Wi-Fi Signals Research Articles

A Contactless Breathing Pattern Recognition System Using Deep Learning and WiFi Signal

A Contactless Breathing Pattern Recognition System Using Deep Learning and WiFi Signal

Ambient nano RF-Energy driven self-powered wearable multimodal real-time health monitoring

As the trend of population aging intensifies, the demand for continuous and efficient health monitoring for solitary elderly individuals and those with limited self-care capabilities is growing. Traditional wearable health monitoring devices primarily rely on battery power, which not only incurs high maintenance costs but also risks interruption of monitoring due to battery depletion. To address these issues, this paper introduces a self-powered flexible wearable monitoring device utilizing far-field Radio Frequency Energy Harvesting (RFEH) technology. This device powers integrated sensors and Bluetooth by harvesting RF energy from ambient Wi-Fi and other wireless signals, enabling real-time monitoring of the wearer's physical behaviour and health status with immediate feedback via mobile terminals. Under conditions of 100 cm distance and a power intensity of 0.8 dBm, the system can charge a 220 μF capacitor to 4.12 V within just 23.24 seconds, ensuring stable operation of the device. Moreover, the monitoring device is equipped with a low-power wireless sensor system capable of sampling up to 100 Hz, which accurately and promptly tracks and analyzes key health indicators such as walking, physiological activities, and respiratory status. This technology provides a reliable health monitoring solution for elderly individuals living alone and those with difficulties in self-care, significantly enhancing the effectiveness of remote medical services, improving their quality of life, and reducing the occurrence of emergency medical events. This research not only advances wearable device technology but also paves new paths for health management in an aging society.

A comprehensive algorithm for vertical positioning in multi-building environments as an advancement in indoor floor-level detection

The proliferation of smartphones has catalyzed diverse services, mainly focusing on indoor localization to determine users’ and devices’ positions within buildings. Despite decades of exploration, the seamless integration of wireless technologies in tracking devices and users has become pivotal in various sectors, including health, industry, disaster management, building operations, and surveillance. Extensive research in laboratory and industrial settings, particularly in wireless sensor networks and robotics, has informed indoor localization techniques. This paper, referencing surveys and original literature reviews, proposes an innovative indoor location system amalgamating GPS and barometer readings. GPS identifies entry through doors, while barometer readings facilitate accurate floor-level tracking. The integration promises continuous real-time location updates, enhancing security, navigation, and emergency response. Notably, the algorithm is infrastructure-independent, relying on the smartphone’s barometer, and versatile, detecting elevator travel when Wi-Fi AP or LTE signals are available. Results indicate high accuracy, with building entry exceeding 93%, elevator recognition achieving 75% sensitivity and 97% specificity, and floor change detection surpassing 95% sensitivity and nearly 98% specificity (which translates to nearly 97% accuracy). This comprehensive solution, emphasizing the critical role of precise vertical positioning, signifies an advancement in tracking within urban structures.

Development of Wi-Fi based Moving Target Tracking System for Precise Shooting

In modern times where analysis of foot traffic is essential for effective resources management and security, moving target systems that use Wi-Fi-based movement counter offers an innovation solution. This paper summarizes the fundamental idea, the methodology as well as the potential uses of the system. The proposed system makes use of the widespread characteristic of Wi-Fi networks to detect and track the movement of moving targets within a given area. With a thorough analysis of Wi-Fi signals generated by tablets, smartphones or any other wireless-enabled device carried by people, the proposed system is capable of accurately counting and recording the movements of individuals in real-time. The system operates passively by eliminating the requirement for sensors or manually counting. Furthermore, it captures live data, which allows rapid making of decisions regarding security, crowd management and resource allocation. Potential possibilities for applications of the wireless moving target counter are diverse. For retail environments they can help enhance the store's layout and personnel according to peak times for traffic as well as popular pathways. Moving target counters that use Wi-Fi provides a high-end yet easy option for precise and instant pedestrian analysis. The potential for it to transform the management of crowds and improve security across a variety of settings is a powerful device for today's environments.

WiFi-CSI Difference Paradigm

Passive tracking plays a fundamental role in numerous applications such as elderly care, security surveillance, and smart home. To utilize ubiquitous WiFi signals for passive tracking, the Doppler speed extracted from WiFi CSI (Channel State Information) is the key information. Despite the progress made, existing approaches still require a large number of samples to achieve accurate Doppler speed estimation. To enable WiFi sensing with minimum amount of interference on WiFi communication, accurate Doppler speed estimation with fewer CSI samples is crucial. To achieve this, we build a passive WiFi tracking system which employs a novel CSI difference paradigm instead of CSI for Doppler speed estimation. In this paper, we provide the first deep dive into the potential of CSI difference for fine-grained Doppler speed estimation. Theoretically, our new design allows us to estimate Doppler speed with just three samples. While conventional methods only adopt phase information for Doppler estimation, we creatively fuse both phase and amplitude information to improve Doppler estimation accuracy. Extensive experiments show that our solution outperforms the state-of-the-art approaches, achieving higher accuracy with fewer CSI samples. Based on this proposed WiFi-CSI difference paradigm, we build a prototype passive tracking system which can accurately track a person with a median error lower than 34 cm, achieving similar accuracy compared to the state-of-the-art systems, while significantly reducing the required number of samples to only 5%.

Wave-CapNet: A Wavelet Neuron-based Wi-Fi Sensing Model for Human Identification

Gait is regarded as a unique feature for identifying people, and gait recognition is the basis of various customized services of the IoT. Unlike traditional techniques for identifying people, the Wi-Fi-based technique is unconstrained by illumination conditions and such that it eliminates the need for dense, specialized sensors and wearable devices. Although deep learning-based sensing models are conducive to the development of Wi-Fi-based identification, the latter technique relies on a large amount of data and requires a long training time, where this limits the scope of its use for identifying people. In this study, we propose a Wi-Fi sensing model called Wave-CapNet for human identification. We use data processing to eliminate errors in the raw data so that the model can extract the characteristics in channel state information (CSI). We also design a dedicated adaptive wavelet neural network to extract representative features from Wi-Fi signals with only a few epochs of training and a small number of parameters. Experiments show that it can identify human gait with an average accuracy of 99%. Moreover, it can achieve an average accuracy of 95% by using only 10% of the data and fewer than five epochs and outperforms state-of-the-art (SOTA) methods.

TomFi: Small Object Tracking Using Commodity WiFi

Rodent infestation has always been one of the most severe threats to humans, which to solve consumes massive manpower and resources. People usually use traps or poisons to treat rat infestation. Such passive countermeasures are inefficient. Damage often occurs when the rats are finally trapped or killed, not to mention the potential risk to injure humans or cause pollution. In this article, we propose a WiFi-based active small object tracking system named TomFi . The core components of TomFi include several deep learning techniques that bridge rat locations/motions and WiFi signal variations (represented by the channel state information). TomFi first employs a detection model to detect the appearance of the rat and then localize it based on a two-branch localization model. Through the design of the two-branch localization model, to our best knowledge, we are the first to solve the information loss and distortion problem. We conducted extensive experiments in both the laboratory environment and real-world kitchen scenarios. The results show that TomFi can achieve a detection success rate of 99%+ and a centimeter-level localization accuracy in real time.

Rectenna System Development Using Harmonic Balance and S-Parameters for an RF Energy Harvester.

With the escalating demand for Radio Frequency Identification (RFID) technology and the Internet of Things (IoT), there is a growing need for sustainable and autonomous power solutions to energize low-powered devices. Consequently, there is a critical imperative to mitigate dependency on batteries during passive operation. This paper proposes the conceptual framework of rectenna architecture-based radio frequency energy harvesters' performance, specifically optimized for low-power device applications. The proposed prototype utilizes the surroundings' Wi-Fi signals within the 2.4 GHz frequency band. The design integrates a seven-stage Cockroft-Walton rectifier featuring a Schottky diode HSMS286C and MA4E2054B1-1146T, a low-pass filter, and a fractal antenna. Preliminary simulations conducted using Advanced Design System (ADS) reveal that a voltage of 3.53 V can be harvested by employing a 1.57 mm thickness Rogers 5880 printed circuit board (PCB) substrate with an MA4E2054B1-1146T rectifier prototype, given a minimum power input of -10 dBm (0.1 mW). Integrating the fabricated rectifier and fractal antenna successfully yields a 1.5 V DC output from Wi-Fi signals, demonstrable by illuminating a red LED. These findings underscore the viability of deploying a fractal antenna-based radio frequency (RF) harvester for empowering small electronic devices.

Contactless WIFI Sensing and Monitoring for Future Healthcare – Emerging Trends, Challenges and Opportunities Dr G.Kiranmaye,

WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the Cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Feature decoupling and regeneration towards wifi-based human activity recognition

Gesture recognition using WiFi is vital for human–computer interaction, smart homes, smart spaces, and elderly care. WiFi signals are non-stationary, sensitive to the environment, and traditional pattern recognition-based HAR methods incur exorbitant training and deployment costs due to their reliance on the quality and quantity of training data. While there has been extensive research on enhancing HAR techniques, the efficiency of the system is still limited by the scarcity of training data and identity-action feature coupling. This research focuses on attaining gesture recognition across multiple users with a limited number of samples. Our findings show that the conventional data augmentation methods are incapable of facilitating the system in attaining sample diversity. As a result, we propose a training-free augmentation strategy as a means to provide adequate training data. Unlike the conventional data enhancement approach, this scheme aims to develop data processing methods that differentiate between various samples in practical applications. Consequently, it enhances data pertinent to specific HAR assignments effectively. To effectively extract action features in WiFi samples, an unsupervised cross-user domain sample generation (CUDSG) model is proposed. This model generates virtual gesture samples for new user domains by decoupling and recombining gesture and identity features. This extends the sensing boundary of the system to new user domains without requiring a significant number of users to participate. Model performance was evaluated using various classifiers, such as SVM, KNN and CNN. The results demonstrate a significant improvement in average classification accuracy from 57.3% to 98.4%. This indicates that CUDSG is a highly effective tool for enhancing the performance of existing gesture recognition techniques.

Seeing Through Walls with Wi-Fi: Wi Vi Technology and its Security Implications

he technology known as WI- FI, or wireless networking, allows users to connect to the internet wirelessly “Wi-Fi" sis the acronym for “WIRELESS FIDELITY. “Data is transferred between transmitters and receivers using Wi-Fi waves. WI-VI (WIRELESS VISION) is a new technology introduced by WI-FI. WIVI is a device that uses WI-FI signals to see through any object on the wall. In a closed room, we can detect the number of moving humans. In the future, we will concentrate on detecting more than three moving objects using WI-FI signals. WI- VI can detect people's gestures and relative location via the WI-FI signal. WIVI technology can be utilized in the tax department to detect black money and weapons, as well as for security purpose. Wivi is used for defense at home. Wivi technology can help us with door-face detection. Within a limited radius of no more than 1000 square feet, Wi-Fi is linked to Wi-Fi. Wi-Vi technology can also be used to assist in paramedic circumstances, like a land slide. Wi-Fi, a popular technology, is nothing more than an information channel between transmitter and receiver. We demonstrate in this work that Wi-Fi can also extend our senses, allowing us to view moving objects across walls and behind closed doors. So, can Wi-Fi signals be used to identify persons in a closed room based on their Relative location? Yes, we Through the use of a technology known as Wi-Vi, things in Confined rooms can be identified. Simple movements can also be identified and combined behind the wall to express messages. This introduces two major changes. Initially, MIMO interference nulling is used to reduce static object reflections and focus on moving objects. It then demonstrates how to follow a human by treating. Keyword- Wi-Fi Sensing, Through-Wall Imaging, MIMO Interference Nulling, Object Detection, Moving Object, wivi, Detection, Human Gesture Recognition, Indoor Localization

Multi-WiIR: Multi-User Identity Legitimacy Authentication Based on WiFi Device

With the proliferation of WiFi devices, WiFi-based identification technology has garnered attention in the security domain and has demonstrated initial success. Nonetheless, when untrained illegitimate users appear, the classifier tends to categorize them as if they were trained users. In response to this issue, researchers have proposed identity legitimacy authentication systems to identify illicit users, albeit only applicable to individual users. In this article, we propose a multi-user legitimacy authentication system based on WiFi, termed Multi-WiIR. Leveraging WiFi signals, the system captures users’ walking patterns to ascertain their legitimacy. The core concept entails training a multi-branch deep neural network, designated WiIR-Net, for feature extraction of individual users. Binary classifiers are then applied to each user, and legitimacy is established by comparing the model’s output to predefined thresholds, thus facilitating multi-user legitimacy authentication. Moreover, the study experimentally investigated the impact of the number of legitimate individuals on accuracy rates. The results demonstrated that The Multi-WiIR system showed commendable performance with low latency, being capable of conducting legitimacy recognition in scenarios involving up to four users, with an accuracy rate reaching 85.11%.

Exploiting channel state information of WiFi signal for human activity detection: an experimental study

Ubiquitous computing aims to seamlessly integrate computing into our daily lives, and requires reliable information on human activities and state for various applications. In this paper, we propose a device-free human activity recognition system that leverages the rich information behind WiFi signals to detect human activities in indoor environments, including walking, sitting, and standing. The key idea of our system is to use the dynamic features of activities, which we carefully examine and analyze through the characteristics of channel state information. We evaluate the impact of location changes on WiFi signal distribution for different activities and design an activity detection system that employs signal processing techniques to extract discriminative features from wireless signals in the frequency and temporal domains. We implement our system on a single off-the-shelf WiFi device connecting to a commercial wireless access point and evaluate it in laboratory and conference room environments. Our experiments demonstrate the feasibility of using WiFi signals for device-free human activity recognition, which could provide a practical and non-intrusive solution for indoor monitoring and ubiquitous computing applications.

Pa-Count: Passenger Counting in Vehicles Using Wi-Fi Signals

Passenger counting is crucial for many applications such as vehicle scheduling and traffic capacity assessment. However, most of the existing solutions are either high-cost, privacy invasive or not suitable for passengers the vehicle scenarios. In this work, we propose the <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pa</u> - <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Count</u> , an effective real-time <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pa</u> ssenger <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Count</u> ing system deployed inside the vehicle via using Wi-Fi <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSI</i> (Channel State Information). Specifically, in Pa-Count, we design a set of combined filters to eliminate environmental interference and enhance CSI quality. In so doing, we can identify the fluctuation of weak CSI caused by passengers' subtle movement, i.e., the fidgeting, and then obtain the distribution of fidgeting period and silent period. Following that, we describe the subtle movements of passengers via power law with exponential cutoff distribution and establish a counting model based on the queuing theory. A mathematical inference method with a priori probability is devised to calculate the number of real-time passengers through CSI. We evaluate the performance of the Pa-Count by conducting a set of experiments in real-world vehicle scenarios (including private car and subway). Experimental results show that Pa-Count can achieve robust performance with an average accuracy of over 92 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\%$</tex-math></inline-formula> .

Leto: Crowdsourced Radio Map Construction With Learned Topology and a Few Landmarks

Existing crowdsourced indoor positioning systems (CIPSs) usually require prior knowledge about the site and a tedious calibration process. Moreover, they may require a large number of landmarks while ignoring the topology information that may be contained in the crowdsourced data. In this paper, we present Leto, a system that uses <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">le</b> arned <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">to</b> pology information from combined user traces to construct a radio map. Leto relies on crowdsourced WiFi and accelerometer signals only without requiring any prior knowledge about the site. Our key idea is that learned topology information can reduce the required number of landmarks, while available landmarks can transform the topology into a map. We propose a novel framework that efficiently learns the map topology by a hybrid multidimensional scaling (HMDS) algorithm and accurately rectifies the map using only a few anchors by an adaptive force-directed (AFD) algorithm. We also provide a theoretical convergence analysis of the HMDS algorithm. Experimental results on real-world datasets show that Leto can capture useful topology information and achieve significant improvements in radio map construction compared to existing systems.

A fingerprint location framework for uneven WiFi signals based on machine learning

A fingerprint location framework for uneven WiFi signals based on machine learning

WiFi-Tally: Crowd Monitoring and Counting: Review

Abstract: WiFiTally is a cutting-edge technology designed for monitoring and counting crowds in public spaces using Wi-Fi signals. Imagine a system that can track and analyze the movement of people in areas like shopping malls, airports, or event venues without invading privacy. WiFiTally achieves this by tapping into the Wi-Fi signals emitted by smartphones and other connected devices carried by individuals. In simple terms, WiFiTally observes the patterns of Wi-Fi signals within a designated area, providing insights into crowd density and movement. Instead of relying on traditional methods like cameras, this technology prioritizes privacy by using existing Wi-Fi signals that people willingly emit from their devices. The process involves collecting anonymous data from Wi-Fi signals and transforming it into valuable information about crowd size and flow. It doesn't identify individuals; instead, it focuses on overall trends, helping businesses and event organizers make informed decisions about crowd management. WiFiTally offers a non-intrusive and privacy-friendly solution for understanding and optimizing crowd dynamics. This abstract aims to introduce the concept of WiFiTally crowd monitoring and counting, emphasizing its user-friendly approach and respect for personal privacy in public spaces.

Weighted Visibility Graph Based WiFi Indoor Positioning Method Using Heuristic Optimization

With the widespread use of wireless communication technologies and IoT applications, researchers are developing approaches that utilize WiFi signals for indoor location determination. In this study, indoor positioning process based on heuristic optimization-based methods was performed by creating weighted visibility matrices of access points based on WiFi signal strength (RSSI) values. In the proposed method, the PSO and GA approaches determine the position of the mobile user using a common fitness function based on the visibility weight matrices. The proposed method has been tested on a virtual scenario where position ranges based on RSSI ranges are determined. Both heuristic optimization methods are compared according to different criteria and the positioning process is performed with a maximum error of 3m for the GA based method and a maximum of 1.5m for the PSO based method.

Robust gesture recognition method toward intelligent environment using Wi-Fi signals

Wi-Fi-based gesture recognition represents an emerging paradigm of human–computer interaction. While deep learning-based and model-based solutions are crucial for improving accuracy and generalization performance, they often depend heavily on extensive datasets and the widespread deployment of sensing devices. In this paper, we address the challenge involving limited sensing data and devices. We propose a signal-based gesture recognition method that leverages gesture coding based on stroke trajectory and feature representation based on dynamic phase changes. Specifically, we develop an endpoint detection algorithm to ensure precise gesture recognition. Additionally, a subcarrier selection algorithm is designed to select optimal subcarriers, capturing comprehensive gesture information. Extensive experiments are conducted to evaluate the performance. Results demonstrate an average accuracy of 93.67% for six gestures. This approach effectively mitigates the impact of location and environment factors on gesture characteristics, and reduces dependence on large quantity of samples and transceiver devices, obviating the need for model training.

U-Shape Networks Are Unified Backbones for Human Action Understanding From Wi-Fi Signals

U-Shape Networks Are Unified Backbones for Human Action Understanding From Wi-Fi Signals