High-g Shocking Testing of the Martlet Wireless Sensing System

This article reports the latest development of a wireless sensing system,named Martlet,on high-g shock acceleration measurement.The Martlet sensing node design is based on a Texas Instruments Piccolo microcontroller,with clock frequency programmable up to 90 MHz.The high clock frequency of the microcontroller enables Martlet to support high-frequency data acquisition and high-speed onboard computation.In addition,the extensible design of the Martlet node conveniently allows incorporation of multiple sensor boards.In this study,a high-g accelerometer interface board is developed to allow Martlet to work with the selected microelectromechanical system(MEMS)high-g accelerometers.Besides low-pass and highpass filters,amplification gains are also implemented on the high-g accelerometer interface board.Laboratory impact experiments are conducted to validate the performance of the Martlet wireless sensing system with the high-g accelerometer board.The results of this study show that the performance of the wireless sensing system is comparable to the cabled system.

An epidermal electrode based triboelectric walking energy harvester for wearable wireless sensing applications

Harvesting bio-kinetic energy using a triboelectric nanogenerator(TENG) is one of the promising routes to solve the sustainable energy supply problem for wearable electronics. However, additional materials, complex fabricating processes or specific mechanical structures are needed for existing TENGs to harvest bio-kinetic energy. Besides, they need to be tightly attached to the human body, which may result in detachment and malfunction under tense human motion. Herein, an intrinsic epidermal electrode-based TENG(E-TENG) is proposed to harvest human walking energy. The wearing shoes and ground are used as tribo-materials, and the human epidermis is used as the back electrode of the E-TENG. Compared with the traditional TENGs,the E-TENG does not need any additional tribo-materials and complex mechanical structures. Under optimal conditions, the voltage output of E-TENG can reach 914 V. E-TENG has been used as a self-powered warning sensor and pedometer sensor for demonstration. Furthermore, E-TENG based self-powered wireless sensor system has been developed using a newly designed micro energy electronic switch(MEES). Ambient ultraviolet intensity, temperature and humidity information can be monitored and then transmitted to mobile phone every 3.5 min, demonstrating great potential for widespread wearable applications.

Research on recognition algorithm for gesture page turning based on wireless sensing

When a human body moves within the coverage range of Wi-Fi signals,the reflected Wi-Fi signals by the various parts of the human body change the propagation path,so analysis of the channel state data can achieve the perception of the human motion.By extracting the Channel State Information(CSI)related to human motion from the Wi-Fi signals and analyzing it with the introduced machine learning classification algorithm,the human motion in the spatial environment can be perceived.On the basis of this theory,this paper proposed an algorithm of human behavior recognition based on CSI wireless sensing to realize deviceless and over-the-air slide turning.This algorithm collects the environmental information containing upward or downward wave in a conference room scene,uses the local outlier factor detection algorithm to segment the actions,and then the time domain features are extracted to train Support Vector Machine(SVM)and eXtreme Gradient Boosting(XGBoost)classification modules.The experimental results show that the average accuracy of the XGBoost module sensing slide flipping can reach 94%,and the SVM module can reach 89%,so the module could be extended to the field of smart classroom and significantly improve speech efficiency.

Literature Review on Wireless Sensing Wi-Fi Signal-Based Recognition of Human Activities

With the rapid development and wide deployment of wireless technology, Wi-Fi signals have no longer been confined to the Internet as a communication medium. Wi-Fi signals will be modulated again by human actions when propagating indoors, carrying rich human body state information. Therefore, a novel wireless sensing technology is gradually emerging that can realize gesture recognition, human daily activity detection, identification,indoor localization and human body tracking, vital signs detection, imaging, and emotional recognition by extracting effective feature information about human actions from Wi-Fi signals. Researchers mainly use channel state information or frequency modulated carrier wave in their current implementation schemes of wireless sensing technology, called "Walls have eyes", and these schemes cover radio-frequency technology, signal processing technology, and machine learning. These available wireless sensing systems can be used in many applications such as smart home, medical health care, search-and-rescue, security, and with the high precision and passively device-free through-wall detection function. This paper elaborates the research actuality and summarizes each system structure and the basic principles of various wireless sensing applications in detail. Meanwhile, two popular implementation schemes are analyzed. In addition, the future diversely application prospects of wireless sensing systems are presented.

Indoor Human Fall Detection Algorithm Based on Wireless Sensing

As the main health threat to the elderly living alone and performing indoor activities,falls have attracted great attention from institutions and society.Currently,fall detection systems are mainly based on wear sensors,environmental sensors,and computer vision,which need to be worn or require complex equipment construction.However,they have limitations and will interfere with the daily life of the elderly.On the basis of the indoor propagation theory of wireless signals,this paper proposes a conceptual verification module using Wi-Fi signals to identify human fall behavior.The module can detect falls without invading privacy and affecting human comfort and has the advantages of noninvasive,robustness,universality,and low price.The module combines digital signal processing technology and machine learning technology.This paper analyzes and processes the channel state information(CSI)data of wireless signals,and the local outlier factor algorithm is used to find the abnormal CSI sequence.The support vector machine and extreme gradient boosting algorithms are used for classification,recognition,and comparative research.Experimental results show that the average accuracy of fall detection based on wireless sensing is more than 90%.This work has important social significance in ensuring the safety of the elderly.

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.

Optical SDMA for applying compressive sensing in WSN

In order to apply compressive sensing in wireless sensor network, inside the nodes cluster classified by the spatial correlation, we propose that a cluster head adopts free space optical communication with space division multiple access, and a sensor node uses a modulating retro-reflector for communication. Thus while a random sampling matrix is used to guide the establishment of links between head cluster and sensor nodes, the random linear projection is accomplished. To establish multiple links at the same time, an optical space division multiple access antenna is designed. It works in fixed beams switching mode and consists of optic lens with a large field of view（FOV）, fiber array on the focal plane which is used to realize virtual channels segmentation, direction of arrival sensor, optical matrix switch and controller. Based on the angles of nodes＇ laser beams, by dynamically changing the route, optical matrix switch actualizes the multi-beam full duplex tracking receiving and transmission. Due to the structure of fiber array, there will be several fade zones both in the focal plane and in lens＇ FOV. In order to lower the impact of fade zones and harmonize multibeam, a fiber array adjustment is designed. By theoretical, simulated and experimental study, the antenna＇s qualitative feasibility is validated.

Resource Allocation for Uplink CSI Sensing Report in Multi-User WLAN Sensing

Sensing in wireless local area network(WLAN) gains great interests recently. In this paper we focus on the multi-user WLAN sensing problem under the existing 802.11 standards. Multiple stations perform sensing with the access point and transmit channel state information(CSI)report simultaneously on the basis of uplink-orthogonal frequency division multiple access(OFDMA). Considering the transmission resource consumed in CSI report and the padding wastage in OFDMA based CSI report, we optimize the CSI simplification and uplink resource unit(RU)allocation jointly, aiming to balance the sensing accuracy and padding wastage performances in WLAN sensing. We propose the minimize padding maximize efficiency(MPME) algorithm to solve the problem and evaluate the performance of the proposed algorithm through extensive simulations.

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.

Self-powered wireless body area network for multi-joint movements monitoring based on contact-separation direct current triboelectric nanogenerators

Body area network has attracted extensive attention for its applications in athletics,medical,diagnosis,and rehabilitation training in the next generation personalized health care solutions.Here,a contact-separation direct current triboelectric nanogenerators(CSDC-TENGs)based selfpowered wireless body area network(SWBAN)is reported that enables multi-joint movements monitoring for human motion.The CSDC-TENG is designed as a flexible active sensor with an internal contact switch,and the flexible substrate makes the TENG-sensor stick onto skin easily.Due to the internal switch,the CSDC-TENG could generate a DC current,a large instantaneous output voltage exceeds 700 V,and an instantaneous power can reach 1.076 W,which is more than 23000 times higher than that of the traditional contact-separation mode TENG in same size and materials without the switch.By coupling with flexible coil,the fixed high-frequency radio signals can be modulated and emitted clearly ranging from 6 to 16 MHz,which can be wirelessly received and demodulated through a reader.Moreover,the SWBAN is demonstrated in a real time monitoring system for joints motion.This work has realized the wearable TENG for self-powered wireless real-time monitoring of body movements driven by low-frequency human daily activities,which may promote a tremendous development of intelligent healthcare,wireless sensing system and body area network.

Energy-efficient digital and wireless IC design for wireless smart sensing

Wireless smart sensing is now widely used in various applications such as health monitoring and structural monitoring.In conventional wireless sensor nodes,significant power is consumed in wirelessly transmitting the raw data.Smart sensing adds local intelligence to the sensor node and reduces the amount of wireless data transmission via on-node digital signal processing.While the total power consumption is reduced compared to conventional wireless sensing,the power consumption of the digital processing becomes as dominant as wireless data transmission.This paper reviews the state-of-the-art energy-efficient digital and wireless IC design techniques for reducing the power consumption of the wireless smart sensor node to prolong battery life and enable self-powered applications.

Optimal transmit beamforming for near-field integrated sensing and wireless power transfer systems

The integrated sensing and wireless power transfer(ISWPT)technology,in which the radar sensing and wireless power transfer functionalities are implemented using the same hardware platform,has been recently proposed.In this paper,we consider a near-field ISWPT system where one hybrid transmitter deploys extremely large-scale antenna arrays,and multiple energy receivers are located in the near-field region of the transmitter.Under such a new scenario,we study radar sensing and wireless power transfer performance trade-offs by optimizing the transmit beamforming vectors.In particular,we consider the transmit beampattern matching and max-min beampattern gain design metrics.For each radar performance metric,we aim to achieve the best performance of radar sensing,while guaranteeing the requirement of wireless power transfer.The corresponding beamforming design problems are non-convex,and the semi-definite relaxation(SDR)approach is applied to solve them globally optimally.Finally,numerical results verify the effectiveness of our proposed solutions.

3D-printed microelectronics for integrated circuitry and passive wireless sensors

Three-dimensional(3D)additive manufacturing techniques have been utilized to make 3D electrical components,such as resistors,capacitors,and inductors,as well as circuits and passive wireless sensors.Using the fused deposition modeling technology and a multiple-nozzle system with a printing resolution of 30μm,3D structures with both supporting and sacrificial structures are constructed.After removing the sacrificial materials,suspensions with silver particles are injected subsequently solidified to form metallic elements/interconnects.The prototype results show good characteristics of fabricated 3D microelectronics components,including an inductor–capacitor-resonant tank circuitry with a resonance frequency at 0.53 GHz.A 3D“smart cap”with an embedded inductor–capacitor tank as the wireless passive sensor was demonstrated to monitor the quality of liquid food(e.g.,milk and juice)wirelessly.The result shows a 4.3%resonance frequency shift from milk stored in the room temperature environment for 36 h.This work establishes an innovative approach to construct arbitrary 3D systems with embedded electrical structures as integrated circuitry for various applications,including the demonstrated passive wireless sensors.

Vibration testing of a steel girder bridge using cabled and wireless sensors

Being able to significantly reduce system installation time and cost,wireless sensing technology has attracted much interest in the structural health monitoring(SHM)community.This paper reports the field application of a wireless sensing system on a 4-span highway bridge located in Wayne,New Jersey in the US.Bridge vibration due to traffic and ambient excitation is measured.To enhance the signal-to-noise ratio,a low-noise high-gain signal conditioning module is developed for the wireless sensing system.Nineteen wireless and nineteen cabled accelerometers are first installed along the sidewalk of two neighboring bridge spans.The performance of the wireless sensing system is compared with the high-precision cabled sensing system.In the next series of testing,16 wireless accelerometers are installed under the deck of another bridge span,forming a 4×4 array.Operating deflection analysis is successfully conducted using the wireless measurement of traffic and ambient vibrations.

A Reducing Iteration Orthogonal Matching Pursuit Algorithm for Compressive Sensing

In recent years, Compressed Sensing（CS） has been a hot research topic. It has a wide range of applications, such as image processing and speech signal processing owing to its characteristic of removing redundant information by reducing the sampling rate. The disadvantage of CS is that the number of iterations in a greedy algorithm such as Orthogonal Matching Pursuit（OMP） is fixed, thus limiting reconstruction precision.Therefore, in this study, we present a novel Reducing Iteration Orthogonal Matching Pursuit（RIOMP） algorithm that calculates the correlation of the residual value and measurement matrix to reduce the number of iterations.The conditions for successful signal reconstruction are derived on the basis of detailed mathematical analyses.When compared with the OMP algorithm, the RIOMP algorithm has a smaller reconstruction error. Moreover, the proposed algorithm can accurately reconstruct signals in a shorter running time.

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.

CSI-based passive intrusion detection bound estimation in indoor NLoS scenario

Passive intrusion detection is important for many indoor safety applications.By utilizing widespread WiFi infrastructures,channel state information(CSI)based intrusion detection methods have attractive advantages such as low cost,non-line-of-sight(NLoS)support,and privacy protection.However,existing CSI-based methods lack in-depth and intensive analysis of intrusion detection bound.To the best of our knowledge,this is the first work that precisely characterizes and models CSI-based intrusion detection bound in typical indoor NLoS scenario.A bound is defined as an intruder’s farthest position from the transmitter during intrusion detection.To derive a model for the bound,we first derived an intrusion-disturbed NLoS channel model by analyzing the influence of human intrusion on the NLoS wireless channel.Subsequently,based on the channel model,we further derived an intrusion detection bound model.Based on the derived bound model,we proposed a practical system to estimate the real intrusion detection bound.Extensive experiments were conducted based on practical systems.The experimental and simulation results verified and demonstrated the effectiveness of the derived bound model.Our work not only reveals the fundamental performance limit of the basic intrusion detection method in an indoor NLoS scenario,but also provides a valuable reference for bound estimation for other fine-grained wireless sensing applications.

Breeze-activated wind speed sensor with ultra-low friction resistance for self-powered gale disaster warning

The early warning and monitoring of gale disasters are very important for the safety of people’s lives and properties. Triboelectric nanogenerators(TENGs) are popular for wind speed sensors due to their self-powered property. However, a TENG cannot easily work at low wind speeds due to the limitation of the high frictional resistance structure. In this paper, a TENG-based breezeactivated wind speed sensor(BAWS) with an ultra-low frictional resistance is proposed. The key drive unit of the BAWS is a Savonius-like vertical axis wind turbine, which is fabricated by arrayed airfoil profile blades with excellent flow field characteristics. Here a wind turbine plays dual roles in driving the electromagnetic generator below it to supply energy and lead the TENG above it to sense the wind force. Compared to a classical turbine with a wind cup, the designed turbine has a low resistance torque. The synergistic effect of the drive unit with low-resistance and triboelectric materials with low viscosity allows the BAWS to be activated even at a wind speed of 2.9 m/s. The sensitivities of the voltage frequency and current amplitude of the TENG are used to reflect the electrical property of the BAWS. The measured values are 0.291 Hz/(m·s-1) and 0.221 μA/(m·s-1),which reflects the good sensitivity of the BAWS. Moreover, the linearity of the BAWS reaches up to 0.991, which shows an accurate output for the wind speed. In addition, the device is equipped with a combined electromagnetic-solar unit as the sole power source to meet the sensor’s all-weather operation requirements. This work expands the application prospects of selfpowered sensing technology in the field of disaster warning.

Identification of electrocardiogram signals using internet of things based on combinatory classification

Combination of computer sciences and electronics has resulted in one of the most remarkable technologies of the recent years called internet of things,considered as a challenge in electronic health systems for taking care of patients.Internet of things presents a promising paradigm for management of digital identification in the form of service customization.The effect of internet of things on healthcare is still in its preliminary stages and requires a substantial development.Various equipment and services are developed and utilized for health systems by providing different things to establish communication and information provision to users at any conditions or places.In this paper,attempts have been made to detect electrocardiogram(ECG)signal through a wireless simple sensing network of body using internet of things operating based on classification and feature extraction.