scholarly journals Device-Free Human Identification Using Behavior Signatures in WiFi Sensing

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5921
Author(s):  
Ronghui Zhang ◽  
Xiaojun Jing
Keyword(s):  
Human Identification ◽  
Signal Propagation ◽  
The Body ◽  
Channel State ◽  
Fine Grained ◽  
Novel Device ◽  
Average Accuracy ◽  
Wireless Signal ◽  
Device Free ◽  
Attention Function

Wireless sensing can be used for human identification by mining and quantifying individual behavior effects on wireless signal propagation. This work proposes a novel device-free biometric (DFB) system, WirelessID, that explores the joint human fine-grained behavior and body physical signatures embedded in channel state information (CSI) by extracting spatiotemporal features. In addition, the signal fluctuations corresponding to different parts of the body contribute differently to the identification performance. Inspired by the success of the attention mechanism in computer vision (CV), thus, to extract more robust features, we introduce the spatiotemporal attention function into our system. To evaluate the performance, commercial WiFi devices are used for prototyping WirelessID in a real laboratory environment with an average accuracy of 93.14% and a best accuracy of 97.72% for five individuals.

scholarly journals 5G-enabled contactless multi-user presence and activity detection for independent assisted living

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Aboajeila Milad Ashleibta ◽  
Ahmad Taha ◽  
Muhammad Aurangzeb Khan ◽  
William Taylor ◽  
Ahsen Tahir ◽  
...  
Keyword(s):  
Deep Learning ◽  
Assisted Living ◽  
Well Being ◽  
Home Health ◽  
Activity Monitoring ◽  
Single Subject ◽  
Channel State ◽  
Average Accuracy ◽  
Wireless Signal ◽  
Simultaneous Identification

AbstractWireless sensing is the state-of-the-art technique for next generation health activity monitoring. Smart homes and healthcare centres have a demand for multi-subject health activity monitoring to cater for future requirements. 5G-sensing coupled with deep learning models has enabled smart health monitoring systems, which have the potential to classify multiple activities based on variations in channel state information (CSI) of wireless signals. Proposed is the first 5G-enabled system operating at 3.75 GHz for multi-subject, in-home health activity monitoring, to the best of the authors’ knowledge. Classified are activities of daily life performed by up to 4 subjects, in 16 categories. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. The CSI amplitudes obtained from 51 subcarriers of the wireless signal are processed and combined to capture variations due to simultaneous multi-subject movements. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system provides a high average accuracy of 91.25% for single subject movements and an overall high multi-class accuracy of 83% for 4 subjects and 16 classification categories. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being.

scholarly journals WiGRUNT: WiFi-enabled Gesture Recognition Using Dual-attention Network

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Yantong Wang ◽  
Meng Wang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Channel State ◽  
Attention Network ◽  
Fine Grained ◽  
Domain Specific ◽  
Cross Domain ◽  
Independent Features ◽  
Device Free ◽  
Domain Independent

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know ``\emph{where to look}'' and ``\emph{when to look}''. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

scholarly journals A Robust Passive Intrusion Detection System with Commodity WiFi Devices

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Enjie Ding ◽  
Xiansheng Li ◽  
Tong Zhao ◽  
Lei Zhang ◽  
Yanjun Hu
Keyword(s):  
Intrusion Detection ◽  
Motion Detection ◽  
Detection System ◽  
Free Motion ◽  
Indoor Environments ◽  
Channel State ◽  
Detection Techniques ◽  
Novel Approach ◽  
Average Accuracy ◽  
Device Free

In recent years, due to the rapidly growing capacities of physical layer, device-free passive detection holds great importance for a broad range of application. Most recent works focus on motion detection, intrusion detection, and vital sign with commodity WiFi devices in the indoor environment. Conventional device-free motion detection techniques, which utilize received signal strength (RSS), may suffer from coarse granularity and high variability problems. In resorting to the finer-grained channel state information (CSI), we propose PhaseMode, a novel approach for device-free motion detection leveraging CSI phase difference data between adjacent antenna pairs. We implement our approach on commercial WiFi devices and validate its performance. We conduct experiments in different test periods of three indoor environments; the results show that the proposed scheme achieves an average accuracy over 99.4% of motion detection in different scenarios.

scholarly journals WiGRUNT: WiFi-enabled Gesture Recognition Using Dual-attention Network

2021 ◽  
Author(s):  
Yu Gu ◽  
Xiang Zhang ◽  
Yantong Wang ◽  
Meng Wang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Gesture Recognition ◽  
Recognition System ◽  
Channel State ◽  
Attention Network ◽  
Fine Grained ◽  
Domain Specific ◽  
Cross Domain ◽  
Independent Features ◽  
Device Free ◽  
Domain Independent

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don't know ``\emph{where to look}'' and ``\emph{when to look}''. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

scholarly journals A Device-Free Indoor Localization Method Using CSI with Wi-Fi Signals

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3233 ◽  
Author(s):  
Xiaochao Dang ◽  
Xuhao Tang ◽  
Zhanjun Hao ◽  
Yang Liu
Keyword(s):  
Indoor Localization ◽  
Indoor Positioning ◽  
Location Based Service ◽  
Channel State ◽  
Position Information ◽  
Fine Grained ◽  
Positioning Method ◽  
Fingerprint Database ◽  
Device Free ◽  
Limited Accuracy

Amid the ever-accelerated development of wireless communication technology, we have become increasingly demanding for location-based service; thus, passive indoor positioning has gained widespread attention. Channel State Information (CSI), as it can provide more detailed and fine-grained information, has been followed by researchers. Existing indoor positioning methods, however, are vulnerable to the environment and thus fail to fully reflect all the position features, due to limited accuracy of the fingerprint. As a solution, a CSI-based passive indoor positioning method was proposed, Wavelet Domain Denoising (WDD) was adopted to deal with the collected CSI amplitude, and the CSI phase information was unwound and transformed linearly in the offline phase. The post-processed amplitude and phase were taken as fingerprint data to build a fingerprint database, correlating with reference point position information. Results of experimental data analyzed under two different environments show that the present method boasts lower positioning error and higher stability than similar methods and can offer decimeter-level positioning accuracy.

CSMC

2021 ◽  
Vol 5 (4) ◽  
pp. 1-22
Author(s):  
Hai Wang ◽  
Baoshen Guo ◽  
Shuai Wang ◽  
Tian He ◽  
Desheng Zhang
Keyword(s):  
Large Scale ◽  
Mobile Network ◽  
Signal Propagation ◽  
Spectrum Management ◽  
External Information ◽  
Temporal Uncertainty ◽  
Large Area ◽  
Fine Grained ◽  
Map Construction ◽  
Spatio Temporal

The rise concern about mobile communication performance has driven the growing demand for the construction of mobile network signal maps which are widely utilized in network monitoring, spectrum management, and indoor/outdoor localization. Existing studies such as time-consuming and labor-intensive site surveys are difficult to maintain an update-to-date finegrained signal map within a large area. The mobile crowdsensing (MCS) paradigm is a promising approach for building signal maps because collecting large-scale MCS data is low-cost and with little extra-efforts. However, the dynamic environment and the mobility of the crowd cause spatio-temporal uncertainty and sparsity of MCS. In this work, we leverage MCS as an opportunity to conduct the city-wide mobile network signal map construction. We propose a fine-grained city-wide Cellular Signal Map Construction (CSMC) framework to address two challenges including (i) the problem of missing and unreliable MCS data; (ii) spatio-temporal uncertainty of signal propagation. In particular, CSMC captures spatio-temporal characteristics of signals from both inter- and intra- cellular base stations and conducts missing signal recovery with Bayesian tensor decomposition to build large-area fine-grained signal maps. Furthermore, CSMC develops a context-aware multi-view fusion network to make full use of external information and enhance signal map construction accuracy. To evaluate the performance of CSMC, we conduct extensive experiments and ablation studies on a large-scale dataset with over 200GB MCS signal records collected from Shanghai. Experimental results demonstrate that our model outperforms state-of-the-art baselines in the accuracy of signal estimation and user localization.

DAFI

2021 ◽  
Vol 5 (4) ◽  
pp. 1-21
Author(s):  
Hang Li ◽  
Xi Chen ◽  
Ju Wang ◽  
Di Wu ◽  
Xue Liu
Keyword(s):  
Indoor Localization ◽  
Domain Adaptation ◽  
Target Domain ◽  
Localization Accuracy ◽  
Source Domain ◽  
Fine Grained ◽  
Time Signals ◽  
Environmental Variations ◽  
Device Free ◽  
Localization Approach

WiFi-based Device-free Passive (DfP) indoor localization systems liberate their users from carrying dedicated sensors or smartphones, and thus provide a non-intrusive and pleasant experience. Although existing fingerprint-based systems achieve sub-meter-level localization accuracy by training location classifiers/regressors on WiFi signal fingerprints, they are usually vulnerable to small variations in an environment. A daily change, e.g., displacement of a chair, may cause a big inconsistency between the recorded fingerprints and the real-time signals, leading to significant localization errors. In this paper, we introduce a Domain Adaptation WiFi (DAFI) localization approach to address the problem. DAFI formulates this fingerprint inconsistency issue as a domain adaptation problem, where the original environment is the source domain and the changed environment is the target domain. Directly applying existing domain adaptation methods to our specific problem is challenging, since it is generally hard to distinguish the variations in the different WiFi domains (i.e., signal changes caused by different environmental variations). DAFI embraces the following techniques to tackle this challenge. 1) DAFI aligns both marginal and conditional distributions of features in different domains. 2) Inside the target domain, DAFI squeezes the marginal distribution of every class to be more concentrated at its center. 3) Between two domains, DAFI conducts fine-grained alignment by forcing every target-domain class to better align with its source-domain counterpart. By doing these, DAFI outperforms the state of the art by up to 14.2% in real-world experiments.

FG-LiquID

2021 ◽  
Vol 5 (3) ◽  
pp. 1-27
Author(s):  
Yumeng Liang ◽  
Anfu Zhou ◽  
Huanhuan Zhang ◽  
Xinzhe Wen ◽  
Huadong Ma
Keyword(s):  
Alcohol Concentration ◽  
Coarse Grained ◽  
Robust Identification ◽  
Fine Grained ◽  
Fresh Milk ◽  
Average Accuracy ◽  
Coarse Grained Material ◽  
Potential Applications ◽  
Fine Grained Material ◽  
The Difference

Contact-less liquid identification via wireless sensing has diverse potential applications in our daily life, such as identifying alcohol content in liquids, distinguishing spoiled and fresh milk, and even detecting water contamination. Recent works have verified the feasibility of utilizing mmWave radar to perform coarse-grained material identification, e.g., discriminating liquid and carpet. However, they do not fully exploit the sensing limits of mmWave in terms of fine-grained material classification. In this paper, we propose FG-LiquID, an accurate and robust system for fine-grained liquid identification. To achieve the desired fine granularity, FG-LiquID first focuses on the small but informative region of the mmWave spectrum, so as to extract the most discriminative features of liquids. Then we design a novel neural network, which uncovers and leverages the hidden signal patterns across multiple antennas on mmWave sensors. In this way, FG-LiquID learns to calibrate signals and finally eliminate the adverse effect of location interference caused by minor displacement/rotation of the liquid container, which ensures robust identification towards daily usage scenarios. Extensive experimental results using a custom-build prototype demonstrate that FG-LiquID can accurately distinguish 30 different liquids with an average accuracy of 97%, under 5 different scenarios. More importantly, it can discriminate quite similar liquids, such as liquors with the difference of only 1% alcohol concentration by volume.

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