Commodity WiFi Research Articles

SpaceBeat: Identity-aware Multi-person Vital Signs Monitoring Using Commodity WiFi

Vital signs monitoring has gained increasing attention due to its ability to indicate various human health and well-being conditions. The development of WiFi sensing technologies has made it possible to monitor vital signs using ubiquitous WiFi signals and devices. However, most existing approaches are dedicated to single-person scenarios. A few WiFi sensing approaches can achieve multi-person vital signs monitoring, whereas they are not identity-aware and sensitive to interferences in the environment. In this paper, we propose SpaceBeat, an identity-aware and interference-robust multi-person vital sign monitoring system using commodity WiFi. In particular, our system separates multiple people and locates each person in the spatial domain by leveraging multiple antennas. We analyze the change of signals at the location of each person to achieve identity-aware vital signs monitoring. We also design a contrastive principal component analysis-contrastive learning framework to mitigate interferences caused by other moving people. We evaluate SpaceBeat in various challenging environments, including interference scenarios, non-line-of-sight scenarios, different distances, etc. Our system achieves an average accuracy of 99.1% for breathing monitoring and 97.9% for heartbeat monitoring.

WiVelo: Fine-grained Wi-Fi Walking Velocity Estimation

Passive human tracking using Wi-Fi has been researched broadly in the past decade. Besides straightforward anchor point localization, velocity is another vital sign adopted by the existing approaches to infer user trajectory. However, state-of-the-art Wi-Fi velocity estimation relies on Doppler-Frequency-Shift (DFS), which suffers from the inevitable signal noise incurring unbounded velocity errors, further degrading the tracking accuracy. In this article, we present WiVelo, which explores new spatial-temporal signal correlation features observed from different antennas to achieve accurate velocity estimation. First, we use subcarrier shift distribution (SSD) extracted from channel state information (CSI) to define two correlation features for direction and speed estimation, separately. Then, we design a mesh model calculated by the antennas’ locations to enable a fine-grained velocity estimation with bounded direction error. Finally, with the continuously estimated velocity, we develop an end-to-end trajectory recovery algorithm to mitigate velocity outliers with the property of walking velocity continuity. We implement WiVelo on commodity Wi-Fi hardware and extensively evaluate its tracking accuracy in various environments. The experimental results show our median and 90-percentile tracking errors are 0.47 m and 1.06 m, which are half and a quarter of state-of-the-art. The datasets and source codes are published through Github ( https://github.com/research-source/code ).

RoSeFi: A Robust Sedentary Behavior Monitoring System With Commodity WiFi Devices

RoSeFi: A Robust Sedentary Behavior Monitoring System With Commodity WiFi Devices

Deep learning-based fall detection using commodity Wi-Fi

As the phenomenon of an aging population gradually becomes common worldwide, the pressure on the elderly has seen a notable increase. To address this challenge, fall detection systems are important in ensuring the safety of the elderly population, particularly those living alone. Wi-Fi sensing, as a privacy-preserving method of perception, can be deployed indoors for detecting human activities such as falls, based on the reflective properties of electromagnetic waves. Signals generated by transmitters experience reflections from various objects within indoor environments, leading to distinct propagation paths. These signals eventually aggregate at the receiver, incorporating details about the objects’ orientation and their activity states. In this study, within practical experimental environments, we collect dataset and utilize deep learning method to classify the falling events.

MuSense: Multiperson Continuous Activity Sensing Using Commodity Wi-Fi

MuSense: Multiperson Continuous Activity Sensing Using Commodity Wi-Fi

Respiration estimation for indoor human via commodity WiFi based on time-frequency analysis

Respiration estimation for indoor human via commodity WiFi based on time-frequency analysis

WiRITE: General and Practical Wi-Fi Based Hand-Writing Recognition

Device-free hand-writing systems identify the content that a user writes by hand movement in the air, thus providing an intuitive human computer interface. In this paper, we propose WiRITE, a Wi-Fi hand-writing recognition system built with commodity Wi-Fi APs. Unlike most existing machine learning based hand-writing recognition systems, which are often subject to severe limitations in generality, e.g., high training overhead when adapted across hand-writing alphabets, environments, and users, WiRITE is designed with unique consideration of its generality when applied to practice—being application-transferable, environment-agnostic, and user-independent. With little training overhead, WiRITE behaves inclusively to different users, environments, and applications, stemming from a comprehensive design of signal processing that is built into its core machine learning model. Extensive evaluation is conducted with five users for three applications, i.e., recognizing Digits, English letters, and Chinese characters, in realistic office environment. The experiment results demonstrate that WiRITE provides at least 0.9 accuracy in various combinations of users and applications with 0.93 accuracy in average.

Multi-Subject 3D Human Mesh Construction Using Commodity WiFi

This paper introduces MultiMesh, a multi-subject 3D human mesh construction system based on commodity WiFi. Our system can reuse commodity WiFi devices in the environment and is capable of working in non-line-of-sight (NLoS) conditions compared with the traditional computer vision-based approach. Specifically, we leverage an L-shaped antenna array to generate the two-dimensional angle of arrival (2D AoA) of reflected signals for subject separation in the physical space. We further leverage the angle of departure and time of flight of the signal to enhance the resolvability for precise separation of close subjects. Then we exploit information from various signal dimensions to mitigate the interference of indirect reflections according to different signal propagation paths. Moreover, we employ the continuity of human movement in the spatial-temporal domain to track weak reflected signals of faraway subjects. Finally, we utilize a deep learning model to digitize 2D AoA images of each subject into the 3D human mesh. We conducted extensive experiments in real-world multi-subject scenarios under various environments to evaluate the performance of our system. For example, we conduct experiments with occlusion and perform human mesh construction for different distances between two subjects and different distances between subjects and WiFi devices. The results show that MultiMesh can accurately construct 3D human meshes for multiple users with an average vertex error of 4cm. The evaluations also demonstrate that our system could achieve comparable performance for unseen environments and people. Moreover, we also evaluate the accuracy of spatial information extraction and the performance of subject detection. These evaluations demonstrate the robustness and effectiveness of our system.

Wi-AM: Enabling Cross-Domain Gesture Recognition with Commodity Wi-Fi.

RF-based gesture recognition systems outperform computer vision-based systems in terms of user privacy. The integration of Wi-Fi sensing and deep learning has opened new application areas for intelligent multimedia technology. Although promising, existing systems have multiple limitations: (1) they only work well in a fixed domain; (2) when working in a new domain, they require the recollection of a large amount of data. These limitations either lead to a subpar cross-domain performance or require a huge amount of human effort, impeding their widespread adoption in practical scenarios. We propose Wi-AM, a privacy-preserving gesture recognition framework, to address the above limitations. Wi-AM can accurately recognize gestures in a new domain with only one sample. To remove irrelevant disturbances induced by interfering domain factors, we design a multi-domain adversarial scheme to reduce the differences in data distribution between different domains and extract the maximum amount of transferable features related to gestures. Moreover, to quickly adapt to an unseen domain with only a few samples, Wi-AM adopts a meta-learning framework to fine-tune the trained model into a new domain with a one-sample-per-gesture manner while achieving an accurate cross-domain performance. Extensive experiments in a real-world dataset demonstrate that Wi-AM can recognize gestures in an unseen domain with average accuracy of 82.13% and 86.76% for 1 and 3 data samples.

Wi-Diag: Robust Multisubject Abnormal Gait Diagnosis With Commodity Wi-Fi

Wi-Diag: Robust Multisubject Abnormal Gait Diagnosis With Commodity Wi-Fi

Multiple WiFi Access Points Co-Localization Through Joint AoA Estimation

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.

WiSOM: WiFi-enabled self-adaptive system for monitoring the occupancy in smart buildings

There has been extensive research on building energy saving (BES), which aims to reduce energy consumption inside buildings. One of the key solutions for energy saving in buildings is to reduce energy consumption in areas that are not occupied by inhabitants. However, effective monitoring of occupants for energy-saving purposes can be challenging due to unpredictable variations in the indoor environment, such as variations in space size, furniture arrangement, the nature of occupants’ activities (e.g., varied intensities and instances), and penetration losses of walls. Unfortunately, the existing solutions for occupancy monitoring in smart buildings, such as PIR sensors, CO2 sensors, and cameras, etc., are expensive, require excessive maintenance, and are not adaptable to the complex variations in indoor environments. This paper introduces WiSOM, for occupancy detection that utilizes the channel state information (CSI), of commodity WiFi. The method is self-adaptive and designed to handle complex variations in indoor environments. We conducted a thorough analysis of WiSOM and evaluated it under various indoor conditions, including the impact of multipath effects, the detection of different intensities and instances of activities of daily living (ADL), and the impact of wall absorption in a real-home scenario. Our evaluation demonstrated an average detection rate of 98.25% for multipath effects, 96.5% and 98.1% for different intensities and instances of ADL, and 94.4% for wall absorption. Additionally, we assessed WiSOM’s resilience to temporal variation in the CSI and achieved a false alarm rate of less than 2%. In comparison to recent baselines, WiSOM outperformed, achieving up to a 21% improvement in detection rate within real-house scenarios.

CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

CSI-RFF: Leveraging Micro-Signals on CSI for RF Fingerprinting of Commodity WiFi

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

Gesture recognition using WiFi sensing is an emerging and innovative approach that leverages the variations in WiFi signals caused by human hand movements to enable non-contact and intuitive human-computer interaction. The identification of the hand used to perform a gesture significantly impacts the channel state information (CSI) characteristics. It plays a crucial role in data analysis and interpretation. This paper presents a model for classifying the gesture and the hand with which it is performed. We use an Intel 5300 network interface card as a receiver and a commodity WiFi router to collect the CSI values. The CSI values are collected for six left-hand and six right-hand gestures (push left, push right, increase, decrease, stop, and next). The collected CSI values are pre-processed to find the CSI ratio among the sub-carriers. Then four different methods including proposed method are used for classifying the hand and the gesture. The four methods are (1) use of the Hampel filter, (2) use of CSI ratio only, (3) use of correlation matrix along with CSI ratio, and (4) proposed CSI ratio and interquartile distance based method. Then various machine learning techniques such as KNN, Decision trees, and SVM are used to classify the hand (right or left) used for signaling the gestures and the type of gestures. It is found that using the top 30 inter-quartile distance of the CSI-ratios corresponding to the CSI values of the sub-carriers while preserving their positions as the feature vector, the KNN machine learning model can classify the hand and gesture with approximately 99% accuracy for test data. Similar performance is also observed for other performance evaluation parameters like precision and recall.

Enabling WiFi Sensing on New-generation WiFi Cards

The last few years have witnessed the rapid development of WiFi sensing with a large spectrum of applications enabled. However, existing works mainly leverage the obsolete 802.11n WiFi cards (i.e., Intel 5300 and Atheros AR9k series cards) for sensing. On the other hand, the mainstream WiFi protocols currently in use are 802.11ac/ax and commodity WiFi products on the market are equipped with new-generation WiFi chips such as Broadcom BCM43794 and Qualcomm QCN5054. After conducting some benchmark experiments, we find that WiFi sensing has problems working on these new cards. The new communication features (e.g., MU-MIMO) designed to facilitate data transmissions negatively impact WiFi sensing. Conventional CSI base signals such as CSI amplitude and/or CSI phase difference between antennas which worked well on Intel 5300 802.11n WiFi card may fail on new cards. In this paper, we propose delicate signal processing schemes to make wireless sensing work well on these new WiFi cards. We employ two typical sensing applications, i.e., human respiration monitoring and human trajectory tracking to demonstrate the effectiveness of the proposed schemes. We believe it is critical to ensure WiFi sensing compatible with the latest WiFi protocols and this work moves one important step towards real-life adoption of WiFi sensing.

Channel Adapted Antenna Augmentation for Improved Wi-Fi Throughput

This paper investigates how the expansion of array size may improve the spatial diversity of state-of-the-art Wi-Fi system and increase its throughput. With comprehensive Wi-Fi measurement studies with augmented antennas, we identify the potential performance gain atop spatial diversity gains from existing technologies like MIMO and beamforming. We propose WINAS, a general Wi-Fi intelligent antenna selection scheme with full system implementation that can be easily integrated with commodity Wi-Fi AP. WINAS provides substantially improved throughput for downlink traffics. Our experimental evaluation suggests that WINAS improves Wi-Fi throughput up to 1.56x, and 1.47x in average, in real user-based evaluation.

CrossTrack: Device-Free Cross-Link Tracking With Commodity Wi-Fi

Device-free Wi-Fi tracking has become essential for ubiquitous wireless sensing. However, current device-free Wi-Fi tracking systems suffer from two limitations: First, Abnormal signals interfere with tracking performance when the user walks across the direct link of the transceivers; Second, The tracking error based on the velocity integral accumulates over time. This paper proposes CrossTrack, the first device-free cross-link tracking system with commodity Wi-Fi. Our inspiration is to regard the cross-link behavior as an opportunity to correct the trajectory instead of disturbing noise like previous work. Our approach involves three main steps. First, we devise a metric that is capable of detecting cross-link behavior. Second, we propose a new theoretical model that identifies the cross-link position as a landmark. Third, we develop a path revision technique that utilizes this landmark to optimize the trajectory. The technique innovation of the paper is to reveal the theoretical approach to transform cross-link interference into optimization in device-free tracking for the first time. We implement CrossTrack based on commercial Wi-Fi devices and conduct comprehensive experiments. Our results show that CrossTrack can reduce tracking errors by 48.75%, and the median tracking error is 0.41m.

Toward Multi-User Authentication Using WiFi Signals

User authentication nowadays has become an important support for not only security guarantees but also emerging novel applications. Although WiFi signal-based user authentication has achieved initial success, it works in single-user scenarios while multi-user authentication remains a challenging task. In this paper, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> , a multi-user authentication system that can authenticate multiple users with a single pair of commodity WiFi devices. The basic idea is to profile multipath components of WiFi signals, and leverage the multipath components to characterize each user individually for multi-user authentication. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> first profiles multipath components of WiFi signals through a proposed MUltipath Time-of-Arrival estimation algorithm (MUTA). Then, after matching corresponding multipath components to each user in complex multi-user scenarios, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> constructs individual CSI based on the multipath components to characterize each user individually. An AoA-based approach is exploited to further separate individual CSI constructed by the users with same ToA. To identify users through their activities, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> extracts user behavior profiles based on the individual CSI, and leverages a dual-task neural network for robust user authentication. Extensive experiments involving 3 simultaneously present users demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MultiAuth</i> is effective in multi-user authentication with 86.2% average accuracy and 9.5% average false accept rate.

Multi-Person Passive WiFi Indoor Localization With Intelligent Reflecting Surface

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.

WiFE: WiFi and Vision Based Unobtrusive Emotion Recognition via Gesture and Facial Expression

Emotion plays a critical role in making the computer more human-like. As the first and most essential step, emotion recognition emerges recently as a hot but relatively nascent topic, i.e., current research mainly focuses on single modality (e.g., facial expression) while human emotion expressions are multi-modal in nature. To this end, we propose a unobtrusive emotion recognition system leveraging two emotion-rich and tightly-coupled modalities, i.e., gesture and facial expression. The system design faces two major challenges, namely, how to capture the emotional expression in both modalities without disturbing the subject and how to leverage the relationship between modalities for recognizing the emotion. For the former, we explore WiFi and vision for unobtrusive and contactless gesture and facial expression sensing, respectively. For the latter, we propose a novel deep learning framework named Multi-Source Learning (MSL) to efficiently exploit both self-correlation in the modality and cross-correlation between modalities for fine-grained emotion recognition. To evaluate the proposed method, we prototype the system on low-cost commodity WiFi and vision devices, build a first-of-its-kind WiFi-Vision emotion dataset, and conduct extensive experiments. Empirical results not only verify the effectiveness of WiFE in emotion recognition, but also confirm the superiority of multi-modality over single-modality.