Radio Biometrics: Human Recognition Through a Wall

Human respiration detection based on Wi-Fi signals does not require users to carry any device, hence it has drawn a lot of attention due to better user acceptance and great potential for real-world deployment. However, recent studies show that respiration sensing performance varies in different locations due to the nature of Wi-Fi radio wave propagation in indoor environments, i.e., respiration detection may experience poor performance at certain locations which we call "blind spots". In this paper, we aim to address the blind spot problem to ensure full coverage of respiration detection. Basically, the amplitude and phase of Wi-Fi channel state information (CSI) are orthogonal and complementary to each other, so they can be combined to eliminate the blind spots. However, accurate CSI phase cannot be obtained from commodity Wi-Fi due to the clock-unsynchronized transceivers. Thus, we apply conjugate multiplication (CM) of CSI between two antennas to remove the phase offset and construct two orthogonal signals--new "amplitude and phase" which are still complementary to each other. In this way, we can ensure full human respiration detection. Based on these ideas, We design and implement a real-time respiration detection system with commodity Wi-Fi devices. We conduct extensive experiments to validate our model and design. The results show that, with only one transceiver pair and without leveraging multiple sub-carriers, our system enables full location coverage with no blind spot, showing great potential for real deployment.

The time reversal (TR) technique combined with the ultra-wideband (UWB) system offers a new potential for decreasing the cost and complexity of the UWB receivers. In spite of TR–UWB's good performance in perfect channel state information (CSI), it is very sensitive to the channel estimation error. The effect of channel imperfection on the TR–UWB system is considered in this paper. At first, based on a minimum mean square error (MMSE) equalizer receiver, a prefilter is calculated in closed form to improve the performance of the TR–UWB system in an imperfect CSI scenario. Furthermore, for comparison purposes, a similar calculation for prefilter is carried out based on a simple matched filter (MF) receiver. Then, in order to improve the MF receiver performance, a two-stage iteration-based algorithm is developed. The initial value for this iteration-based improved algorithm is considered to be a prefilter which is calculated in the TR–UWB system with MMSE equalizer. This optimized algorithm causes the channel estimation error in the TR–UWB system to become zero in some steps. Finally, exhaustive simulations are done to demonstrate the performance advantage attained by the improved algorithm.

A drop in physical activity and a deterioration in the capacity to undertake daily life activities are both connected with ageing and have negative effects on physical and mental health. An Elderly and Visually Impaired Human Activity Monitoring (EV-HAM) system that keeps tabs on a person’s routine and steps in if a change in behaviour or a crisis might greatly help an elderly person or a visually impaired. These individuals may find greater freedom with the help of an EVHAM system. As the backbone of human-centric applications like actively supported living and in-home monitoring for the elderly and visually impaired, an EVHAM system is essential. Big data-driven product design is flourishing in this age of 5G and the IoT. Recent advancements in processing power and software architectures have also contributed to the emergence and development of artificial intelligence (AI). In this context, the digital twin has emerged as a state-of-the-art technology that bridges the gap between the real and virtual worlds by evaluating data from several sensors using artificial intelligence algorithms. Although promising findings have been reported by Wi-Fi-based human activity identification techniques so far, their effectiveness is vulnerable to environmental variations. Using the environment-independent fingerprints generated from the Wi-Fi channel state information (CSI), we introduce Wi-Sense. This human activity identification system employs a Deep Hybrid convolutional neural network (DHCNN). The proposed system begins by collecting the CSI with a regular Wi-Fi Network Interface Controller. Wi-Sense uses the CSI ratio technique to lessen the effect of noise and the phase offset. The t- Distributed Stochastic Neighbor Embedding (t-SNE) is used to eliminate unnecessary data further. The data dimension is decreased, and the negative effects on the environment are eliminated in this process. The resulting spectrogram of the processed data exposes the activity’s micro-Doppler fingerprints as a function of both time and location. These spectrograms are put to use in the training of a DHCNN. Based on our findings, EVHAM can accurately identify these actions 99% of the time.

A Multi-Input Multi-Output (MIMO) Ultra-Wide Band (UWB) communication system with Time Reversal (TR) is investigated in this paper. The system uses spatial multiplexing (SM) scheme to achieve high data rate. In order to cope with long delay spread of the UWB channel, time reversal technique is adopted at transmitter. Later a Multi Stream Interference (MSI) mitigation algorithm based on Zero Forcing (ZF) is proposed. The proposed ZF algorithm requires the knowledge of the channel state information (CSI), which is already available for time reversal at tranmitter. Simulation results show that the algorithm can considerably improve the BER performance of the system when the number of data streams is large (ges 3). This paper focuses on MIMO single user cases, however, the proposed MIMO-TR-UWB can be also extended to MIMO broadcasting system.

An antenna selection scheme for multi-input multi-output (MIMO) ultra-wide band (UWB) communication system with time reversal (TR) is investigated in this paper. The system exploits spatial multiplexing (SM) scheme to achieve high data rate. In order to cope with long delay spread of the UWB channel, time reversal technique is adopted. TR can mitigate not only the interference intersymbol (ISI) but also multi stream interference (MSI) caused by transmitting several data streams simultaneously. Antenna selection algorithm allows to reduce the number of transmit antenna by using the channel state information (CSI), which is already available for time reversal. Simulation results show that the algorithm can considerably improve the BER performance of the system when the number of diversity branch is not so large.

Two factor authentication offers significant benefits but has had limited uptake as users find the extra step required at each authentication attempt (e.g., to enter a verification code) to be onerous. In this paper, we present a novel twofactor authentication system called Wi-Access that requires no additional effort from the user beyond typing her credentials. The first factor is established by the knowledge of the password. Wi-Access uses the unique perturbations in the ambient WiFi signals caused by the hand and finger motions of the user during password entry as a way to confirm inherence (i.e., something that the user is) as the second factor for authentication. We show that the way in which Alice moves her hands and fingers while typing her password is uniquely manifested in the Channel State Information (CSI) of ubiquitous WiFi signals. We also show that if Bob types Alice's password, then the corresponding CSI signals are significantly distinct to Alice. Wi-Access uses Principle Component Analysis to reduce the dimensionality of the CSI data. A variance-based thresholding algorithm is used to extract the CSI signals that precisely correspond to the typing action. A one-class classifier based on an autoencoder that uses specific thresholds tailored to each user's training data is used to make the authentication decision. We implement Wi-Access using offthe-shelf 802.11n devices and evaluate its performance with 10 subjects. Our tests reveal that Wi-Access can on average achieve 92% authentication accuracy with 8% false positives. Moreover, Wi-Access is robust in preventing attacks with a 91.8% attack detection accuracy.

With the ubiquity of commodity WiFi devices and the rapid development of Internet of Things (IoT), there are increasingly intelligent sensing applications emerging by utilizing fine-grained channel state information (CSI) from WiFi signals, which can realize contactless human-computer interaction (HCI) for smart home. However, most of CSI-based activity recognition approaches are vulnerable to random noises derived from indoor environments. In this paper, we present a multimodal fusion-AdaBoost based human activity recognition scheme on WiFi platform. For this purpose, we develop two theoretical underpinnings, including a sensing model and a recognition model. The sensing model is firstly established to investigate the impact of human activities on propagation properties of WiFi signals. To this end, Fresnel zone model is used to quantify the correlation between CSI dynamics and human activities. Moreover, the recognition model is used to exploit physical activity-induced signal changes to infer potential activity information. In the process, we firstly construct a CSI tensor through integrating all CSI information at each WiFi receiver. Then, a CANDECAMP/PARAFAC (CP) decomposition method is applied to this CSI tensor for obtaining representative features. Finally, based on the features extracted, human activities are able to be recognized by combining multimodal fusion and AdaBoost. We implement the proposed scheme on a set of WiFi devices and evaluate it in both laboratory and corridor environments. The experimental results confirm that the proposed scheme can achieve average recognition accuracies of 96% and 95% in two indoor scenarios, respectively.

Passive motion detection (PMD) systems based on WiFi signals consider the traditional wireless network as a sensing network for detecting human motion. However, most WiFi-based PMD systems may perform poorly when the deployment environment changes (e.g., changes of transceiver or furniture placement). In addition, under the constraints of the limited devices and measurement noise, developing a robust and accurate motion detector is still heavily required. To this end, we propose R-MoDe to revisit the PMD problem from the perspective of the distribution difference of environmental statistics. First, R-MoDe exploits a statistical model based on temporal correlation function (TCF) to characterize the time-varying channel induced by human motion. Based on the statistical model, R-MoDe calculates environmental statistics by TCF and treats the distribution of the statistics under non-motion scenarios as a template profile, independent of the deployment environment and measurement noise. Next, with the limited devices, we regard available subcarriers as virtual sensors and design a clustering-vote-based sensor selection scheme to boost detection performance. Then, R-MoDe adopts the distance between the distribution of environmental statistics and the template profile as a robust detection metric. Finally, we propose a motion detection strategy based on double thresholds and hysteresis tracking to enable accurate and robust detection. R-MoDe is implemented on commodity WiFi devices in practical indoor environments. Experimental results show that R-MoDe can achieve an average motion detection rate of 96.19% and an average true negative rate of 97.04% using only one pair of transceivers, significantly outperforming the state-of-the-art methods.

Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.

High rate data transmission in an underwater link necessitates the use of multiple antennas. Intersymbol interference (ISI), one of the major issues in single antenna underwater acoustic (UWA) systems, becomes even more prominent while establishing a multi-antenna communication link. Deploying an adaptive equalizer to mitigate this ISI incurs huge computational complexity as the equalizer needs large number of taps. Also, this might require a compromise in data rate and/or error performance of the system. A solution to this is the time reversal (TR) technique which has been proven to be a computationally simpler approach to combat ISI and to provide high signal to noise ratio gains in a single antenna system. In this paper, we explore the possibility of the TR technique in improving error rate, and minimizing computational burden of the equalizer in a multi-antenna UWA link. We present an experimental demonstration which shows that while reducing the computational complexity, the TR technique also provides better error performance at higher data rate.

In this paper, a novel indoor localization system with channel state information (CSI) fingerprints is proposed, which learns the spatial and frequency features of CSI in the fifth-generation (5G) cellular network by a Siamese convolution neural network. In particular, considering that the CSI continuously collected by a moving target possesses the implicit spatial association, we locate the target by the successive CSI data gathered within a time interval which can be regarded as an information subspace of the fingerprint database. Therefore, the fingerprint localization can be modeled as a subspace matching problem and solved by the Siamese network-based similarity learning. In the proposed system, we design a structure of CSI fingerprint which includes the information from multiple base stations in spatial and frequency domains. Then, the proposed Siamese architecture extracts the CSI feature and estimates the location of the target by feature similarity comparison. Compared with the existing algorithms, it can increase the positioning accuracy significantly by the feature relevance among the CSI data collected at different positions. The field tests indicate that compared to other CSI fingerprint-based positioning methods, our proposed algorithm can effectively reduce the localization error.

HandFi: WiFi Sensing based Hand Gesture Recognition using Channel State Information

A novel method of utilizing the channel state information at the transmitter (CSIT) for orthogonal frequency division multiplexing (OFDM) systems with multiple transmit antennas is presented in this paper. Using the conventional time reversal (TR) technique along with a specific OFDM symbol structure, the method facilitates the application of full-rate orthogonal space-time block coding (OSTBC) to systems with more than two transmit antennas for complex signalling. Compared to the conventional eigenbeamforming method, which maximizes the received signalto-noise ratio (SNR) when CSIT is available, it is shown that the proposed system has a much reduced computational complexity. However, this complexity reduction comes at the expense of a loss of diversity in an uncoded system, which we show through an analysis of bit error rate (BER). Despite this drawback, we show through simulations that the proposed method achieves BER performance almost as well as the eigenbeamforming method when channel coding is employed, which is the case in most practical systems.

IEEE 802.11 CSI randomization to preserve location privacy: An empirical evaluation in different scenarios

Due to rapid developments of smart devices and mobile applications, there is an urgent need for a new human-in-the-loop architecture with better system efficiency and user experience. Compared with conventional device-based human-computer interactive (HCI) methods, device-free technology with WiFi provides a new HCI method and is promising for providing better user-perceived quality-of-experience. Being essential for device-free applications, device-free human detection has gained increasing interest, of which through-the-wall (TTW) human detection is of great challenge. Existing TTW detection systems either rely on massive deployment of transceivers or require specialized WiFi monitors, making them inapplicable for real-world applications. Recently, more and more researchers have tapped into the physical layer for more robust and reliable human detection, ever since channel state information (CSI) can be exported with commodity devices. Despite great progress achieved, there have been few works studying TTW detection. In this paper, we propose a novel scheme for robust device-free TTW detection (R-TTWD) of a moving human with commodity devices. Different from the time dimension-based features exploited in the previous works, R-TTWD takes advantage of the correlated changes over different subcarriers and extracts the first-order difference of eigenvector of CSI across different subcarriers for TTW human detection. Instead of direct feature extraction, we first perform a PCA-based filtering on the preprocessed data, since a simple low-pass filtering is insufficient for noise removal. Furthermore, the detection results across different transmit-receive antenna pairs are fused with a majority-vote-based scheme for more robust and accurate detection. We prototype R-TTWD on commodity WiFi devices and evaluate its performance both in different environments and over long test period, validating the robustness of R-TTWD with both detection rates for moving human and human absence over 99% regardless of different wall materials, dynamic moving speeds, and so on.