Textile-Based Strain Sensor for Human Motion Detection

Human motion analysis consists of real-time monitoring and recording of human body’s kinematics. It is very essential to track ambulatory and dailylife human motion, which is crucial for many applications and disciplines.Electronic textiles(e-textiles) afford a valid alternative to traditional solidstate sensors due to their merits of low cost, lightweight, flexibility, and feasibility to fit various human bodies. In this mini-review, textile-based sensor platforms and human motion analysis are well discussed in Section 1.Second, theoretical principles of textile-based strain sensors are introduced including resistive, capacitive, and piezoelectrical sensors. Section 3 focuses on various types of textile materials that are functionalized as sensing systems by intrinsic or extrinsic modifications. Section 4 summaries various types of e-textile-based strain sensors for human motion analysis. The final two sections mainly present perspectives and challenges, and conclusions,respectively.

Textile-Based Sensors for Inspection of Composite Materials

The monitoring of mechanical deformation and damage of composite materials is normally performed by established analytical methods,such as strain gauges and optical and piezoelectric sensors.However,large areas outside of the measuring cell remain unconsidered.A large-area sensitive sensor for composites is presented,which is simply generated by printing a carbon layer on the reinforcing glass fiber fabrics or on any composite itself.Such printed sensors enable to determine mechanical deformations and damages of the entire component and it responds with a measurable electrical resistance to external tension or pressure without any hysteresis.The strongest influence on sensor signal was identified with low carbon concentration and thin layers.The sensors signal linearly correlates with the degree of deformation and bending velocity whereas bending direction can be identified through signal change under residual tensile stress or compressive stress.

A personalized electronic textile for ultrasensitive pressure sensing enabled by biocompatible MXene/ PEDOT:PSS composite

Flexible,breathable,and highly sensitive pressure sensors have increasingly become a focal point of interest due to their pivotal role in healthcare monitoring,advanced electronic skin applications,and disease diagnosis.However,traditional methods,involving elastomer film-based substrates or encapsulation techniques,often fall short due to mechanical mismatches,discomfort,lack of breathability,and limitations in sensing abilities.Consequently,there is a pressing need,yet it remains a significant challenge to create pressure sensors that are not only highly breathable,flexible,and comfortable but also sensitive,durable,and biocompatible.Herein,we present a biocompatible and breathable fabric-based pressure sensor,using nonwoven fabrics as both the sensing electrode(coated with MXene/poly(3,4-ethylenedioxythiophene):polystyrene sulfonate[PEDOT:PSS])and the interdigitated electrode(printed with MXene pattern)via a scalable spray-coating and screen-coating technique.The resultant device exhibits commendable air permeability,biocompatibility,and pressure sensing performance,including a remarkable sensitivity(754.5 kPa^(−1)),rapid response/recovery time(180/110 ms),and robust cycling stability.Furthermore,the integration of PEDOT:PSS plays a crucial role in protecting the MXene nanosheets from oxidation,significantly enhancing the device's long-term durability.These outstanding features make this sensor highly suitable for applications in fullrange human activities detection and disease diagnosis.Our study underscores the promising future of flexible pressure sensors in the realm of intelligent wearable electronics,setting a new benchmark for the industry.

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Textile-Based Capacitive Pressure Distribution Measurement System for Human Sitting Posture Monitoring

Flexible pressure monitoring device can help correct the sitting posture and prevent health problems(e.g.,deformity of spinal column and musculoskeletal disease).Currently,most measurement systems hinder their wide applications owing to the high cost or low accuracy.In this study,a flexible sitting pressure measurement system was proposed based on a textile-based capacitive pressure sensor array in order to measure sitting pressure distribution simply and conveniently.The capacitive pressure sensor array is sandwich structure composed of a high-density sponge layer and two electrode array fabrics,which possesses high resolution(2.26 sensors/cm2),high sensitivity(0.701 kPa-1)and fast response(≤35 ms).It is worth noting that the raw materials of the sensing fabric include commercialized copper sheets and polyester yarns.The as-prepared pressure measurement system can accurately measure the pressure distribution nephogram for sitting posture analysis.The sitting pressure of 10 volunteers was measured and six types of posture were distinguished clearly.

In-site health monitoring of cement concrete pavements based on optical fiber sensing technology

Premature stress of cement concrete pavements i the coupled action of construction technique,structural ma-terial and environmental action.It is quite diffiault to accurately get the actual stress distribution merely based on the theoretical or simulation analysis.Ther efore,in-situ health monitoring is particularly si gnificant to obtain the stress or strain information for the assessment on structural perfor mance of cement concrete pavements.To contribute this topic,different kinds of FBG based sensors have been specially designed to measure the tem-perature,pressure and deformation in cement concrete pavements.A relatively long-term monitoring has been aonducted to collect the effective data after the solidification of the pavement lasts for about 15 d.Data analysis indicates that the temperature variation inside the pavement was very stable,with maximum ampltude smaller than 2.25°C in Sep.2020.The longitudinal,transverse and ver tical deformations of the pavement behaved in non-umniform distribution,and partial me asuring points suffered from large tensile force.The concrete course had better deformation resi stance than that of the soil base,and local interfacial micro void defects existed in the soil base.The preliminary results can help to understand the actual structural performance of cement concrete pavements based on the optical fiber sensing sys tem.

A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

A Path-Based Approach for Data Aggregation in Grid-Based Wireless Sensor Networks

Sensor nodes in a wireless sensor network （WSN） are typically powered by batteries, thus the energy is constrained. It is our design goal to efficiently utilize the energy of each sensor node to extend its lifetime, so as to prolong the lifetime of the whole WSN. In this paper, we propose a path-based data aggregation scheme （PBDAS） for grid-based wireless sensor networks. In order to extend the lifetime of a WSN, we construct a grid infrastructure by partitioning the whole sensor field into a grid of cells. Each cell has a head responsible for aggregating its own data with the data sensed by the others in the same cell and then transmitting out. In order to efficiently and rapidly transmit the data to the base station （BS）, we link each cell head to form a chain. Each cell head on the chain takes turn becoming the chain leader responsible for transmitting data to the BS. Aggregated data moves from head to head along the chain, and finally the chain leader transmits to the BS. In PBDAS, only the cell heads need to transmit data toward the BS. Therefore, the data transmissions to the BS substantially decrease. Besides, the cell heads and chain leader are designated in turn according to the energy level so that the energy depletion of nodes is evenly distributed. Simulation results show that the proposed PBDAS extends the lifetime of sensor nodes, so as to make the lifetime of the whole network longer.

Surface Acoustic Wave Humidity Sensors Based on(1120) ZnO Piezoelectric Films Sputtered on R-Sapphire Substrates

ZnO films on R-sapphire substrates are prepared and characterized by x-ray diffraction and scanning electron microscopy, which indicate that the thin films are well crystallized with （1120） texture. Love wave and Rayleigh wave are used for fabrications of humidity sensors, which are excited in [1100] and [0001] directions of the （1120） ZnO piezoelectric films, respectively. The experimental results show that both kinds of sensors have good humidity response and repeatability, and the performances of the Love wave sensors are better than those of the Rayleigh wave sensors at room temperature. Moreover, the theoretical calculations of the mass sensitivity of the sensors are a/so carried out and the calculated results are in good agreement with the experimental measurements.

Carbon Nanotube Based Chemical Sensors

Carbon nanotubes CNTs)have been receiving enormous attention in the last decade due to their extraordinary mechanical properties and unique elec- tronic properties.This combination has produced an unprecedented range of applications for CNTs:elec- tronic,logic and memory chips,chemical and biosen- sots,composites,lithium batteries,gas storage,filters and membranes,etc.This presentation will focus on carbon nanotube based sensors and discuss fabrication, testing and performance.

An intelligent hybrid MAC protocol for a sensor-based personalized healthcare system

Sensors based on personalized healthcare systems have been widely used in the medical field.However,energy limitations have greatly hindered the further development of medical sensors.For the traditional Medium Access Control(MAC)protocol,the duration of low-power listening is fixed because it ignores that the available energy of sensors is different in some situations,which leads to a high delay and low energy utilization.In this paper,a Maximum Listening Length MAC(MLL-MAC)protocol is proposed to fully utilize the energy in the sensor-based systems.The MLL-MAC protocol is an improvement of the Receiver-Initiated(RI)MAC protocol.The main advance is that the sensor node performs the following additional operations:(1)The sender sends a beacon when it wakes up and sends data,thus establishing a communication link with the receiver in the listening state;(2)The receiver keeps listening as long as possible to reduce the delay when it wakes up and listens to the channel,which is different from the previous strategy in which the node turns into a sleep state immediately without receiving data.Furthermore,the sensor node can dynamically determine whether to send beacons and prolong listening duration according to its available energy level.The MLL-MAC protocol is evaluated through theoretical analysis and experimental results.The results show that,compared with the RI-MAC protocol,the MLL-MAC protocol can reduce the average end-to-end delay by 41.4%and improve the energy utilization by 15.1%.

Refractive Plasmonic Sensor Based on Fano Resonances in an Optical System

A symmetric plasmonie structure consisting of metal-insulator metal waveguide, groove studied, which supports double Fano resonances deriving from two different mechanisms and slot cavities is One of the Fano resonances originates from the interference between the resonances of groove and slot cavities, and the other comes from the interference between slot cavities. The spectral line shapes and the peaks of the double Fano resonances can be modulated by changing the length of the slot cavities and the height of the groove. Furthermore, the wavelength of the resonance peak has a linear relationship with the length of the slot cavities. The proposed plasmonic nanosensor possesses a sensitivity of 800nm/RIU and a figure of merit of 3150, which may have important applications in switches, sensors, and nonlinear devices.

Tree Based Energy and Congestion Aware Routing Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have inherent and unique characteristics rather than traditional networks. They have many different constraints, such as computational power, storage capacity, energy supply and etc;of course the most important issue is their energy constraint. Energy aware routing protocol is very important in WSN, but routing protocol which only considers energy has not efficient performance. Therefore considering other parameters beside energy efficiency is crucial for protocols efficiency. Depending on sensor network application, different parameters can be considered for its protocols. Congestion management can affect routing protocol performance. Congestion occurrence in network nodes leads to increasing packet loss and energy consumption. Another parameter which affects routing protocol efficiency is providing fairness in nodes energy consumption. When fairness is not considered in routing process, network will be partitioned very soon and then the network performance will be decreased. In this paper a Tree based Energy and Congestion Aware Routing Protocol (TECARP) is proposed. The proposed protocol is an energy efficient routing protocol which tries to manage congestion and to provide fairness in network. Simulation results shown in this paper imply that the TECARP has achieved its goals.

Rule-based Fault Diagnosis of Hall Sensors and Fault-tolerant Control of PMSM

Hall sensor is widely used for estimating rotor phase of permanent magnet synchronous motor(PMSM). And rotor position is an essential parameter of PMSM control algorithm, hence it is very dangerous if Hall senor faults occur. But there is scarcely any research focusing on fault diagnosis and fault-tolerant control of Hall sensor used in PMSM. From this standpoint, the Hall sensor faults which may occur during the PMSM operating are theoretically analyzed. According to the analysis results, the fault diagnosis algorithm of Hall sensor, which is based on three rules, is proposed to classify the fault phenomena accurately. The rotor phase estimation algorithms, based on one or two Hall sensor(s), are initialized to engender the fault-tolerant control algorithm. The fault diagnosis algorithm can detect 60 Hall fault phenomena in total as well as all detections can be fulfilled in 1/138 rotor rotation period. The fault-tolerant control algorithm can achieve a smooth torque production which means the same control effect as normal control mode (with three Hall sensors). Finally, the PMSM bench test verifies the accuracy and rapidity of fault diagnosis and fault-tolerant control strategies. The fault diagnosis algorithm can detect all Hall sensor faults promptly and fault-tolerant control algorithm allows the PMSM to face failure conditions of one or two Hall sensor(s). In addition, the transitions between health-control and fault-tolerant control conditions are smooth without any additional noise and harshness. Proposed algorithms can deal with the Hall sensor faults of PMSM in real applications, and can be provided to realize the fault diagnosis and fault-tolerant control of PMSM.

Location-Based Routing Protocols for Wireless Sensor Networks: A Survey

Recently, location-based routings in wireless sensor networks (WSNs) are attracting a lot of interest in the research community, especially because of its scalability. In location-based routing, the network size is scalable without increasing the signalling overhead as routing decisions are inherently localized. Here, each node is aware of its position in the network through some positioning device like GPS and uses this information in the routing mechanism. In this paper, we first discuss the basics of WSNs including the architecture of the network, energy consumption for the components of a typical sensor node, and draw a detailed picture of classification of location-based routing protocols. Then, we present a systematic and comprehensive taxonomy of location-based routing protocols, mostly for sensor networks. All the schemes are subsequently discussed in depth. Finally, we conclude the paper with some insights on potential research directions for location-based routing in WSNs.

An Intelligent Sensor Based System for Real Time Heart Rate Monitoring (HRM)

A method and apparatus for monitoring heart rate of the heart using a wearable system is designed and implemented in this paper. A heart rate receives from heart beat signals and stores the data to a database and after a time period this method can determine an idle heart rate of the monitoring body. This idle heart rate is compared with the stored data and can determine the normal and abnormal heart rate variability. After the certain time period this system can detect the heart rate and also can send a signal to the user in time of abnormalities. Consequent estimations of heart rate variability are contrasted with this.

Piezoresistive Response Extraction for Smart Cement-based Composites/Sensors

A kind of piezoresistive response extraction method for smart cement-based composites/sensors was proposed.Two kinds of typical piezoresistive cement-based composites/sensors were fabricated by respectively adding carbon nanotubes and nickel powders as conductive fillers into cement paste or cement mortar.The variation in measured electrical resistance of such cement-based composites/sensors was explored without loading and under repeated compressive loading and impulsive loading.The experimental results indicate that the measured electrical resistance of piezoresistive cement-based composites/sensors exhibits a two-stage variation trend of fast increase and steady increase with measurement time without loading,and an irreversible increase after loading.This results from polarization caused by ionic conduction in these composites/sensors.After reaching a plateau,the measured electrical resistance can be divided into an electrical resistance part and an electrical capacity part.The piezoresistive responses of electrical resistance part in measured electrical resistance to loading can be extracted by eliminating the linear electrical capacity part in measured electrical resistance.

Rule Based Collector Station Selection Scheme for Lossless Data Transmission in Underground Sensor Networks

There are fundamentally two different communication media in wireless underground sensor networks. The first of these is a solid medium where the sensor nodes are buried underground and wirelessly transmit data from underground to aboveground. The second is an underground medium such as tunnel, cave etc. and the data is transmitted from underground to the aboveground through partially solid medium. The quality of communication is greatly influenced by the humidity of the soil in both environments. The placement of wireless underground sensor nodes at hard-to-reach locations makes energy efficient work compulsory. In this paper, rule based collector station selection scheme is proposed for lossless data transmission in underground sensor networks. In order for sensor nodes to transmit energy-efficient lossless data, rulebased selection operations are carried out with the help of fuzzy logic. The proposed wireless underground sensor network is simulated using Riverbed software, and fuzzy logic-based selection scheme is implemented utilizing Matlab software. In order to evaluate the performance of the sensor network;the parameters of delay, throughput and energy consumption are investigated. Examining performance evaluation results, it is seen that average delay and maximum throughput are accomplished in the proposed underground sensor network. Under these conditions, it has been shown that the most appropriate collector station selection decision is made with the aim of minimizing energy consumption.

An ionic liquid supported CeO_2 nanoparticles-carbon nanotubes composite-enhanced electrochemical DNA-based sensor for the detection of Pb^(2+)

An electrochemical sensor incorporating a signal enhancement for the determination of lead （II） ions （Pb2＋） was designed on the basis of the thrombin-binding aptamer （TBA） as a molecular recog- nition element and ionic liquid supported cerium oxide （CeO2） nanoparticles-carbon nanotubes compo- site modification. The composite comprises nanoparticles CeO2, multi-waU carbon nanotubes （MWNTs） and hydrophobic room temperature ionic liquid （RTIL） 1-ethyl-3-methylimidazolium tetrafluoroborate （EMIMBF4）. The electrochemical sensors were fabricated by immersing the CeOa-MWNTs-EMIMBF4 modified glassy carbon electrode （GCE） into the solution of TBA probe. In the presence of Pb2＋, the TBA probe could form stable G-quartet structure by the specific binding interactions between Pb2＋ and TBA. The TBA-bound Pb2＋ can be electrochemically reduced, which provides a readout signal for quantitative detection of Pb2＋. The reduction peak current is linearly related to the concentration of Pb2＋ from 1.0 ＊ 10-8 M to 1.0 ＊ 105 M with a detection limit of 5 ＊ 109 M. This work demonstrates that the CeOz-MWNTs-EMIMBF4 nanocomposite modified GCE provides a promising platform for immobi- lizing the TBA probe and enhancing the sensitivity of the DNA-based sensors.