Multidomain Correlation-Based Multidimensional CSI Tensor Generation for Device-FreeWi-Fi Sensing

Due to the fine-grained communication scenarios characterization and stability,Wi-Fi channel state information(CSI)has been increasingly applied to indoor sensing tasks recently.Although spatial variations are explicitlyreflected in CSI measurements,the representation differences caused by small contextual changes are easilysubmerged in the fluctuations of multipath effects,especially in device-free Wi-Fi sensing.Most existing datasolutions cannot fully exploit the temporal,spatial,and frequency information carried by CSI,which results ininsufficient sensing resolution for indoor scenario changes.As a result,the well-liked machine learning(ML)-based CSI sensing models still struggling with stable performance.This paper formulates a time-frequency matrixon the premise of demonstrating that the CSI has low-rank potential and then proposes a distributed factorizationalgorithm to effectively separate the stable structured information and context fluctuations in the CSI matrix.Finally,a multidimensional tensor is generated by combining the time-frequency gradients of CSI,which containsrich and fine-grained real-time contextual information.Extensive evaluations and case studies highlight thesuperiority of the proposal.

Two-person device-free localization system based on ZigBee and transformer

Most studies on device-free localization currently focus on single-person scenarios.This paper proposes a novel method for device-free localization that utilizes ZigBee received signal strength indication(RSSI)and a Transformer network structure.The method aims to address the limited research and low accuracy of two-person device-free localization.This paper first describes the construction of the sensor network used for collecting ZigBee RSSI.It then examines the format and features of ZigBee data packages.The algorithm design of this paper is then introduced.The box plot method is used to identify abnormal data points,and a neural network is used to establish the mapping model between ZigBee RSSI matrix and localization coordinates.This neural network includes a Transformer encoder layer as the encoder and a fully connected network as the decoder.The proposed method's classification accuracy was experimentally tested in an online test stage,resulting in an accuracy rate of 98.79%.In conclusion,the proposed two-person localization system is novel and has demonstrated high accuracy.

A Review of Device-Free Indoor Positioning for Home-Based Care of the Aged:Techniques and Technologies

With the development of urbanization,the problem of neurological diseases brought about by population aging has gradually become a social problem of worldwide concern.Aging leads to gradual degeneration of the central nervous system,shrinkage of brain tissue,and decline in physical function in many elderlies,making them susceptible to neurological diseases such as Alzheimer’s disease(AD),stroke,Parkinson’s and major depressive disorder(MDD).Due to the influence of these neurological diseases,the elderly have troubles such as memory loss,inability to move,falling,and getting lost,which seriously affect their quality of life.Tracking and positioning of elderly with neurological diseases and keeping track of their location in real-time are necessary and crucial in order to detect and treat dangerous and unexpected situations in time.Considering that the elderly with neurological diseases forget to wear a positioning device or have mobility problems due to carrying a positioning device,device-free positioning as a passive positioning technology that detects device-free individuals is more suitable than traditional active positioning for the home-based care of the elderly with neurological diseases.This paper provides an extensive and in-depth survey of device-free indoor positioning technology for home-based care and an in-depth analysis of the main features of current positioning systems,as well as the techniques,technologies andmethods they employ,fromthe perspective of the needs of the elderly with neurological conditions.Moreover,evaluation criteria and possible solutions of positioning techniques for the home-based care of the elderly with neurological conditions are proposed.Finally,the opportunities and challenges for the development of indoor positioning technology in 6G mobile networks for home-based care of the elderly with neurological diseases are discussed.This review has provided comprehensive and effective tracking and positioning techniques,technologies and methods for the elderly,by which we can obtain the location information of the elderly in real-time and make home-based care more comfortable and safer for the elderly with neurological diseases.

Multi-Person Device-Free Gesture Recognition Using mmWave Signals

Device-free gesture recognition is an emerging wireless sensing technique which could recognize gestures by analyzing its influence on surrounding wireless signals,it may empower wireless networks with the augmented sensing ability.Researchers have made great achievements for singleperson device-free gesture recognition.However,when multiple persons conduct gestures simultaneously,the received signals will be mixed together,and thus traditional methods would not work well anymore.Moreover,the anonymity of persons and the change in the surrounding environment would cause feature shift and mismatch,and thus the recognition accuracy would degrade remarkably.To address these problems,we explore and exploit the diversity of spatial information and propose a multidimensional analysis method to separate the gesture feature of each person using a focusing sensing strategy.Meanwhile,we also present a deep-learning based robust device free gesture recognition framework,which leverages an adversarial approach to extract robust gesture feature that is insensitive to the change of persons and environment.Furthermore,we also develop a 77GHz mmWave prototype system and evaluate the proposed methods extensively.Experimental results reveal that the proposed system can achieve average accuracies of 93%and 84%when 10 gestures are conducted in Received:Jun.18,2020 Revised:Aug.06,2020 Editor:Ning Ge different environments by two and four persons simultaneously,respectively.

Device-Free Through-the-Wall Activity Recognition Using Bi-Directional Long Short-Term Memory and WiFi Channel State Information

Activity recognition plays a key role in health management and security.Traditional approaches are based on vision or wearables,which only work under the line of sight(LOS)or require the targets to carry dedicated devices.As human bodies and their movements have influences on WiFi propagation,this paper proposes the recognition of human activities by analyzing the channel state information(CSI)from the WiFi physical layer.The method requires only the commodity:WiFi transmitters and receivers that can operate through a wall,under LOS and non-line of sight(NLOS),while the targets are not required to carry dedicated devices.After collecting CSI,the discrete wavelet transform is applied to reduce the noise,followed by outlier detection based on the local outlier factor to extract the activity segment.Activity recognition is fulfilled by using the bi-directional long short-term memory that takes the sequential features into consideration.Experiments in through-the-wall environments achieve recognition accuracy>95%for six common activities,such as standing up,squatting down,walking,running,jumping,and falling,outperforming existing work in this field.

Deep learning and transfer learning for device-free human activity recognition:A survey

Device-free activity recognition plays a crucial role in smart building,security,and human–computer interaction,which shows its strength in its convenience and cost-efficiency.Traditional machine learning has made significant progress by heuristic hand-crafted features and statistical models,but it suffers from the limitation of manual feature design.Deep learning overcomes such issues by automatic high-level feature extraction,but its performance degrades due to the requirement of massive annotated data and cross-site issues.To deal with these problems,transfer learning helps to transfer knowledge from existing datasets while dealing with the negative effect of background dynamics.This paper surveys the recent progress of deep learning and transfer learning for device-free activity recognition.We begin with the motivation of deep learning and transfer learning,and then introduce the major sensor modalities.Then the deep and transfer learning techniques for device-free human activity recognition are introduced.Eventually,insights on existing works and grand challenges are summarized and presented to promote future research.

Device-free human micro-activity recognition method using WiFi signals

Human activity tracking plays a vital role in human–computer interaction.Traditional human activity recognition(HAR)methods adopt special devices,such as cameras and sensors,to track both macro-and micro-activities.Recently,wireless signals have been exploited to track human motion and activities in indoor environments without additional equipment.This study proposes a device-free WiFi-based micro-activity recognition method that leverages the channel state information(CSI)of wireless signals.Different from existed CSI-based microactivity recognition methods,the proposed method extracts both amplitude and phase information from CSI,thereby providing more information and increasing detection accuracy.The proposed method harnesses an effective signal processing technique to reveal the unique patterns of each activity.We applied a machine learning algorithm to recognize the proposed micro-activities.The proposed method has been evaluated in both line of sight(LOS)and none line of sight(NLOS)scenarios,and the empirical results demonstrate the effectiveness of the proposed method with several users.

Adaptive Scheme for Crowd Counting Using off-the-Shelf Wireless Routers

Since the outbreak of the world-wide novel coronavirus pandemic,crowd counting in public areas,such as in shopping centers and in commercial streets,has gained popularity among public health administrations for preventing the crowds from gathering.In this paper,we propose a novel adaptive method for crowd counting based on Wi-Fi channel state information(CSI)by using common commercial wireless routers.Compared with previous researches on device-free crowd counting,our proposed method is more adaptive to the change of environ-ment and can achieve high accuracy of crowd count estimation.Because the dis-tance between access point(AP)and monitor point(MP)is typically non-fixed in real-world applications,the strength of received signals varies and makes the tra-ditional amplitude-related models to perform poorly in different environments.In order to achieve adaptivity of the crowd count estimation model,we used convo-lutional neural network(ConvNet)to extract features from correlation coefficient matrix of subcarriers which are insensitive to the change of received signal strength.We conducted experiments in university classroom settings and our model achieved an overall accuracy of 97.79%in estimating a variable number of participants.

A novel ophthalmic viscosurgical devicefree phakic intraocular lens implantation makes myopic surgery safer

Purpose:To assess the efficacy and safety of a novel ophthalmic viscosurgical device-free(OVD-free)method for posterior chamber phakic intraocular lens(PIOL)implantation in myopic eyes.Methods:In this retrospective cohort study,the medical records of myopic eyes that underwent PIOL(Implantable Collamer Lens,ICL)implantation for myopia correction at the Eye Hospital of Wenzhou Medical University between May 2015 and March 2017 were reviewed.A total of 49 eyes with complete data that met follow up requirements(2 h,1 day,1 week,3 months postoperatively)were recruited.Based on the surgical techniques used,the eyes were divided into the OVD-free method group and the standard method group.The clinical data,including intraocular pressure(IOP),corrected distance visual acuity(CDVA)and spherical equivalent(SE),at each follow-up were collected for comparison.Endothelial cell loss and complications were also investigated.Results:Twenty-one eyes received the standard method,and 28 eyes received the OVD-free method.A rise in IOP>22 mmHg at 2 h was noted in 14 eyes(66.7%)in the standard group and none(0%)in the OVD-free group(p<0.001).The rise in IOP from baseline was significantly higher at 2 h in the standard group(10.5±5.2 mmHg vs.2.2±3.3 mmHg,difference:8.3,95%CI 5.8 to 10.8;p<0.001).There was a significant difference in the time course of LogMAR CDVA changes between the two groups(p=0.047).The LogMAR CDVA was significantly better in the OVD-free method group compared to the standard group at 1 day(−0.076,95%CI−0.134 to−0.018;p=0.012),1 week(−0.071,95%CI−0.135 to−0.007;p=0.03),but not at 3 months(−0.046,95%CI−0.107 to 0.015;p=0.134).There was no significant difference in the time course of SE changes between the two groups(p=0.471;p=0.705).In the OVD-free group,mean endothelial cell loss was 4.6%at 3 months(2522±281 vs.2407±226 cells/mm^(2),difference:-115,95%CI−295 to 65;p=0.187).No complications were reported in both groups except for the early IOP elevation in the standard group during the observation period.Conclusions:The OVD-free method is safe and efficient for ICL implantation.It can be a safer method of ICL implantation compared to the standard method in that it completely eliminates ophthalmic viscoelastic devicesrelated complications without causing additional complications.

Sensorless Sensing with WiFi

Can WiFi signals be used for sensing purpose？ The growing PHY layer capabilities of WiFi has made it possible to reuse WiFi signals for both communication and sensing. Sensing via WiFi would enable remote sensing without wearable sensors, simultaneous perception and data transmission without extra communication infrastructure, and contactless sensing in privacy-preserving mode. Due to the popularity of WiFi devices and the ubiquitous deployment of WiFi networks, WiFi-based sensing networks, if fully connected, would potentially rank as one of the world＇s largest wireless sensor networks. Yet the concept of wireless and sensorless sensing is not the simple combination of WiFi and radar. It seeks breakthroughs from dedicated radar systems, and aims to balance between low cost and high accuracy, to meet the rising demand for pervasive environment perception in everyday life. Despite increasing research interest, wireless sensing is still in its infancy. Through introductions on basic principles and working prototypes, we review the feasibilities and limitations of wireless, sensorless, and contactless sensing via WiFi. We envision this article as a brief primer on wireless sensing for interested readers to explore this open and largely unexplored field and create next-generation wireless and mobile computing applications.

Robust and Passive Motion Detection with COTS WiFi Devices

Device-free Passive（DfP） detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength（RSS）, most recent studies rely on finer-grained Channel State Information（CSI）. However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight（LOS） or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection（R-PMD）. Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis（PCA）-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.