paper, we propose RoFi, a robust WiFi intrusion detection system which can handle more complex scenarios. It achieves this by exploring the distribution of autocorrelation function (ACF) of Channel State Information (CSI) when intrusion occurs, where likelihood ratio testing is employed to discriminate intrusion and non-intrusion scenarios, eliminating the variance of different environments. Without complex calibration, RoFi achieves an accuracy of over 97.5% in practical deployment, outperforming existing methods.

Millimeter-wave (mmWave) radar has gained increasing attention in environmental perception due to its robustness under low-light conditions. However, existing methods fail to address the challenges of multipath interference and low angle resolution. In this paper, we introduce DiffRadar which leverages the diffusion probabilistic model (DPM) for high-quality mmWave environmental sensing. To adapt DPM for radar signals that lack pix-level structural information, we design a contour encoder to capture intrinsic scene features that enable the DPM to learn a robust representation from radar data. Then the DPM decoder utilizes this high-level semantic information to effectively reconstruct real-world scene distribution. Extensive experiments have demonstrated that our approach surpasses state-of-the-art methods in various complex scenarios.

The mutual interference mitigation between identical or similar radar systems in autonomous driving has gained wide spread attention from both academia and industry. The resulted ghost target interference will reduce the sensitivity of the radar sensor and increase the false alarm rate. To tackle this problem, in this paper, we make full use of two characteristics of interference to achieve ghost target interference mitigation in the Doppler domain. The key insight lies in the fact that the interference is one-way propagation, and thus the resulted ghost target can be converted to the noise floor in the Doppler domain through random slow-time coding. Moreover, the high power characteristic of interference allows us to further enhance the interference mitigation performance by adopting a signal-to-interference-plus-noise ratio (SINR) maximization principle. Numerical examples are provided to demonstrate the effectiveness of the proposed interference mitigation approach.

Recent advancements in WiFi indoor localization have demonstrated the potential for achieving decimeter-level accuracy based on Angle of Arrival (AoA). However, existing commercial WiFi Access Points (APs) suffer from phase offset across different antennas, which significantly degrade the performance of AoA-based methods in practical deployment. Previous work either relied on labor-intensive manual calibration or involved inaccurate and non-robust automatic calibration. In this paper, we propose AutoCali, an accurate and robust automatic phase offset calibration system. The key insight is to utilize the binary nature of phase offsets and the property that triangulation exhibits higher convergence when the correct combination of phase offsets is employed. Extensive experiments demonstrate that AutoCali outperforms state-of-the-art methods by 22.1% in median localization error for simple scenarios and by 37.1% for complex multipath scenarios.

Intelligent reflecting surfaces (IRS) is a novel integrated sensing and communication technology that can manipulate the propagation of wireless signals. However, existing IRS-based sensing systems require directional antennas for signal transmission, incompatible with commercial WiFi devices. This paper proposes an IRS-aided localization system using omnidirectional antennas and reveals two critical challenges in practical deployment. First, the accurate distance between the IRS and the transmitter is needed for IRS codebook design, but practice measurements invariably introduce centimeter-level bias, which seriously affects localization accuracy. We derive a linear relationship between measurement bias and localization error for calibration. Second, only relative IRS phase change under different bias voltages can be measurable, not the absolute phase offset, introducing an unknown fixed phase offset in reflections. We solve this challenge by eliminating the signals that are not related to the IRS. Experiments validate the proposed calibration techniques, proving that our system achieves high-precision passive localization.

This paper introduces a novel paradigm for learning-based RF sensing, termed Pre-training RF signals In Sparsity-aware Masked autoencoders (PRISM), which shifts the RF sensing paradigm from supervised training on limited annotated datasets to unsupervised pre-training on large-scale unannotated datasets, followed by fine-tuning with a small annotated dataset. PRISM leverages a carefully designed sparsity-aware masking strategy to predict missing contents by masking a portion of RF signals, resulting in an efficient pre-training framework that significantly reduces computation and memory resources. This addresses the major challenges posed by large-scale and high-dimensional RF datasets, where memory consumption and computation speed are critical factors. We demonstrate PRISM’s excellent generalization performance across diverse RF sensing tasks by evaluating it on three typical scenarios: human silhouette segmentation, 3D pose estimation, and gesture recognition, involving two general RF devices, radar and WiFi. The experimental results provide strong evidence for the effectiveness of PRISM as a robust learning-based solution for large-scale RF sensing applications.

Advanced human sensing technologies based on radio frequency (RF) signals have gained widespread attention in recent years. However, due to the sparsity and incompleteness of RF signals, fine-grained RF-based multi-person 3D pose estimation has progressed more slowly. In this paper, we present RF-based Pose Machine (RPM 2.0) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we treat the horizontal and vertical millimeter-wave radars as “RF cameras” with different viewing angles and propose a Multi-view Fusion Network to unproject the RF signals into a unified latent feature space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RPM 2.0 and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RPM 2.0 outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of$73 mm$and generalizes well in new data such as occlusion, low illumination.

Millimeter-wave (mmWave) radar pointcloud offers attractive potential for 3D sensing, thanks to its robustness in challenging conditions such as smoke and low illumination. However, existing methods failed to simultaneously address the three main challenges in mmWave radar pointcloud reconstruction: specular information lost, low angular resolution, and severe interference. In this paper, we propose DREAM-PCD, a novel framework specifically designed for real-time 3D environment sensing that combines signal processing and deep learning methods into three well-designed components to tackle all three challenges: Non-Coherent Accumulation for dense points, Synthetic Aperture Accumulation for improved angular resolution, and Real-Denoise Multiframe network for interference removal. By leveraging causal multiple viewpoints accumulation and the "real-denoise" mechanism, DREAM-PCD significantly enhances the generalization performance and real-time capability. We also introduce RadarEyes, the largest mmWave indoor dataset with over 1,000,000 frames, featuring a unique design incorporating two orthogonal single-chip radars, Lidar, and camera, enriching dataset diversity and applications. Experimental results demonstrate that DREAM-PCD surpasses existing methods in reconstruction quality, and exhibits superior generalization and real-time capabilities, enabling high-quality real-time reconstruction of radar pointcloud under various parameters and scenarios. We believe that DREAM-PCD, along with the RadarEyes dataset, will significantly advance mmWave radar perception in future real-world applications.

The electrocardiogram (ECG) has always been an important biomedical test to diagnose cardiovascular diseases. Current approaches for ECG monitoring are based on body attached electrodes leading to uncomfortable user experience. Therefore, contactless ECG monitoring has drawn tremendous attention, which however remains unsolved. In fact, cardiac electrical-mechanical activities are coupling in a well-coordinated pattern. In this paper, we achieve contactless ECG monitoring by breaking the boundary between the cardiac mechanical and electrical activity. Specifically, we develop a millimeter-wave radar system to contactlessly measure cardiac mechanical activity and reconstruct ECG without any contact in. To measure the cardiac mechanical activity comprehensively, we propose a series of signal processing algorithms to extract 4D cardiac motions from radio frequency (RF) signals. Furthermore, we design a deep neural network to solve the cardiac related domain transformation problem and achieve end-to-end reconstruction mapping from RF input to the ECG output. The experimental results show that our contactless ECG measurements achieve timing accuracy of cardiac electrical events with median error below 14ms and morphology accuracy with median Pearson-Correlation of 90% and median Root-Mean-Square-Error of 0.081mv compared to the groudtruth ECG. These results indicate that the system enables the potential of contactless, continuous and accurate ECG monitoring.

While deep learning-based RF perception has received significant attention in recent years, the requirement for massive labeled RF data has hindered its further advancement. Despite existing efforts in synthesizing signals, they fail to accurately calculate the Radar Cross Section (RCS) of the target, leading to less practicality of the synthesized signals. In this paper, we introduce Simulated Body Radio Frequency (SBRF), a novel signal synthesis framework for calculating more realistic RCS by combining ray tracing with electromagnetic computation. SBRF involves three key components: a grid-based Shooting and Bouncing Ray (SBR) algorithm to calculate fine-grained human body RCS, a novel ray partitioning algorithm to improve the efficiency of ray tracing, and a coordinate transformation method to sense moving targets. Furthermore, we also design unique data augmentation techniques to improve the efficiency and generalizability of signal synthesis. Extensive experimental evaluations conducted on two publicly available datasets, involving wide-scale activity recognition and fine-grained gesture recognition, demonstrate the effectiveness of SBRF-generated signals in improving RF perception performance and alleviating the challenge of RF data collection.

WiFi-based indoor localization has gained widespread attention due to the pervasive availability of WiFi Access Points (APs). While signal processing-based methods can achieve decimeter-level localization, their performance is constrained by the limited spatial resolution of WiFi systems, especially in complex environments with strong interference. By contrast, deep learning-based methods have achieved impressive performance even in complex environments, which however often fail to generalize to new environments. In this paper, we propose a novel framework to learn domain-invariant model for WiFi-based indoor localization, which maintains impressive performance across different environments. The key insight is to design a deep learning-based WiFi localization system through the perspective of signal processing. Specifically, we let the neural network estimate APs-centered polar coordinates to avoid fitting the coordinates of APs strongly correlated with the environment, enabling us to obtain the domain-invariant model. To unleash the potential of neural networks in regressing high-precision parameters, we design a beamforming layer to integrate the knowledge of signal processing. Furthermore, we propose a multi-task learning scheme to further improve localization accuracy. Extensive experiments on diverse datasets have demonstrated that the localization performance of our method outperforms state-of-the-art methods and demonstrates superiority under cross-domain conditions.

WiFi-based respiration sensing technology has gained increasing attention due to its contactless sensing capabilities and utilization of existing WiFi devices. However, existing studies are limited to certain scenarios without addressing the motion interference from other individuals. In this paper, we tackle the challenge of robust respiration sensing in the presence of other individuals. Specifically, through an in-depth examination of the correlation between respiratory signals and spatial beam patterns, we develop a respiratory-energy based approach to evaluate the diverse impact of dynamic interference on respiratory signals. When significant interference is detected, we employ a convex-optimization-based beam control strategy, which exploits the inherent characteristics of human respiration, to adaptively adjust the spatial beam pattern. This approach enables a robust and precise gain adjustment between the target and interfering individual, effectively mitigating the impact of interference. Experimental results demonstrate that our approach can reduce the mean absolute error (MAE) of respiration detection by up to 32% compared to state-of-the-art methods, significantly enhancing the accuracy and robustness of WiFi-based respiration sensing.

Millimeter-wave (mmWave) radar has been widely used in wireless sensing due to its non-contact nature, privacy preservation, and immunity to adverse lighting conditions. However, with more and more mmWave radars working in the same frequency band, mutual device interference among them is inevitable and has become a serious problem. The device interference reduces the signal-to-interference-plus-noise ratio (SINR) and significantly degrades the detection performance. Existing works mainly focus on the vital sign monitoring in different practical scenarios (e.g., device movement, human movement, multi-person interference, and in-car scenario), and the vital sign monitoring in the presence of mutual device interference is still not well resolved. In this paper, we propose a novel interference mitigation framework, iSense, to enable radar vital sign sensing under device interference. By exploiting one-way propagation characteristic of device interference, iSense can effectively detect and suppress the interference. We evaluate iSense under a variety of complex device interference scenarios, including different distances, angles, and numbers of aggressor radars, as well as the impact of different environments. Experimental results show that the accuracy of respiration and heartbeat estimation of iSense can reach over 99.2% and 98.6%, indicating that iSense takes an important step towards the practical development of radar sensing.

Indoor localization is a fundamental task to many real-world applications, which however remains unresolved, especially with commodity WiFi Access Points (APs). In this paper, we tackle this problem and propose an accurate, robust, and real-time indoor localization system that can be directly deployed on commodity WiFi infrastructure. Specifically, the proposed system makes three key contributions: 1) we introduce a non-parametric metric to measure the accuracy of Angle of Arrival (AoA) estimation; 2) we are the first to explicitly consider the relationship among the AoAs of different APs and propose a multiple APs co-localization algorithm to exploit such a relationship to improve the localization performance; 3) we propose several strategies to reduce the computational complexity of our system to achieve real-time localization. Extensive experiments are conducted to evaluate the performance of the proposed system under various situations, which demonstrate that the proposed system can achieve a 4 degrees median error of AoA estimation and 30 cm localization median error, outperforming the state-of-the-art systems.

Intelligent reflecting surface has gained significant attention for supporting integrated sensing and communication (ISAC) by manipulating wireless signals. However, existing IRS-based sensing systems commonly utilize directional horn antennas for signal transmission, which is incompatible with commercial WiFi devices and contradicts the concept of ISAC. In this paper, we propose an IRS-aided localization system with omnidirectional antennas to overcome these limitations. We reveal two critical challenges for implementing such a system. First, precise distance between the IRS and transmitter is requisite for IRS codebook design, yet practical measurement introduces centimeter-scale biases, causing severe localization errors. We derive a linear relationship between measurement bias and localization error, which serves as the basis for a calibration method. Second, in practice, we can only obtain the relative phase change of the IRS under different bias voltages, but cannot measure the phase offset introduced by IRS at zero voltage. Consequently, an unknown fixed offset appears when calculating the channel response of the signal reflected by the IRS, which destroys subsequent IRS codebook design. We resolve this problem by eliminating the signals that are not related to the IRS. Extensive experiments validate the proposed calibration techniques and demonstrate that our system achieves accurate passive localization.

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Feature Fusion Network (FFN) is designed to effectively fuse radio signals from horizontal and vertical planes based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons from the sparse and incomplete RF signals. Specifically, a spatial attention module is designed to model non-local relationships among joints and reconstruct body parts that a single RF snapshot cannot capture. Afterwards, a temporal attention module is proposed to refine 3D pose based on temporal coherency learned from frame queries. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3d skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of$5.71 cm$and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.

Existing RF-based human pose estimation methods usually require intensive computations and cannot meet the real-time processing and portability requirements for mobile devices. To tackle the limitation, in this article, we introduce a lightweight RF-based pose estimation model, i.e., MobiRFPose, to construct the portable RF-based pose camera. Different from traditional optical-based cameras, the RF-based camera does not capture visual information, which means the privacy-preserving characteristic. Specifically, we only utilize a horizontal antenna array to transceive RF signals, then estimate the human locations on the RF signal heatmap and crop the human location regions, and finally estimate the fine-grained human poses based on the cropped small RF signal heatmaps. To evaluate the performance, we compare MobiRFPose with state-of-the-art methods. Experimental results demonstrate that MobiRFPose can achieve accurate 3D human pose estimation with fewer parameters and computations. We also test the trained MobiRFPose model using mobile computing devices, where the model structures and parameters only take up 268 KB and 3226 KB of disk space, and MobiRFPose can achieve 66 FPS processing speed. The pose estimation error is 11.05 cm in the case of a single person and 11.29 cm in the case of multiple people. All experimental results indicate that our proposed method can construct a portable RF camera to estimate human poses accurately.

In this paper, we present RF-based Multi-view Pose machine (RF-MvP) for multi-person 3D pose estimation using RF signals. Specifically, we first develop a lightweight anchor-free detector module to locate and crop regions of interest from horizontal and vertical RF signals. Afterward, we propose a Multi-view Fusion Network to unproject the RF signals from the horizontal and vertical millimeter-wave radars into a unified latent space, and then calculate the correlation for weighted fusion. Finally, a Spatio-Temporal Attention Network is designed to reconstruct the multi-person 3D skeleton sequences, in which the spatial attention module is proposed to recover invisible body parts using non-local correlations among joints and the temporal attention module refines the 3D pose sequences using temporal coherency learned from frame queries. We evaluate the performance of the proposed RF-MvP and state-of-the-art methods on a large-scale dataset with multi-person 3D pose labels and corresponding radar signals. The experimental results show that RF-MvP outperforms all of the baseline methods, which locates multi-person 3D key points with an average error of 73mm and generalizes well in new data such as occlusion, low illumination.

Toxic gases inhalation is the most common cause of death in fire scenes, which can make people unconscious and unable to save themselves. Hence, discovering the unconscious victims is crucial to improve their survival rate. In this paper, we propose RF-Search, a victim searching system with RF device mounted on the drone. The challenge mainly comes from the fact that drone motion would overwhelm the subtle vital signs utilized for victim identification. To resolve this problem, we have noted that the physical signature of drone motion has been encoded in stationary object reflections. Leveraging this unique physical signature, we propose to identify the unconscious victim through the spatio-temporal correlation between signals reflected from the victim and the surrounding stationary objects. To extract respiration information of the victim, we propose a motion segmentation module and a motion compensation module to suppress the signal variation caused by drone movement. Extensive experiments have demonstrated that our system could achieve an accuracy of 92.5% for victim identification.

The past decade has witnessed emerging applications of breath monitoring using off-the-shelf WiFi devices owing to their low-cost, non-intrusive, and privacy-friendly characteristics. While existing works have achieved promising results in certain scenarios, the performance degradation introduced by the interfering person who moves around the target user has not been fully investigated, which hinders practical applications of WiFi-based breath sensing. In this paper, we propose a robust respiration sensing system with WiFi which could achieve accurate respiration sensing under strong interference. To achieve this, we first design a 2-D Capon beamformer to maximize the signal-to-interference-plus-noise ratio (SINR). Then, the interfering user’s trajectory is estimated through spatial-temporal processing. Finally, we design a respiration extracting algorithm based on the constraint of the interferer’s trajectory and breath energy to find the optimal position to extract breath signals. Extensive experimental results show that the proposed framework can reduce the Mean Absolute Error (MAE) of breath rate estimation by up to 48% compared with the existing state-of-the-art methods, which demonstrates the superior robustness and effectiveness of our system.

Human gesture recognition with WiFi signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of WiFi signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this paper, we propose UDAWiGR, an unsupervised domain adaptation framework for WiFi-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose a pseudo-labeling method with confidence control constraint to utilize unlabeled data for model training. We then utilize consistency regularization to align the output distribution for enhancing the robustness of neural network under signal perturbations. Furthermore, we propose a cross-match loss to combine the pseudo-labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% accuracy improvement comparing with the state-of-the-art methods on public dataset.

The past years have witnessed increasing research interests in achieving passive human localization using WiFi signals. However, due to the limited spatial resolution of WiFi devices, it is still difficult to achieve accurate localization with existing WiFi infrastructures. To tackle this problem, in this paper, we propose a RIS-aided passive localization frame-work, which exploits the degree of freedom provided by the Reconfigurable Intelligent Surface (RIS) to achieve accurate localization. We have noted that RIS is composed of a large number of controllable reflective elements, which can break through the resolution limitation of commodity WiFi devices without making any changes to existing infrastructures. Hence, we propose a phase control optimization algorithm that can maximize the discrepancy between human reflection and multi-path interference. In order to solve the near-far effect in multi-person scenario, we propose a Side-lobe Cancellation Algorithm to separate the reflected signals of different people and achieve accurate localization. Extensive simulation results demonstrate the proposed framework is capable of locating the moving persons passively with sub-centimeter accuracy in the presence of noise and multi-path interference.

The past decade has witnessed emerging applications of achieving indoor localization using WiFi by estimating the Angle of Arrival (AoA). While various algorithms have been proposed, their performance in practical indoor environment is still limited. An important limitation lies in the fact that the confidences of AoA estimations from different devices are actually unequal, which has not been considered nor addressed. In this paper, we propose WiCo, a confidence-aware localization framework by analyzing the confidence of the spatial spectrum. Specifically, we first evaluate the unequal confidence caused by different beamwidth and multipath on different APs. Then, we propose a normalized distribution confidence and a full reference confidence to quantify the reliability of spatial spectrum on different devices. Finally, we resolve the unequal confidence factor caused by geometric deployment through re-weighting and perform localization. Extensive real-world experiments demonstrate that WiCo could reduce the median localization error by 43.8%.

WiFi-based imaging enables pervasive sensing in a privacy-preserving and cost-effective way. However, most of existing methods either require specialized hardware modification or suffer from the poor imaging performance due to the fundamental limit of off-the-shelf commodity WiFi devices in spatial resolution. We observe that the recently developed reconfigurable intelligent surface (RIS) could be a promising solution to overcome these challenges. Thus, in this article, we propose an RIS-aided WiFi imaging framework to achieve high-resolution imaging with the off-the-shelf WiFi devices. Specifically, we first design a beamforming method to achieve the first-stage imaging by separating the signals from different spatial locations with the aid of the RIS. Then, we propose an optimization-based super-resolution imaging algorithm by leveraging the low-rank nature of the reconstructed object. During the optimization, we also explicitly take into account the effect of finite phase quantization in RIS to avoid the resolution degradation due to quantization errors. Simulation results demonstrate that our framework achieves median root-mean-square error (RMSE) of 0.03 and median structural similarity (SSIM) of 0.52. The visual results show that high-resolution imaging results are achieved with simulation signals at 5 GHz that are matched with commercial WiFi 802.11n/ac protocols.

Human gesture recognition with radio frequency (RF) signals has attained acclaim due to the omnipresence, privacy protection, and broad coverage nature of RF signals. These gesture recognition systems rely on neural networks trained with a large number of labeled data. However, the recognition model trained with data under certain conditions would suffer from significant performance degradation when applied in practical deployment, which limits the application of gesture recognition systems. In this article, we propose an unsupervised domain adaptation framework for RF-based gesture recognition aiming to enhance the performance of the recognition model in new conditions by making effective use of the unlabeled data from new conditions. We first propose pseudo labeling and consistency regularization to utilize unlabeled data for model training and eliminate the feature discrepancies in different domains. Then we propose a confidence constraint loss to enhance the effectiveness of pseudo labeling, and design two corresponding data augmentation methods based on the characteristic of the RF signals to strengthen the performance of the consistency regularization, which can make the framework more effective and robust. Furthermore, we propose a cross-match loss to integrate the pseudo labeling and consistency regularization, which makes the whole framework simple yet effective. Extensive experiments demonstrate that the proposed framework could achieve 4.35% and 2.25% accuracy improvement comparing with the state-of-the-art methods on public WiFi data set and millimeter wave (mmWave) radar data set, respectively.

This paper introduces a novel framework, i.e., RFPose-OT, to enable three-dimensional (3D) human pose estimation from radio frequency (RF) signals. Different from existing methods that predict human poses from RF signals at the signal level directly, we consider the structure difference between the RF signals and the human poses, propose a transformation of the RF signals to the pose domain at the feature level based on the optimal transport (OT) theory, and generate human poses from the transformed features. To evaluate RFPose-OT, we build a radio system and a multi-view camera system to acquire the RF signal data and the ground-truth human poses. The experimental results in a basic indoor environment, an occlusion indoor environment, and an outdoor environment demonstrate that RFPose-OT can predict 3D human poses with higher precision than state-of-the-art methods.

Radio frequency (RF) imaging has been of interest for years due to its ability to create images of targets without being affected by weather and light conditions. The existing heuristic solutions such as back projection (BP), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We also develop a coarse-to-fine scan structure to reduce the computational complexity of the proposed algorithm. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a$16 \times 6$multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Liveness detection has been widely applied in face authentication systems to combat malicious attacks. However, existing methods purely depending on visual frames become vulnerable once visual perception is not reliable. The emerging face spoof and forge techniques urge the systems to exploit the defensive potential of non-visual modalities. To tackle this challenge, we introduce SonarGuard, a system combining ultrasonic and visual information to achieve robust liveness detection on mobile devices. More specifically, SonarGuard simultaneously extracts micro-doppler signatures from ultrasound reflections and motion trajectories from video frames both corresponding to the user’s lip movement. To further confirm the collected ultrasonic and visual information is not derived from malicious audio/video attacks, we consolidate the system via introducing a cross-modal matching mechanism, which demands the inherent consistency between these two modalities. Extensive experiments on a new dataset collected with existing mobile devices demonstrate that the proposed system could achieve average classification error rate of 0.91% under presentation attacks. This result indicates that SonarGuard can boost the security of face authenfication systems in real world usage without additional hardware modification.

Human gesture recognition using millimeter-wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need of intensive data collection, extra training efforts when adapting to new domains, and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive signal variations corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework for mmWave signals based on correlations between signal patterns and gesture variations. Furthermore, a spatial-temporal gesture segmentation algorithm is employed for real-time recognition. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18%, and 98.76% for new users, environments, and locations, respectively. We also evaluate DI-Gesture in challenging scenarios like real-time recogntion and sensing at extreme angles, all of which demonstrates the superior robustness and effectiveness of our system.

In this paper, we propose a radio-assisted human detection framework by incorporating radio information into the state-of-the-art detection methods, including anchor-based one-stage detectors and two-stage detectors. We extract the radio localization and identifier information from the radio signals to assist the human detection, due to which the problem of false positives and false negatives can be greatly alleviated. For both detectors, we use the confidence score revision based on the radio localization to improve the detection performance. For two-stage detection methods, we propose to utilize the region proposals generated from radio localization rather than relying on region proposal network (RPN). Moreover, with the radio identifier information, a non-max suppression method with the radio localization constraint has also been proposed to further suppress the false detections and reduce miss detections. Experiments on the simulative Microsoft COCO dataset and Caltech pedestrian datasets show that the mean average precision (mAP) and the miss rate of the state-of-the-art detection methods can be improved with the aid of radio information. Finally, we conduct experiments in real-world scenarios to demonstrate the feasibility of our proposed method in practice.

Human silhouette segmentation, which is originally defined in computer vision, has achieved promising results for understanding human activities. However, the physical limitation makes existing systems based on optical cameras suffer from severe performance degradation under low illumination, smoke, and/or opaque obstruction conditions. To overcome such limitations, in this paper, we propose to utilize the radio signals, which can traverse obstacles and are unaffected by the lighting conditions to achieve silhouette segmentation. The proposed RFMask framework is composed of three modules. It first transforms RF signals captured by millimeter wave radar on two planes into spatial domain and suppress interference with the signal processing module. Then, it locates human reflections on RF frames and extract features from surrounding signals with human detection module. Finally, the extracted features from RF frames are aggregated with an attention based mask generation module. To verify our proposed framework, we collect a dataset containing804,760radio frames and402,380camera frames with human activities under various scenes. Experimental results show that the proposed framework can achieve impressive human silhouette segmentation even under the challenging scenarios (such as low light and occlusion scenarios) where traditional optical-camera-based methods fail. To the best of our knowledge, this is the first investigation towards segmenting human silhouette based on millimeter wave signals. We hope that our work can serve as a baseline and inspire further research that perform vision tasks with radio signals. The dataset and codes will be made in public.

This paper demonstrates human synthesis based on the Radio Frequency (RF) signals, which leverages the fact that RF signals can record human movements with the signal reflections off the human body. Different from existing RF sensing works that can only perceive humans roughly, this paper aims to generate fine-grained optical human images by introducing a novel cross-modal RFGAN model. Specifically, we first build a radio system equipped with horizontal and vertical antenna arrays to transceive RF signals. Since the reflected RF signals are processed as obscure signal projection heatmaps on the horizontal and vertical planes, we design a RF-Extractor with RNN in RFGAN for RF heatmap encoding and combining to obtain the human activity information. Then we inject the information extracted by the RF-Extractor and RNN as the condition into GAN using the proposed RF-based adaptive normalizations. Finally, we train the whole model in an end-to-end manner. To evaluate our proposed model, we create two cross-modal datasets (RF-Walk&RF-Activity) that contain thousands of optical human activity frames and corresponding RF signals. Experimental results show that the RFGAN can generate target human activity frames using RF signals. To the best of our knowledge, this is the first work to generate optical images based on RF signals.

The past years have witnessed increasing research interest in achieving passive human localization with commodity WiFi devices. However, due to the fundamental limited spatial resolution of WiFi signals, it is still very difficult to achieve accurate localization with existing commodity WiFi devices. To tackle this problem, in this paper, we propose to exploit the degree of freedom provided by the Intelligent Reflecting Surface (IRS), which is composed of a large number of controllable reflective elements, to modulate the spatial distribution of WiFi signals and thus break down the spatial resolution limitation of WiFi signals to achieve accurate localization. Specifically, in the single-person scenario, we derive the closed-form solution to optimally control the phase shift of the IRS elements. In the multi-person scenario, we propose a Side-lobe Cancellation Algorithm to eliminate the near-far effect to achieve accurate localization of multiple persons in an iterative manner. Extensive simulation results demonstrate that without any change to the existing WiFi infrastructure, the proposed framework can locate multiple moving persons passively with sub-centimeter accuracy under multipath interference and random noise.

Human gesture recognition using millimeter wave (mmWave) signals provides attractive applications including smart home and in-car interfaces. While existing works achieve promising performance under controlled settings, practical applications are still limited due to the need for intensive data collection, extra training efforts when adapting to new domains (i.e. environments, persons and locations) and poor performance for real-time recognition. In this paper, we propose DI-Gesture, a domain-independent and real-time mmWave gesture recognition system. Specifically, we first derive the signal variation corresponding to human gestures with spatial-temporal processing. To enhance the robustness of the system and reduce data collecting efforts, we design a data augmentation framework based on the correlation between signal patterns and gesture variations. Furthermore, we propose a dynamic window mechanism to perform gesture segmentation automatically and accurately, thus enabling real-time recognition. Finally, we build a lightweight neural network to extract spatial-temporal information from the data for gesture classification. Extensive experimental results show DI-Gesture achieves an average accuracy of 97.92%, 99.18% and 98.76% for new users, environments and locations, respectively. In real-time scenario, the accuracy of DI-Gesture reaches over 97% with an average inference time of 2.87ms, which demonstrates the superior robustness and effectiveness of our system.

The monitoring of blood pressure is critical for the prevention, diagnosis and treatment of cardiovascular diseases. However, existing methods require physical contact between human body and sensor, which are not suitable for long-term monitoring. In this paper, we propose a contactless blood pressure monitoring system, mmBP, using millimeter wave radar. Specifically, we first separate signals reflected from different spatial locations by coherently combining the signals on different antennas. Then, we locate and extract the arterial pulse using convolutional neural network (CNN) assisted template matching with location tracking. Finally, we design an encoder-decoder neural network to derive the blood pressure information from the extracted signal. Experimental results on 20 subjects show that the measurement deviation rate is 9.00% and 3.69% for systolic and diastolic blood pressure, which demonstrates the feasibility and effectiveness of the proposed system.

Fall is a severe health threat for elders’ health care. While existing systems could achieve promising performance under specific scenarios, the required computing resources are usually not affordable, which is not applicable for real-time detection. In this paper, we propose mmFall, a real time fall detection system using millimeter wave signal which can achieve impressive accuracy with low computation complexity. Specifically, we first extract the signal variation corresponding to human activity with spatial-temporal processing. To enhance the system performance and robustness, we perform data augmentation by shifting, flipping, extracting and interpolating the signal. Finally, we design a light-weight convolutional neural network to achieve real-time fall detection. Extensive experimental results demonstrate that the pro-posed system could achieve state-of-the-art performance with limited computation complexity.

Compared with optical imaging, radio frequency (RF) imaging enables object imaging in an all-weather, privacy-preserving, and cost-effective way. However, the existing heuristic solutions such as back projection (BP) and range migration (RM), suffer from the sidelobe interference due to limited antenna aperture. In this paper, we propose a super-resolution 3-D imaging algorithm to enhance imaging performance. Mathematically, the reconstruction problem is an inverse problem that can be formulated as an optimization problem. The low-rank property of the object is exploited to regularize the imaging problem by nuclear norm minimization. We evaluate the proposed algorithm with both simulations and measurements. With a frequency band range from 2.7-4.1 GHz and a 16×6 multiple-input-multiple-output (MIMO) antenna array, simulation results show high imaging quality with a median boundary keypoint precision of 2 cm, and experimental results validate the feasibility of the proposed algorithm in a real-world environment.

Radio-frequency (RF) based human sensing technologies, due to their great practical value in various applications and privacy-preserving nature, have gained tremendous attention in recent years. However, without fully exploiting the characteristics of radio signals, the performance of existing methods are still limited. First, RF features of the moving human body have different representations in dimensions such as channel and scale, which is challenging when performing feature fusion. Besides, the human body is specularly reflective with respect to the radar, which means the human body cannot be fully captured by a single RF snapshot. Therefore, the radar signal reflected by the human body is sparse and incomplete, which is difficult to extract high-quality features for 3D human pose estimation. In this paper, we present the RF-based Pose Machines (RPM), a novel framework which can generate 3D skeletons from RF signals. Considering the characteristics of RF signals, RPM includes several modules to overcome the challenges. Firstly, a Multidimensional Feature Fusion (MFF) backbone is designed to effectively fuse radio signals based on the channels' correlation and maintain high-quality feature via a multi-scale fusion block. A Spatio-Temporal Attention network is then designed to reconstruct 3D skeletons by modeling the non-local spatio-temporal relationships. To evaluate the performance of our RPM framework, we construct a large-scale dataset of synchronized 3D skeletons and RF signals, RFSkeleton3D. Our experimental results show that RPM locates 3D key points of the human body with an average error of 5.71cm and maintains its performance in new environments with occlusion or bad illumination. The dataset and codes will be made in public.

This paper tackles a new challenge: how to generate human pose images from wireless signals? Although the optical camera can capture optical images, it is easily restricted by bad lighting. The wireless signals do not rely on visible lights. However, the low-resolution characteristics make previous works can only generate a rough skeleton of human posture, missing a lot of detailed visual information, such as background, appearance, etc. Since the visual information usually maintains unchanged for a period and the wireless signals can capture the movements of the human, in this paper, we propose a framework to generate the target human pose images by combining the wireless signals with an initial optical image. We utilize multiple wireless devices to collect the WiFi signals and a camera to capture the initial optical image. Then a data preprocessing component is designed to preprocess the wireless and vision data. Finally, a deep learning model learns to generate the human pose images from the processed wireless signals and the initial optical image. We conduct experiments to evaluate our proposed framework and results show that it achieves higher accuracy than the state-of-the-art WiFi-based pose estimation method and better visual quality than the state-of-the-art human generation method.

By leveraging the mechanical motions on the body surface conducted by physiological activities, many works have achieved radio-frequency(RF)-based physiological sensing. However, previous works generally simplify the model on both the mechanism of physiological motion and the signal propagation around the human body, which leads to the loss of valuable information. In this paper, we introduce the concept of micro-motion(MM) camera to provide a more cognitive imaging modality to observe torso surface motion comprehensively so as to dynamically image the motions of the breath and cardiac activities. We develop a sub-6G MMCamera prototype system. The camera functionality is implemented to prove the concept novelty from the view of respiratory-cardiovascular system monitoring. Our result shows that the proposed system could provide fine-grid torso surface motion imaging with breath and cardiac activities distributed over the entire thorax and abdomen.

The major obstacle for learning-based RF sensing is to obtain a high-quality large-scale annotated dataset. However, unlike visual datasets that can be easily annotated by human workers, RF signal is non-intuitive and non-interpretable, which causes the annotation of RF signals time-consuming and laborious. To resolve the rapacious appetite of annotated data, we propose a novel unsupervised representation learning (URL) framework for RF sensing, RF-URL, to learn a pre-training model on large-scale unannotated RF datasets that can be easily collected. RF-URL utilizes a contrastive framework to mind the gap between signal-processing-based RF sensing and learning-based RF sensing. By constructing positive and negative pairs through different signal processing representations, RF-URL seamlessly integrates the existing RF signal processing algorithms into the learning-based networks. Moreover, the RF-URL is carefully designed to take into account the asymmetric characteristics of different RF signal processing representations. We show that RF-URL is universal to a variety of RF sensing tasks by evaluating RF-URL in three typical RF sensing tasks (human gesture recognition, 3D pose estimation and silhouette generation) based on two general RF devices (WiFi and radar). All experimental results strongly demonstrate that RF-URL takes an important step towards learning-based solutions for large-scale RF sensing applications.

Vibration is a widespread physical phenomenon that often carries important information such as the internal state of the devices. Thus, vibration measurement is of great importance in the field of modern engineering and has drawn much attention. While achieving promising performance, existing methods fail when the vibration amplitude is tiny, e.g, smaller than 50 um. To address such a challenge, in this paper, we propose a contactless method with deep learning, denoted as DeepVib, to sense the tiny vibration using millimeter wave radar. Specifically, DeepVib first makes full advantage of the physical characteristics of the vibrating object and combines Range-Doppler FFT to find the range bin of vibrating objects. Then, DeepVib trains a denoising neural network using a large amount of simulated data, which takes the noisy sample points as input and outputs the denoised data with better SNR. Finally, the vibration status is recovered through the phase variation of the extracted signal. Simulation results show that DeepVib achieves over 40% improvement in measuring um-level amplitudes with over 5x faster processing time, while real experimental results show that DeepVib achieves a mean amplitude error of 2.1 um for the 100um-amplitude vibration.

We observed that remarkable and impressive performance on image-based human pose estimation have been achieved by deep Convolutional Neural Networks (CNN). Nevertheless, directly applying these image-based models on videos is not only computionally intensive, but also may cause jitter and loss. The main reason is that the image-based models purely focus on the local features of individual frames and totally ignore the temporal information among adjacent frames. Some existing methods are proposed to address the temporal coherency issue. However, these methods need to be designed carefully and cannot be combined with existing image-based methods. In this paper, we propose a simple yet effective module to refine the estimated pose by exploiting the temporal coherency among the heatmaps of adjacent frames, which can be easily inserted into image-based networks as a plug-in. We show that the temporal coherency issue among the heatmap frames could be re-formulated as a graph path selection optimization problem. Moreover, to speed up the refinement process, we propose a hierarchical graph optimization to achieve the refinement from coarse to fine. Experimental results on two large-scale video pose estimation benchmarks show that our module can improve the performance with little speed loss when combined with image-based methods as an efficient plug-in.

In this paper, we propose BreathTrack2.0, a contactless breath tracking system based on commodity WiFi devices. Breath-Track2.0 could track the detailed breath status through the phase variation of channel state information(CSI). To achieve this, BreathTrack2.0 first combines the signal on multiple antennas and subcarriers with a joint Angle of Arrival(AoA) and Time of Flight(ToF) beamformer to strengthen the signal from human. Then, it utilizes the fact that the phase offsets introduced by the hardware imperfection are the same among different propagation paths to cancel the phase offsets. Finally, the phase variation of the extracted human signal is utilized to estimate the breath rate and indicate the detailed breath status. Real world experiments demonstrate that the proposed system could estimate the breath rate with the median accuracy of over 99% and could track the detailed status of human breath.

Indoor human speed estimation is critical to in-home health monitoring of elderly people since it can provide the moving status of the human. Contactless indoor speed estimation with radio signals is challenging due to the complicated relationship between the speed of moving human and radio signals. In this article, we propose an indoor speed estimation framework, SpeedNet, to estimate the speed from the radio signals. Specifically, SpeedNet first extracts the dominant path signal reflected from the human through the beamforming technique. Then, SpeedNet obtains the doppler frequency shift (DFS) corresponding to the moving human by analyzing the short-time Fourier transform (STFT) spectrogram of the dominant path signal. Finally, SpeedNet trains a deep neural network composed of convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) to utilize the spatial and temporal features of DFS to estimate the speed of moving human. The experimental results show that SpeedNet can estimate the human moving speed with an average accuracy of 96.33% in a typical indoor environment, which is better than the state-of-the-art approaches.

In this article, we propose a human sensing system with radio signals, MTrack, for in-home healthcare, which is capable of tracking the trajectories of moving persons and vital signs of static persons under the multiperson scenarios. To achieve this, we implement a multiantenna wideband system that can provide high-resolution Angle of Arrival (AoA) and Time of Flight (ToF). A 2-D beamformer is utilized to transform the raw radio signals into the AoA-ToF domain. To track the trajectories of moving persons, we leverage the movement of persons to cancel static multipaths and propose a path selection algorithm to estimate the locations of human and suppress the interferences from dynamic multipaths. To track the vital signs of static persons, we utilize the breath of static persons to eliminate static multipaths and propose a correlation-based algorithm to eliminate dynamic multipaths. Extensive experiments show that the proposed MTrack system is capable of tracking multiple moving persons with subdecimeter level accuracy, and can estimate the breath and heartbeat rate of static persons with the median accuracy of 99.8% and 98.46%, respectively.

Indoor human speed estimation, which can provide the moving status of human, is attracting considerable critical attention, especially in the field of in-home health monitoring of elderly people. Since the relationship between the moving of human and radio signal is very complicated, indoor speed estimation via radio signals is non-trivial and challenging. To address the challenge, in this paper, we propose a SpeedNet framework to estimate the speed of moving human from the radio signals. Specifically, SpeedNet first utilizes the beamforming technique to extract the dominant path signal reflected from individuals. Then, with short time Fourier transform (STFT), SpeedNet analyzes the spectrogram of the dominant path signal and obtains the doppler frequency shift (DFS) that corresponds to the moving human. Finally, SpeedNet exploits the spatial and temporal features of the DFS through a deep neural network, which consists of convolutional neural networks (CNN) and long short-term memory networks (LSTM), to estimate the speed of moving human. Extensive experiments show that compared with the state-of-the-art approaches, SpeedNet can achieve much better speed estimation performance with a mean absolute percentage error (MAPE) of 3.67% in a typical indoor environment.

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices. BreathTrack exploits the phase variation of the channel state information (CSI) to track human breath. To resolve the phase distortions introduced by the hardware imperfection of the commodity WiFi chips, BreathTrack utilizes both the hardware and software correction methods. The time-invariant PLL phase offset is calibrated by the hardware correction using cables and splitters, while the time-varying carrier frequency offset, sampling frequency offset and packet detection delay are removed by the software corrections using the phase difference between the CSI at the receiver antennas and that at the reference antenna connected from the transmitter. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.

In this paper, we propose a contact-free breath tracking system, BreathTrack, to track the status of breath using the off-the-shelf WiFi devices by exploiting the phase variation of the channel state information(CSI). BreathTrack utilizes a reference antenna connected from the transmitter to resolve the phase distortions introduced by the hardware imperfection. Moreover, BreathTrack utilizes the sparse recovery method to find the dominant path in the multipath indoor environment and derive the corresponding complex attenuation coefficient. Then, the phase variation of the complex attenuation coefficient is utilized to extract the detailed breath status and the breath rate. Extensive experiments are conducted to show that BreathTrack could estimate the breath rate with the median accuracy of over 99% in most scenarios, and could track the detailed status of breath directly using the raw phase variation.