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Covid-19 Detection Using Organic LED and Photodiode-Based sensor Device

IEEE sensors JOURNAL 2024年第24期24卷 40678-40684页

作者： Yadav, Sugandha Mittal, Poornima Negi, Shubham Delhi Technol Univ Dept Elect & Commun Engn Delhi India Chandigarh Univ Mohali Elect & Commun Dept Mohali 140413 Punjab India

The utilization of fluorescence detection for various biomedical applications such as oximeter sensors, cancer detection, monitoring of protein-DNA interactions, heart stroke detection, etc. has been on the rise which was first introduced by Heyduk and Lee in 1990. The integration of organic light emitting diode and organic photodiode (OPD) is well suited for these kinds of health-care detections. In this article, a methodology is proposed for Covid-19 detection which consists of a highly flexible blue organic LED (OLED), an OPD, and a human saliva sample. An in-depth investigation is being performed on both the OLED and OPD devices to make these devices suitable for the detection of the SARS-COV-2 virus inside human saliva samples. The presented methodology is based on fluorescence detection. Herein, the OLED emits an excitation wavelength of 470 nm and OPD produces two different currents 63.5 and 37.2 mA corresponding to the emission wavelengths of 490 and 525 nm, correspondingly. It is concluded here that if the OPD produces 63.5 mA current, the person is infected by Covid-19 and if it produces 37.2 mA current, the person is healthy. This article also compares the proposed methodology with some other researcher's work. The proposed OLED-OPD integration may be utilized for other sensing applications such as environmental monitoring, multispectral sensing, optical sensing, IoT devices, etc.

关键词： Covid-19 organic LED (OLED) organic photodiode (OPD) sensor V-OTFT

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Autoencoder reconstruction residual-Wasserstein distance based in-situ calibration for indoor environment spatial expansion virtual sensors☆

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ENERGY AND BUILDINGS 2025年 333卷

作者： Shin, Hakjong Jo, Seng-Kyoun Choi, Won-Kyu Elect & Telecommun Res Inst ETRI Agr Anim Aquaculture & Ocean Intelligence Res Ctr Daejeon South Korea

Increasing reliance on digital twin technology for managing indoor environments necessitates the development of spatial expansion virtual sensors (SEVS). However, in practical applications, SEVS performance often deteriorates due to shifts in data distribution and environmental conditions, presenting challenges for consistent reliability. Most existing SEVS research has primarily focused on initial model development, with limited consideration to in-situ calibration strategies. This study introduces an autoencoder reconstruction residualWasserstein distance (AR-WD)-based error estimation model, designed for spatial expansion virtual sensors with the primary objective of enhancing their performance in practical applications. The proposed model utilizes residuals from autoencoders and Wasserstein features, which can be derived without additional sensor installations, for real-time calibration. A comprehensive evaluation was conducted using temperature data from a pigsty, where the AR-WD model demonstrated robust performance across various machine learning algorithms, particularly with random forest and XGBoost, showing high predictive accuracy with a mean absolute error as low as 0.086. These findings suggest that the integration of AR-WD features significantly enhances the reliability and accuracy of virtual sensors. In addition, the AR-WD model leverages the unique characteristics of SEVS to enable real-time error estimation based solely on input data variations, thereby addressing common limitations of non-intrusive calibration methods. This research not only advances the field of virtual sensor development but also provides critical insights for optimizing sensor systems in complex indoor settings.

关键词： Digital twin Virtual sensor Indoor environment Spatial expansion Calibration

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Labyrinthine Wrinkle-Patterned Fiber sensors Based on a 3D Stress Complementary Strategy for Machine Learning-Enabled Medical Monitoring and Action Recognition

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SMALL 2025年第5期21卷 e2407390页

作者： Lv, Yongming Chu, Zhenming Huang, Desheng Fan, Xiaoxing Zhang, Wei Liaoning Univ Sch Phys Liaoning Key Lab Optoelect Funct Devices & Detect Liaoning 110036 Peoples R China China Med Univ Sch Publ Hlth Shenyang 110122 Liaoning Peoples R China China Med Univ Sch Intelligent Med Shenyang 110122 Liaoning Peoples R China

Fiber strain sensors show good application potential in the field of wearable smart fabrics and equipment because of their characteristics of easy deformation and weaving. However, the integration of fiber strain sensors with sensitive response, good stretchability, and effective practical application remains a challenge. Herein, this paper proposes a new strategy based on 3D stress complementation through pre-stretching and swelling processes, and the polydimethylsiloxane (PDMS)/silver nanoparticle (AgNPs)/MXene/carbon nanotubes (CNTs) fiber sensor with the bilayer labyrinthian wrinkles conductive network on the PU fiber surface is fabricated. Benefiting from the wrinkled structure and the synergies of sensitive composite materials, the fiber sensor exhibits good stretchability (>150%), high sensitivity (maximum gauge factor is 57896), ultra-low detection limit (0.1%), fast response/recovery time (177/188 ms) and good long-term durability. It can be used as Morse code issuance and recognition to express the patient's symptoms and feelings. Further, the sensor enables comprehensive human movement monitoring and collects data of different characteristics with the assistance of machine learning, different letters/numbers are recognized and predicted with an accuracy of 99.17% and 99.33%. Therefore, this fiber sensor shows potential as a new generation of flexible strain sensors with applications in medical monitoring and human-computer interaction.

关键词： 3D stresses fiber strain sensors labyrinthine wrinkles machine learning predictions motion monitoring

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Fault location and type identification method for current and voltage sensors in traction rectifiers

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JOURNAL OF POWER ELECTRONICS 2025年第4期25卷 666-674页

作者： Yu, Yunjun Song, Yunquan Tao, Hongwei Nanchang Univ Sch Informat Engn Nanchang Peoples R China Nanchang Univ Minist Educ Engn Res Ctr Watershed Carbon Neutralizat Nanchang Peoples R China

The reliability of rectifiers is regarded as one of the most important factors in traction systems. Unexpected faults occurring in sensors can degrade the performance and lead to secondary faults. Accordingly, a sensor fault diagnosis method is proposed in this paper. It can locate faults and identify fault types. Three high-incidence fault types in current and voltage sensors have been taken into consideration. Only the current residual is needed in the process of fault diagnosis. No additional sensors are required in this method. First, a traction rectifier model is developed. Then, a grid current estimator is constructed, the residual is acquired and applied to fault detection. Next, the residual is analyzed under different kinds of sensor faults. Fault diagnosis functions are constructed and the faults can be diagnosed. Finally, an experiment test is processed to demonstrate the effectiveness of the proposed method.

关键词： sensor fault diagnosis Fault location Fault type identification Rectifiers

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Leader-Follower Control of Rehabilitative Soft Glove Based on Collaborative Sensing and Fine Motion Recognition

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IEEE sensors JOURNAL 2024年第19期24卷 30329-30339页

作者： Zhang, Jianhua Li, Xiangli Li, Hui Wang, Hui Zhang, Jianjun Hao, Yufei Univ Sci & Technol Beijing Sch Mech Engn Beijing 100083 Peoples R China Hebei Univ Technol Sch Mech Engn Tianjin 300401 Peoples R China

Rehabilitative gloves with leader-follower control offer a promising approach for stroke patients with impaired hand function. However, the current integration of sensors in rehabilitative soft gloves is limited, which poses challenges in achieving position feedback and desired bending angles of actuators. Moreover, fine movement perception and recognition add complexity to leader-follower control. Here, we propose a rehabilitative soft glove system with cooperative sensing and fine gesture recognition, utilizing a carbon black (CB) strain sensor and electromyography sensor. The strain sensor has high flexibility and stretchability, and we conducted a comprehensive analysis of its electrical characteristics. It demonstrates an impressive 100% stretchability with a gauge factor of 1.14-1.34. By seamlessly integrating the strain sensors into a data glove, we successfully monitored finger movement and grip. We propose two leader-follower control methods, namely strain sensor-based position control and cooperative sensing-based fine dynamic gesture recognition control. In the former approach, we calibrated of all sensors integrated in the data glove and actuators, enabling position control of the rehabilitative soft glove through an incremental PID controller. For fine dynamic gesture recognition, we introduce a ResNet-ConvLSTM-self-attention (RCSA) deep learning framework that eliminates the need for sensor calibration and achieves an impressive recognition accuracy of 96.20%. Finally, we validate the effectiveness of our proposed control scheme through online experiments with the rehabilitative soft glove.

关键词： Collaborative sensing leader-follower control rehabilitative soft glove strain sensor Collaborative sensing leader-follower control rehabilitative soft glove strain sensor

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Application of FBG sensor in health monitoring of engineering building structure: a review

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sensor REVIEW 2025年第1期45卷 129-145页

作者： Zhou, Chenxia Jia, Zhikun Song, Shaobo Luo, Shigang Zhang, Xiaole Zhang, Xingfang Pei, Xiaoyuan Xu, Zhiwei Tiangong Univ Sch Text Sci & Engn Tianjin Peoples R China HeBeihuabei Petr Engn Construct Co Ltd Shijiazhuang Hebei Peoples R China Carbon Technol Grp Co Ltd Tianjin Peoples R China

Purpose - The aging and deterioration of engineering building structures present significant risks to both life and property. Fiber Bragg grating (FBG) sensors, acclaimed for their outstanding reusability, compact form factor, lightweight construction, heightened sensitivity, immunity to electromagnetic interference and exceptional precision, are increasingly being adopted for structural health monitoring in engineering buildings. This research paper aims to evaluate the current challenges faced by FBG sensors in the engineering building industry. It also anticipates future advancements and trends in their development within this field. Design/methodology/approach - This study centers on five pivotal sectors within the field of structural engineering: bridges, tunnels, pipelines, highways and housing construction. The research delves into the challenges encountered and synthesizes the prospective advancements in each of these areas. Findings - The exceptional performance of FBG sensors provides an ideal solution for comprehensive monitoring of potential structural damages, deformations and settlements in engineering buildings. However, FBG sensors are challenged by issues such as limited monitoring accuracy, underdeveloped packaging techniques, intricate and time-intensive embedding processes, low survival rates and an indeterminate lifespan. Originality/value - This introduces an entirely novel perspective. Addressing the current limitations of FBG sensors, this paper envisions their future evolution. FBG sensors are anticipated to advance into sophisticated multi-layer fiber optic sensing networks, each layer encompassing numerous channels. Data integration technologies will consolidate the acquired information, while big data analytics will identify intricate correlations within the datasets. Concurrently, the combination of finite element modeling and neural networks will enable a comprehensive simulation of the adaptability and longevity of FBG sensors in their

关键词： FBG sensor Engineering building structure Health monitoring Future development trend

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A fiber-shaped sensor constructed by coaxial wet-spinning for dual-mode sensing

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JOURNAL OF MATERIALS CHEMISTRY A 2025年第8期13卷 5870-5881页

作者： Ma, Duixin Fang, Huayang Sun, Jianping Jiang, Tao Guangxi Univ State Key Lab Featured Met Mat & Life Cycle Safety Nanning 530004 Peoples R China Guangxi Univ Finance & Econ Sch Journalism & Cultural Commun Nanning 530004 Guangxi Peoples R China

With the booming development of smart electronic devices, the forms of wearable sensors are gradually diversifying. However, integrating dual sensing capabilities into a single sensor for decoupled strain and humidity detection remains a significant challenge. In this study, we report a flexible dual-modal sensor designed with a "skin-core" structure that integrates pressure and humidity sensing layers, enabling decoupled monitoring of pressure and humidity. Using a coaxial wet-spinning method, we fabricated multifunctional sensing fibers with MXene/CNF as the core and cationic cellulose as the skin. By controlling the ratio of the spinning solution for the core layer, the resulting MXene/CNF@cationic cellulose aerogel fiber (MCC) pressure sensor exhibits high sensitivity (120 kPa-1), rapid response time (50 ms for response, 55 ms for recovery), and excellent cycling stability under compression. Furthermore, the unique structure of the coaxially spun aerogel and the inherent properties of the MCC fiber material endow the sensor with outstanding cycling stability, fast moisture absorption and desorption responses (response time of 9.43 s and recovery time of 5.3 s), and excellent moisture absorption and desorption characteristics. This study promotes the effective utilization of cellulose-based materials in wearable sensing and health management, expands how sensors can be worn, and lays the foundation for the integration of sensors with garments.

关键词： Aerogels

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Real-Time Sleep Apnea Monitoring System Enabled by High-Performance Strain sensors Based on Elastic E-Fibers

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IEEE sensors JOURNAL 2024年第12期24卷 19756-19762页

作者： Bing, Yu Xue, Hua Li, Fan Zhao, Hongran Lin, Xiuzhu Zhang, Tong Jilin Univ Coll Elect Sci & Engn State Key Lab Integrated Opto Elect Changchun 130012 Peoples R China

Breathing, as a vital physiological parameter, holds significance in reflecting the breathing abnormalities associated with sleep apnea syndrome (SAS) through real-time breathing monitoring. Stretchable strain sensors, designed to match the strain patterns of chest and abdominal movements that accompany breathing, can deform to the maximum extent along with the thoracic and abdominal changes, thereby capturing the intricate details of breathing state variations. In this study, we commence with strain sensors to explore and develop a portable, user-friendly, and cost-effective wearable equipment for real-time breathing monitoring. A core-shell elastic thread composed of a hydrophilic shell and an elastic core is utilized as the substrate, a strain sensor is constructed through a simple immersion method, and carbon-based materials are loaded onto the hydrophilic shell layer. The sensor benefits from the unique weaving structure of the shell, exhibiting a high gauge factor (GF) (25.8) and linearity (0.998) within a strain range of 0%-50%, along with excellent stability (>20 000 cycles). integration of the sensor into a system yields a wireless breathing monitoring device capable of real-time tracking of breathing rate and status. This comprehensive research covers the entire development process, encompassing sensor fabrication, software development, and a specific focus on sleep apnea applications.

关键词： Breathing monitoring electronic fibers (e-fibers) strain sensors wearable electronics

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Ultra-Soft Curvature sensor-Based Textile Glove With Real-Time Signal Feedback: Enhancing Human-Machine Interaction

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Lu, Sitong Wang, Yun Yin, Meijuan Sun, Xiaotian Chen, Dianshen Jiao, Jian North China Univ Technol Sch Mech & Mat Engn Beijing 100144 Peoples R China Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China Peng Cheng Lab Shenzhen 518055 Peoples R China

The integration of ultra-soft curvature sensors into textile data gloves is essential to enhance user experience with greater flexibility and adaptability, particularly in various human-machine interface scenarios, including rehabilitation tasks. Current options for such sensors are limited, often relying on strain variance measurements that can introduce significant errors due to stretching interference. Here, the proposed ultra-soft curvature sensor, composed of a soft elastomer and conductive liquid, directly measures curvature with minimal stretching interference, ensuring high performance and reliability. The sensor's microchannel design with embedded nylon ropes and utilizable storage regions provides a wide detection range (0 degrees-180 degrees), low hysteresis (2.46%), and high stability (loading: 3.28%, unloading: 4.29%). This study also presents a data glove integrating flexible curvature sensors and an embedded light-trigger system, aimed at optimizing the human-machine interaction experience through active signal feedback. The design, incorporating real-time feedback mechanisms, is expected to promote physical recovery and a psychological sense of belonging among patients, thereby advancing their autonomy and confidence.

关键词： sensors Microchannels Bending Training Resistance Liquids Deformation Data mining Artificial intelligence Sensitivity Human-machine interface real-time feedback ultra-soft curvature sensor wearable device

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Ultraflexible, High-Stability Woven Yarn sensors With a Wide Detection Range for Enhanced Human-Machine Interaction

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IEEE sensors JOURNAL 2024年第20期24卷 33048-33055页

作者： Song, Linlong Yang, Ping-An Ding, Yifan Mo, Chunyan Yang, Yingtian Gui, Yingang Shao, Yiqian Chen, Qiao Chongqing Univ Posts & Telecommun Sch Automat Chongqing 40065 Peoples R China Peoples Hosp Yubei Dist Chongqing Ophthalmol Dept Chongqing 401120 Peoples R China Southwest Univ Coll Engn & Technol Chongqing 400715 Peoples R China

Flexible sensors play a crucial role in enhancing information transmission and human-machine control efficiency, particularly in challenging environments such as nighttime or dusty conditions encountered in counter-terrorism operations. Combining high stability sensing performance and exceptional flexibility is paramount for effective human-machine interaction facilitated by flexible sensors. Finding a balance between high flexibility, wide detection range, and stability is challenging because they are mutually limiting. In this study, a latex-polyester core-spun elastic yarn with a double-helix structure was used, using a dip-coating process to apply two layers of MWCNTs and one layer of PEDOT:PSS, aiming to enhance the detection range and stability of sensors. The developed yarn sensor exhibited a low modulus (0.09 MPa), a wide detection range (0.025%-200%), and high stability (>20000 cycles). Its flexibility surpassed that of human skin, demonstrating excellent stability under varying rates, strain ranges, and sample lengths. integration of the yarn sensor into sensing gloves facilitated natural gesture recognition in counter-terrorism operations and enabled successful control of unmanned counter-terrorism vehicles, thereby enhancing human-machine interaction. This innovative solution presents significant potential for enhancing the efficiency and accuracy of counter-terrorism operations through improved sensing capabilities.

关键词： sensors Yarn Optical fiber sensors Coatings Strain Image sensors Surface treatment Flexible sensor gesture recognition human-machine interaction sensor glove

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