Radio frequency (RF) sensing has been considered as an effective approach to human perception of nonintrusive and high-privacy scenarios. However, the existing wireless sensing techniques mostly rely on extensive labeled RF sensing data for offline training, while wireless sensory data collection is highly time consuming and costly. To ridge this gap, we investigate the problem of generalized dataset augmentation with an artificial intelligence (AI) generated content (AIGC) approach, termed RF-AIGC, for wireless sensing, which can not only purposefully generate new RF sensing data but reduce the data collection cost by augmenting a limited training dataset with synthesized RF data. We propose a conditional recurrent generative adversarial network (termed RF-CRGAN) to generate labeled synthetic RF data for specified human activities for multiple wireless sensing platforms, such as WiFi, radio-frequency identification (RFID), and millimeter wave (mmWave) radar. We also propose a holistic quantitative method to help evaluate and explain the effects of the synthesized data. The experimental results demonstrate that the proposed approach can effectively enhance the diversity of training data and achieve similar performance as real data.

Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this article, we propose a time–frequency semantic generative adversarial network framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution and attention semantic feature embedding methods for the semantic extraction module. A discrete wavelet transform is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.

With the rapid development towards smart Internet of Things (IoT), detection of human activity has become essential in a variety of applications. Various radio-frequency (RF) sensing technologies, such as WiFi, Radio-Frequency Identification (RFID), and Frequency-Modulated Continuous Wave (FMCW) radar, have been utilized for non-invasive human activity recognition (HAR). It will be highly desirable to develop a HAR solution that can work with different types of RF technologies, such that the cost and the barrier of wide deployment can both be greatly reduced, and more robust performance can be achieved by utilizing the complementary RF sensory data. In this paper, we propose a technology-agnostic approach for RF-based HAR, termed TARF, which works with several different RF sensing technologies. A novel data generalization technique is proposed to mitigate the disparity in measured data from different RF devices. A domain adversarial neural network is proposed to combat the interference from various RF sensing technologies. The performance of the proposed system is evaluated with experiments using four different RF sensing technologies. TARF is shown to outperform the state-of-the-art Convolutional Neural Network (CNN)-based solution with considerable gains.

With outbreak of the COVID-19 pandemic, contact tracing has become an important problem. It has been proven that maintaining social distance and isolating affected people are highly beneficial for curbing the spread of COVID-19, which all depend on identifying people’s trajectories. However, the current interview-based approach is costly, and the existing mobile app-based schemes rely on complete and accurate data. In this paper, we propose a transformer encoder-based approach with spatial position embedding extracted using a graph Combinatorial Laplacian matrix to interpolate incomplete human trajectories. To model human trajectory, we propose a graphical embedded module to extract spatial features based on predefined location clusters. The incomplete trajectory sequences are first preprocessed into matrices and then used to train a deep transformer encoder network for trajectory completion. Our experiments using a real world Bluetooth Low Energy (BLE) dataset validate the efficacy of our proposed approach, which outperforms several baseline methods.

Interest in Radio Frequency (RF) based 3D human pose tracking has skyrocketed in the age of Artificial Intelligence of Things (AIoT). Compared to Computer Vision (CV) based methods, RF-based approaches are more resilient to lighting and non-line-of-sight conditions, and can better preserve user privacy. However, the majority of the current RF-based methods rely on a vision-aided multi-modal learning approach. An extensive amount of paired training data, i.e., Radio-Frequency Identification (RFID) data and vision data, must be collected, to achieve an adequate performance with the supervised-learning network. In order to mitigate such time-consuming and costly tasks, we propose a data augmentation method based on Generative Adversarial Network (GAN), named RFPose-GAN, to generate synthesized RFID data to alleviate the complications of using commodity RFID tags and receivers. In this paper, a forward kinematic layer is incorporated to generate simulated vision pose data, thus eliminating the need of using a Kinect 2.0 device in RFPose-GAN. Experiments conducted demonstrate that the synthesized data achieves accurate pose estimation performance.

With the rapid growth of the Internet of Things (IoT), the radio-frequency identification (RFID) technology has been recognized as an effective and low-cost solution for many IoT applications. In this article, we study the problem of utilizing a sparse RFID tag array for backscatter indoor localization. We first theoretically and experimentally validate the feasibility of using sparse tag arrays for the direction of arrival (DOA) estimation. We then present the SparseTag system, which leverages a novel sparse tag array for high-precision backscatter indoor localization. The SparseTag system includes sparse array processing, difference co-array design, DOA estimation using a spatial smoothing-based method, and a localization method. A robust channel selection method based on the RFID tag array is adopted for mitigating the multipath effect. The SparseTag system is implemented with commodity RFID devices. Its superior performance is validated in two different environments with extensive experiments and comparison to baseline schemes.

Human pose tracking has attracted great interest re-cently. Considerable efforts have been made in Radio-Frequency (RF) sensing techniques for human pose tracking without using a video camera. Although the existing RF based schemes can well protect user privacy, they are usually sensitive to the RF environment and are hard to generalize to new environments. In this paper, we analyze the challenges of generalization of Radio-Frequency Identification (RFID) based human pose tracking systems. We then present an RFID based 3D human pose tracking system, termed Meta-Pose, which incorporates meta-learning and few-shot fine-tuning to achieve high adaptability to new environments. The proposed system is implemented with commodity RFID devices and extensive experiments are conducted for performance evaluation. The experiment results validate the superior human pose tracking performance and high adaptability of the proposed Meta-Pose system.

Millimeter Wave (mmWave) communications, as a core technique of 5G, can be leveraged for outdoor localization because of its large bandwidth and massive antenna array. Fingerprinting based mmWave outdoor localization methods using deep learning are highly suitable for non-line-of-sight (NLOS) environments. In this paper, we propose a deep convolutional Gaussian process (DCGP) based regression approach to achieve high robustness for fingerprinting-based mmWave outdoor localization, which exploits the convolutional structure for deep Gaussian process to allow uncertainty estimation on location predictions. Specially, we present a system architecture of mmWave based outdoor localization, including beamforming image construction and DCGP training, where DCGP model can effectively learn the location features from mmWave beamforming images. Our experimental results show that the proposed DCGP method can achieve higher outdoor localization accuracy than a CNN-based baseline method.

Vital sign (e.g., respiration rate) monitoring has become increasingly more important because it offers useful clues about medical conditions such as sleep disorders. There is a compelling need for technologies that enable contact-free and easy deployment of vital sign monitoring over an extended period of time for healthcare. In this article, we present a SonarBeat system to leverage a phase-based active sonar to monitor respiration rates with smartphones. We provide a sonar phase analysis and discuss the technical challenges for respiration rate estimation utilizing an inaudible sound signal. Moreover, we design and implement the SonarBeat system, with components including signal generation, data extraction, received signal preprocessing, and breathing rate estimation with Android smartphones. Our extensive experimental results validate the superior performance of SonarBeat in different indoor environment settings.

To improve driving safety and avoid accidents caused by driving fatigue, drowsiness detection aims to alarm the driver before he/she falls asleep. Since breathing rate is a key indicator of the drowsy state, respiration monitoring in the noisy driving environment is critical for developing an effective driving fatigue detection system. In this paper, we propose, for the first time, an RFID based respiration monitoring system for driving environments. The system estimates the respiration rate of a driver based on phase values sampled from multiple RFID tags attached to the seat belt, while exploiting the tag diversity to combat the strong noise in the driving environment. Both tensor completion and tensor Canonical Polyadic Decomposition (CPD) are applied to process the phase values, to overcome the influence of frequency hopping, random sampling, vehicle vibration, and other environmental movements. The proposed system is analyzed and implemented with commodity RFID devices. Its accurate and robust performance is demonstrated with extensive experiments conducted in a real driving car.

In recent years, human pose tracking has become an important topic in computer vision (CV). To improve the privacy of human pose tracking, there is considerable interest in techniques without using a video camera. To this end, radio-frequency identification (RFID) tags, as a low-cost wearable sensor, provide an effective solution for 3-D human pose tracking. In this article, we propose RFID-Pose, a vision-aided realtime 3-D human pose estimation system, which is based on deep learning assisted by CV. The RFID phase data are calibrated to effectively mitigate the severe phase distortion, and high accuracy low rank tensor completion is employed to impute the missing RFID data. The system then estimates the spatial rotation angle of each human limb, and utilizes the rotation angles to reconstruct human pose in realtime with the forward kinematic technique. A prototype is developed with commodity RFID devices. High pose estimation accuracy and realtime operation of RFID-Pose are demonstrated in our experiments using Kinect 2.0 as a benchmark.

This paper aims at predicting accurate outdoor and indoor locations using deep neural networks, for the data collected using the Long-Range Wide-Area Network (LoRaWAN) communication protocol. First, we propose an interpolation aided fingerprinting-based localization system architecture. We propose a deep autoencoder method to effectively deal with the large number of missing samples/outliers caused by the large size and wide coverage of LoRa networks. We also leverage three different deep learning models, i.e., the Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and the Convolutional Neural Network (CNN), for fingerprinting based location regression. The superior localization performance of the proposed system is validated by our experimental study using a publicly available outdoor dataset and an indoor LoRa testbed.

With the rapid development of computer vision, human pose tracking has attracted increasing attention in recent years. To address the privacy concerns, it is desirable to develop techniques without using a video camera. To this end, RFID tags can be used as a low-cost wearable sensor to provide an effective solution for 3D human pose tracking. User adaptability is another big challenge in RF based pose tracking, i.e., how to use a well-trained model for untrained subjects. In this paper, we propose Cycle-Pose, a subject-adaptive realtime 3D human pose estimation system, which is based on deep learning and assisted by computer vision for model training. In Cycle-Pose, RFID phase data is calibrated to effectively mitigate the severe phase distortion, and High Accuracy LowRank Tensor Completion (HaLRTC) is employed to impute missing RFID data. A cycle kinematic network is proposed to remove the restriction on paired RFID and vision data for model training. The resulting system is subject-adaptive, achieved by learning to transform the RFID data into a human skeleton for different subjects. A prototype system is developed with commodity RFID tags/devices and evaluated with experiments. Compared with a traditional system RFIDPose, higher pose estimation accuracy and subject adaptability are demonstrated by Cycle-Pose in our experiments using Kinect 2.0 data as ground truth.

Radio Frequency (RF) based human pose estimation techniques have been proposed to generate human pose without using a camera, so people will no longer worry about their privacy. Compared with other RF sensing based systems, Radio Frequency Identification (RFID) provides a promising solution for RF based human pose estimation. RFID tags can be used as wearable sensors because of their small size. The interference caused by the multipath effect is much smaller in the RFID system. The cost of RFID systems is also lower than the advanced radar based systems such as FMCW radar. Thus, we propose the RFID-Pose system for tracking the movements of multiple human limbs in realtime [1]. In the proposed system, RFID tags are attached to the target human joints. The movement of the tags are captured by the phase variations in the responses from each tag. The human pose is reconstructed by estimating rotation angles from RFID data and the initial human skeleton. The vision data will not be needed anymore in the testing phase, so the user's privacy can be well protected.

Vital signs, such as respiration and heartbeat, are useful for health monitoring because such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this article, we present PhaseBeat to exploit channel state information, in particular, phase difference data to monitor breathing and heart rates with commodity WiFi devices. We provide a rigorous analysis of channel state information phase difference with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.

With the growth of the number of vehicles and car accidents, driving safety is becoming increasingly important. There is a compelling need for an effective, low-cost driving fatigue detection system. In this paper, we propose an RFID based system, termed NodTrack, to detect the nodding movements of drivers, which is a key indicator of fatigue and one of the most dangerous motions during drowsy driving. The NodTrack system utilizes the phase difference between two RFID tags mounted on the back of a hat worn by the driver, to extract nodding features. We propose an effective technique to mitigate the cumulative error caused by frequency hopping in most FCC-compliant RFID systems, as well as a long short-term memory (LSTM) autoencoder model to learn the nodding features from calibrated data. The highly accurate detection performance of the proposed system is validated by our experimental study.

In this paper, we study the problem of utilizing a sparse RFID tag array for backscatter indoor localization. We first theoretically and experimentally validate the feasibility of using RFID tag array for direction of arrival (DOA) estimation. We then present the SparseTag system, which leverages a novel sparse RFID tag array for high-precision backscatter indoor localization. The SparseTag system includes sparse array processing, difference co-array design, DOA estimation using a spatial smoothing based method, and a localization method, while a robust channel selection method based on the RFID tag array is proposed for mitigating the indoor multipath effect. The SparseTag system is implemented with commodity RFID devices. Its superior performance is validated in two different environments with extensive experiments.

With the growth of smart healthcare in the Internet of Things (IoT), breathing monitoring and apnea detection are of increasing importance. In this paper, we propose AutoTag, a recurrent variational autoencoder model for breathing and apnea detection with commodity RFID Tags. The AutoTag system consists of signal extraction, calibration, and respiration monitoring modules. We propose a novel method to mitigate the frequency hopping offset with realtime calibration for FCC complaint RFID systems, and a new recurrent variational autoencoder method for apnea and breathing detection. Experimental results demonstrate the effectiveness of the proposed AutoTag system in two different environments.

Vital signs, such as breathing rate, can provide useful information for personal healthcare. In this paper, we present ResBeat, a commodity 5GHz WiFi based system to exploit bimodal channel state information (CSI), including amplitude and phase difference, for realtime, long- term, and contact-free breathing monitoring. We first present an analysis of breathing signal anomaly based on bimodal CSI data. We then describe the data preprocessing, adaptive signal selection, and breathing signal monitoring modules of ResBeat, and employ peak detection to estimate breathing rates. We conduct extensive experiments under three different environments, where superior performance over two alternative methods is validated.

Vital signs, such as respiration and heartbeat, are useful to health monitoring since such signals provide important clues of medical conditions. Effective solutions are needed to provide contact-free, easy deployment, low-cost, and long-term vital sign monitoring. In this paper, we present PhaseBeat to exploit channel state information (CSI) phase difference data to monitor breathing and heartbeat with commodity WiFi devices. We provide a rigorous analysis of the CSI phase difference data with respect to its stability and periodicity. Based on the analysis, we design and implement the PhaseBeat system with off-the-shelf WiFi devices, and conduct an extensive experimental study to validate its performance. Our experimental results demonstrate the superior performance of PhaseBeat over existing approaches in various indoor environments.

Breathing signal monitoring can provide important clues for health problems. Compared to existing techniques that require wearable devices and special equipment, a more desirable approach is to provide contact-free and long-term breathing rate monitoring by exploiting wireless signals. In this article, we propose TensorBeat, a system to employ channel state information (CSI) phase difference data to intelligently estimate breathing rates for multiple persons with commodity WiFi devices. The main idea is to leverage the tensor decomposition technique to handle the CSI phase difference data. The proposed TensorBeat scheme first obtains CSI phase difference data between pairs of antennas at the WiFi receiver to create CSI tensors. Then canonical polyadic (CP) decomposition is applied to obtain the desired breathing signals. A stable signal matching algorithm is developed to identify the decomposed signal pairs, and a peak detection method is applied to estimate the breathing rates for multiple persons. Our experimental study shows that TensorBeat can achieve high accuracy under different environments for multiperson breathing rate monitoring.