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Science China(Technological Sciences) 2024年第2期67卷 587-596页

作者： TANG Jian WU YuTing MA ShiDong ZHANG YuMin XU RuoJie YAN Tao PAN ZhiJuan College of Textile and Clothing Engineering Soochow UniversitySuzhou 215123China National Engineering Laboratory for Modern Silk Suzhou 215123China

Various yarn-shaped flexible strain sensors have recently been developed.However,research is lacking on additive manufacturing for smart clothing for integrating yarn sensors with commercial garments.Herein,a strain-sensing yarn is sewn into a piece of fabric through a novel stitching technique,and the influence of the stitching method and needle pitch on the sensing performance is investigated using finite element analysis(FEA).The sensing performance could be improved when the sensing yarn is self-locked in the fabric at the needle eyes,and the needle pitch was reduced to 0.5 cm,which is attributed to the enhanced stress and strain concentration.Meanwhile,the composite sensing fabric featured outstanding performance,including a low detection limit(0.1%),rapid response(280 ms),excellent durability(10000 cycles),and high stability(negligible drift and frequency independence).In addition,the remarkable wear resistance,washability,and anti-interference to ambient humidity and perspiration were obtained.Therein,the optimal stitch trace lengths of sensing yarn for detecting elbow motion,breathing,and heartbeats are discussed.Finally,a smart clothing system composed of smart clothing,data acquisition unit,and mobile APP was developed to simultaneously detect human movement and physiological signals.This work provides a reference to produce intelligent garments based on yarn sensors for health monitoring.

关键词： strain-sensing yarn composite sensing fabric smart clothing system stitching technology health monitoring

Eutectogel-based self-powered wearable sensor for health monitoring in harsh environments

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Nano Research 2024年第6期17卷 5559-5568页

作者： Junpeng Wu Xinru Teng Lu Liu Hongzhi Cui Xiaoyi Li College of Materials Science and Engineering Ocean University of ChinaQingdao 266100China Key Lab for Special Functional Materials of Ministry of Education School of Materials Science and EngineeringHenan UniversityKaifeng 475004China

Triboelectric nanogenerators(TENG)have emerged as a highly promising energy harvesting technology,attracting significant attention in recent years for their broad applications.Gel-based TENGs,with superior stretchability and sensitivity,have been widely reported as wearable sensors.However,the traditional hydrogel-based TENGs suffer from freezing at low temperatures and drying at high temperatures,resulting in malfunctions.In this study,we introduce an anti-freezing eutectogel,which uses a deep eutectic solvent(DES),to improve the stability and electrical conductivity of TENGs in harsh environmental conditions.The eutectogel-based TENG(E-TENG)produces an open-circuit voltage of 776 V,a short-circuit current of 1.54μA,and a maximum peak power of 1.1 mW.Moreover,the E-TENG exhibits exceptional mechanical properties with an elongation at a break of 476%under tension.Importantly,it maintains impressive performances across a wide temperature range from−18 to 60℃,with conductivities of 2.15 S/m at−10℃and 1.75 S/m at−18℃.Based on the excellent weight stability of the E-TENG sensor,motion sensing can be achieved in the air,and even underwater.Finally,the versatility of the E-TENG can serve as a wearable sensor,by integrating it with Bluetooth technology.The self-powered E-TENG can monitor various human motion signals in real-time and send the health signals directly to mobile phones.This research paves a new road for the applications of TENGs in harsh environments,offering wireless flexible sensors with real-time health signal monitoring capabilities.

关键词： triboelectric nanogenerators self-powered harsh environment health monitoring Eutectogel

health monitoring of Milling Tool Inserts Using CNN Architectures Trained by Vibration Spectrograms

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Computer Modeling in Engineering & Sciences 2023年第7期136卷 177-199页

作者： Sonali S.Patil Sujit S.Pardeshi Abhishek D.Patange Department of Mechanical Engineering College of Engineering PunePune411005India

In-process damage to a cutting tool degrades the surface􀀀nish of the job shaped by machining and causes a signi􀀀cant􀀀nancial loss.This stimulates the need for Tool Condition monitoring(TCM)to assist detection of failure before it extends to the worse phase.Machine Learning(ML)based TCM has been extensively explored in the last decade.However,most of the research is now directed toward Deep Learning(DL).The“Deep”formulation,hierarchical compositionality,distributed representation and end-to-end learning of Neural Nets need to be explored to create a generalized TCM framework to perform eciently in a high-noise environment of cross-domain machining.With this motivation,the design of dierent CNN(Convolutional Neural Network)architectures such as AlexNet,ResNet-50,LeNet-5,and VGG-16 is presented in this paper.Real-time spindle vibrations corresponding to healthy and various faulty con􀀀gurations of milling cutter were acquired.This data was transformed into the time-frequency domain and further processed by proposed architectures in graphical form,i.e.,spectrogram.The model is trained,tested,and validated considering dierent datasets and showcased promising results.

关键词： Milling tool inserts health monitoring vibration spectrograms deep learning convolutional neural network

health monitoring of Dry Clutch System Using Deep Learning Approach

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Intelligent Automation & Soft Computing 2023年第8期37卷 1513-1530页

作者： Ganjikunta Chakrapani V.Sugumaran School of Mechanical Engineering VIT University Chennai CampusVandalur-Kelambakkam RoadKeelakottatiyurChennai600127India

Clutch is one of the most significant components in automobiles.To improve passenger safety,reliability and economy of automobiles,advanced supervision and fault diagnostics are required.Condition monitoring is one of the key divisions that can be used to track the reliability of clutch and allied components.The state of the clutch elements can be monitored with the help of vibration signals which contain valuable information required for classification.Specific drawbacks of traditional fault diagnosis techniques like high reliability on human intelligence and the requirement of profes-sional expertise,have made researchers look for intelligent fault diagnosis techniques.In this article,the classification performance of the deep learning technique(employing images plotted from vibration signals)is compared with the machine learning technique(using features extracted from vibration signals)to identify the most viable solution for condition monitoring of the clutch system.The overall experimentation is carried out in two phases,namely the deep learning phase and the machine learning phase.Overall,the effectiveness of the pre-trained networks was assessed and compared with machine learning algorithms.Based on the comparative study,the best-performing technique is recommended for real-time application.

关键词： Deep learning health monitoring pre-trained models transfer learning vibration analysis statistical features

health monitoring-Based Assessment of Reinforcement with Prestressed Steel Strand for Cable-Stayed Bridge

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Structural Durability & health monitoring 2022年第1期16卷 53-80页

作者： Kexin Zhang Tianyu Qi Dachao Li Xingwei Xue Yanfeng Li School of Traffic Engineering Shenyang Jianzhu UniversityShenyang110168China

This paper presents the method of reinforcing main girder of reinforced concrete cable-stayed bridge with prestressed steel strands.To verify the effectiveness of external prestressed strand reinforcement method.Static load tests and health monitoring-based assessment were carried out before and after reinforcement.Field load test shows that the deflection and stress values of the main girder are reduced by 10%~20%after reinforcement,and the flexural strength and stiffness of the strengthened beam are improved.The deflection and strain data of health monitoring of the specified section are collected.The deflection of the second span is 4 mm~10 mm,the strain range of the upper edge of the second span is-10με~-40με,and the strain range of the lower edge is 30με~75με.These values show the deflection and strain values fluctuate within a prescribed range,verifying the safety of the bridge.The reinforcement method of prestressed steel strand is feasible and effective.It can provide reference basis for the application of external prestressed strand reinforcement technology in similar projects.

关键词： Prestressed steel strand reinforcement cable-stayed bridge load testing health monitoring

Internet of things-based health monitoring system for early detection of cardiovascular events during COVID-19 pandemic

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World Journal of Clinical Cases 2022年第26期10卷 9207-9218页

作者： Sina Dami Department of Computer Engineering West Tehran BranchIslamic Azad UniversityTehran ***Iran

The coronavirus disease 2019(COVID-19)has currently caused the mortality of millions of people around the world.Aside from the direct mortality from the COVID-19,the indirect effects of the pandemic have also led to an increase in the mortality rate of other non-COVID patients.Evidence indicates that novel COVID-19 pandemic has caused an inflation in acute cardiovascular mortality,which did not relate to COVID-19 infection.It has in fact increased the risk of death in cardiovascular disease(CVD)patients.For this purpose,it is dramatically inevitable to monitor CVD patients’vital signs and to detect abnormal events before the occurrence of any critical conditions resulted in *** of things(IoT)and health monitoring sensors have improved the medical care systems by enabling latency-sensitive surveillance and computing of large amounts of patients’data.The major challenge being faced currently in this problem is its limited scalability and late detection of cardiovascular events in IoT-based computing environments.To this end,this paper proposes a novel framework to early detection of cardiovascular events based on a deep learning architecture in IoT environments.Experimental results showed that the proposed method was able to detect cardiovascular events with better performance(95.30%average sensitivity and 95.94%mean prediction values).

关键词： health monitoring Early detection Cardiovascular events COVID-19 Pandemic Internet of things

Data Reliability and Sensors Lifetime in Bridge health monitoring using LoRaWAN-Zigbee

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Computers, Materials & Continua 2022年第11期73卷 2663-2678页

作者： Awad Ali Reyazur Rashid Irshad Ahmed Abdu Alattaab Aamir Fatahayab Department of Computer Science College of Science and Arts-SharourahNajran UniversityNajranKingdom of Saudi Arabia Department of Computer Science JJTUJhunjhunuRajasthanIndia

The Wireless Sensor Network(WSN)is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors.In the monitoring region,several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway.The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study.To communicate in a network,two Long–Range Wide Area Network(LoRaWAN)sensor clusters and two Zigbee sensor clusters are employed,together with two Zigbee and LoRaWAN converters.The suggested Golden eagle shepherd optimization(GESO)method then forms Zigbee as well as LoRaWAN networking clusters.Furthermore,depending on energy usage and data packet size,the fitness of each sensor node is assessed using the Dynamic Intelligent Reasoning Based Neural(DIRN)approach.MATLAB software is used to implement and execute this study.When the Zigbee network’s transmission distance is 650 m and the LoRaWAN network’s transmission range is 3.5 km,the system can function with a packet loss rate of less than 0.04 percent.This study shows significant gains in the performance of the system when compared to traditional approaches based on digital findings obtained on software solutions.

关键词： Zigbee LoRaWAN wireless sensor network health monitoring routing protocol

Finger-actuated wireless-charging wearable multifunctional sweatsensing system for levodopa and vitamin C

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Nano Research 2024年第4期17卷 3096-3106页

作者： Qihong Ning Shaoqing Feng Qingwen Sun Ruoyao Yu Tangan Li Hao Xu Daxiang Cui Kan Wang School of Sensing Science and Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityKey Laboratory of Thin Film and Microfabrication Technology(Ministry of Education)Shanghai 200240China Department of Plastic and Reconstructive Surgery Shanghai Ninth People’s HospitalShanghai Jiao Tong University School of MedicineShanghai 200011China School of Naval Architecture Ocean&Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Efficient portable wearable sweat sensors allow the long-term monitoring of changes in the status of biomarkers in sweat,which can be useful in diagnosis,medication,and nutritional assessment.In this study,we designed and tested a wireless,battery-free,flexible,self-pumping sweat-sensing system that simultaneously tracks levodopa and vitamin C levels in human sweat and detects body temperature.The system includes a microfluidic chip with a self-driven pump and anti-reflux valve,a flexible wireless circuit board,and a purpose-designed smartphone app.The microfluidic chip is used for the efficient collection of sweat and the drainage of excess sweat.The dual electrochemical sensing electrodes in the chip are modified with functional materials and appropriate enzymatic reagents,achieving excellent selectivity and stability.The sensitivities of the levodopa sensor and the vitamin C sensor are 0.0073 and 0.0018μA·μM^(-1),respectively,and the detection correlation coefficients of both exceed 0.99.Both sensors have a wide linear detection range of 0–100 and 0–1000μM,respectively,and low detection limits of 0.28 and 17.9μM,respectively.The flexible wireless circuit board is equipped with the functions of wireless charging,electrical signal capture and processing,and wireless transmission.The data recorded from each sensor are displayed on a smartphone via a self-developed app.A series of experimental results confirmed the reliability of the sweat-sensing system in noninvasively monitoring important biomarkers in the human body and its potential utility in the comprehensive assessment of biological health.

关键词： self-pumping microfluidic channel wireless charging wearable sensor health monitoring

Pushing the frontiers: Chip-based detection based on micro-and nano-structures

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Chinese Chemical Letters 2024年第4期35卷 86-98页

作者： Meihui Liu Xinyuan Zhou Xiao Li Zhenjie Xue Tie Wang Tianjin Key Laboratory of Life and health Detection Life and Health Intelligent Research InstituteTianjin University of TechnologyTianjin 300384China

Changes in trace substances in human metabolites, which are related to disease processes and health status, can serve as chemical markers for disease diagnosis and symptom monitoring. Real-time online detection is an inevitable trend for the future of health monitoring, and the construction of chips for detection faces major challenges. The response of sensors often fails to meet the requirements for chipbased detection of trace substances due to the low efficiency of interfacial heterogeneous reactions, necessitating a rational design approach for micro-and nano-structures to improve sensor performance with respect to sensitivity and detection limits. This review focuses on the influence of micro-and nanostructures that used in chip on sensing. Firstly, this review categorizes sensors into chemiresistors, electrochemical sensors, fluorescence sensors, and surface enhanced Raman scattering(SERS) sensors based on their sensing principle, which have significant applications in disease diagnosis. Subsequently, commencing from the application requirements in the field of sensing, this review focuses on the different structures of nanoparticle(NP) assemblies, including wire, layered, core-shell, hollow, concave and deformable structures. These structures change in the size, shape, and morphology of conventional structures to achieve characteristics such as ordered alignment, high specific surface area, space limitation,vertical diffusion, and swaying behavior with fluid, thereby addressing issues such as poor signal transmission efficiency, inadequate adsorption and capture capacity, and slow mass transfer speed during sensing. Finally, the design direction of micro-and nano-structures, and possible obstacles and solutions to promote chip-based detection have been discussed. It is hope that this article will inspire the exploration of interface micro-and nano-structures modulated sensing methods.

关键词： Chip-based detection Micro-and nano-structures Real-time online detection Sensing health monitoring

Feed weight estimation model for health monitoring of meat rabbits based on deep learning

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International Journal of Agricultural and Biological Engineering 2022年第1期15卷 233-240页

作者： Enze Duan Liangju Wang Hongying Wang Hongyun Hao Rangling Li College of Engineering China Agricultural UniversityBeijing 100083China

With the development of precision livestock farming,non-contact health monitoring technology is particularly important in the breeding process.To help improve the management of the rabbit breeding industry,a remaining feed weight(RFW)estimation model was developed based on the image segmentation method.The model proposed in this study consisted of a feed instance segmentation neural network and feed weight estimation network.Feed instance segmentation neural network was based on the improved Mask Region-based Convolution Neural Network(Mask RCNN),the state-of-art image segmentation method,and the PointRend algorithm was used to replace the original network head.Through an adaptive subdivision strategy,the boundary points were segmented with fine details.Features were extracted from the segmentation results and used as the input of the feed weight estimation network based on the Back Propagation(BP)algorithm.The model was applied in rabbit breeding to explore the relationship between RFW and the mortality probability of meat rabbits.The model evaluation results showed that the Average Precision(AP)value of the feed instance segmentation neural network was 0.987,the Mean Pixel Accuracy(MPA)value was 0.985.The correlation coefficient of the feed weight estimation network was 0.97,the Mean Squared Error(MSE)was 208.3,and the Mean Absolute Error(MAE)was 10.51 g.The practical application results showed that the feed intake of the unhealthy meat rabbits would decrease significantly.When the RFW was more than 50%of feed quantity,the mortality probability of the rabbit was more than 85%;when the RFW was more than 65%of feed quantity,all the rabbits finally died in a short time.Therefore,there is a significant correlation between RFW and the mortality probability of rabbits,by which this proposed model can help farms to monitor the health of meat rabbits by predicting RFW.

关键词： meat rabbit remaining feed weight estimation convolutional neural network deep learning health monitoring