IEEE Sensors Journal最新文献

{"title":"Pedestrian Identification System Based on RFID Signaling and Deep Learning","authors":"Linqi Zhao;Pedro Cheong;Wenhai Zhang;Wai-Wa Choi","doi":"10.1109/JSEN.2025.3544298","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544298","url":null,"abstract":"In this article, we propose a new pedestrian identification system based on radio frequency identification (RFID) technology and deep learning algorithms. Our system is designed with a strong focus on privacy protection, in addition to achieving accuracy and reliability. We introduce a 2-D RFID tag array to realize spatial diversity as pedestrians move across the RFID sensor system. The backscattered signal strength indicator (RSSI) and phase angle, affected by the gait and body shape of the pedestrian, carry important personal biometric features for identification. To fully leverage the potential of RFID technology in identification, we propose an innovative neural network model, c3dTAnet, based on attention mechanism and bidirectional long short-term memory (BiLSTM). In the experiment, we have a total of 3060 samples from 50 volunteers and backgrounds to evaluate the performance of the proposed system. The results are promising, with the system achieving 99.1% accuracy in fivefold cross-validation. This demonstrates the significant advantages of our system in both accuracy and training speed over existing pedestrian identification solutions.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12003-12015"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Multisensor Fusion for Remote Sensing Change Detection Using USASE","authors":"Guangyi Shi","doi":"10.1109/JSEN.2025.3543717","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543717","url":null,"abstract":"Binary change detection (BCD) in remote sensing has advanced, yet challenges remain in reducing feature redundancy and effectively utilizing difference information between dual-time images, which affects precision in identifying change areas. In addition, the effective fusion of multisensor data types limits adaptability and accuracy in change detection (CD) models. This article presents the ultralightweight semantic-aware spatial exchange (USASE) network, a three-encoder-three-decoder architecture designed for improved adaptability in multisensor data fusion. USASE integrates a micro convolutional unit (MCU) for reduced feature redundancy through pointwise and depthwise separable convolutions, while a temporal-aware feature aggregation module (TAFAM) captures global semantic relationships to enhance detection precision across sensor types. An adaptive weighting mechanism further optimizes dual-time image accuracy in multisource data fusion. Tested on the SYSU-CD, LEVIR-CD, and DSIFN datasets, USASE achieves the <inline-formula> <tex-math>${F}1$ </tex-math></inline-formula>-scores of 83.12%, 90.72%, and 81.34%, respectively, outperforming several baselines in accuracy, efficiency, and computational cost. This study highlights USASEs potential as a robust, real-time solution for dynamic and complex remote sensing applications.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"12265-12277"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Nonintrusive Fatigue Detection Based on Multidomain Features of Sitting Pressure and Machine Learning","authors":"Haiyan Tu;Xiaoyue Tan;Zhongping Yin;Xuegang Zhang;Kang Yang;Zhengkun Qiu;Xiujuan Zheng","doi":"10.1109/JSEN.2025.3544299","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3544299","url":null,"abstract":"Fatigue significantly reduces productivity and cognitive performance by impairing the concentration and information-processing abilities of individuals. Existing fatigue detection methods often rely on invasive or privacy-disrupting techniques, limiting their applicability in real-world settings. To address these challenges, we propose a nonintrusive fatigue detection method using multidomain features of sitting pressure and machine learning. First, we design a wireless and flexible mat with an embedded pressure sensor array to acquire sitting pressure nonintrusively. Then, we extract multidomain features (time, frequency, and time-frequency domain) and apply feature selection techniques to find an optimal feature subset. We then detect fatigue using well-known machine learning methods based on full domain features and the optimal feature subset, respectively. Experimental results show that utilizing feature selection can improve the performance of fatigue detection and reduce computational complexity. Compared to single classifiers, ensemble learning models require more computational resources but achieve better performance in fatigue detection, with accuracies above 92%. Finally, we use the bootstrap method to evaluate the stability of each model, and the results show that random forest (RF) with the subset excels in terms of stability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11749-11758"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quadrature Signal Extraction by Current Modulation in Self-Mixing Interferometry for Displacement Sensing","authors":"Hanqiao Chen;Shuangxi Zhang;Xiaohan Mao;Desheng Zhu;Xiong Zheng;Zhipeng Dong;Xiulin Wang;Wencai Huang","doi":"10.1109/JSEN.2025.3543725","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543725","url":null,"abstract":"This article proposes a method for generating quadrature self-mixing interference (SMI) signals using the current modulation for real-time displacement sensing. By applying a square-wave current to a semiconductor laser, periodically phase-modulated SMI signals are produced at a certain distance. Subsequently, a pair of quadrature SMI signals is obtained by separately sampling at the high and low levels of the square-wave signal, followed by an interpolation process. Finally, the target’s motion displacement is reconstructed by quadrature phase unwrapping algorithm. The method demonstrates excellent robustness, achieving reconstruction accuracy better than 20 nm under 7-dB Gaussian noise in simulation. Experiments for both sinusoidal vibrations and nonharmonic moving targets are conducted, with a displacement reconstruction error of 22.3 nm. This method does not require any additional optical components to produce phase modulation, significantly reducing system complexity and cost. Furthermore, this article analyzes the algorithm’s applicability under different distances, current modulation coefficients, and modulation voltages, providing a reference for the application of current modulation methods in SMI displacement measurements.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10831-10838"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dual-Path CNN–BiLSTM for mmWave-Based Human Skeletal Pose Estimation","authors":"Yuqiang He;Jun Wang;Yaxin Li;Yuquan Luo","doi":"10.1109/JSEN.2025.3543343","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543343","url":null,"abstract":"In this article, we introduce a novel method for human skeletal joint localization using millimeter-wave (mmWave) radar, effectively overcoming the limitations of vision-based pose estimation methods, which are vulnerable to changes in lighting conditions and pose privacy concerns. The method leverages mmWave radar to generate 4-D time-series point cloud data, which is then projected onto the depth-azimuth and depth-elevation planes. This projection helps mitigate the sparsity inherent in traditional point cloud data and reduces the complexity of the machine learning model required for pose estimation. The input data structure is optimized using a sliding window technique, where consecutive frames are processed by a convolutional neural network (CNN) to extract spatial features. These features are then sorted chronologically and fed into a bi-directional long short-term memory (BiLSTM) to capture temporal features, resulting in the accurate localization of 25 skeletal joints. To validate the performance and effectiveness of the proposed method, we created a dataset comprising three body types and ten distinct actions. The experimental results demonstrate the method’s outstanding human pose estimation capability.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11683-11696"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting Accelerometer Baseline Correction and Nondivergent Deformation Velocity Based on Convolutional Neural Network (CNN) During GNSS Downgrade","authors":"Ce Jing;Elisa Bertolesi;Guanwen Huang;Xin Li;Qin Zhang;Weiwei Zhai;Guolin Liu;Hang Li","doi":"10.1109/JSEN.2025.3543726","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543726","url":null,"abstract":"Accelerometer and global navigation satellite system (GNSS) can be effectively combined to establish a robust multisensor deformation monitoring system. However, GNSS signals may get downgraded in challenging environments, and then destroy the Kalman filter data fusion model. As a result, the accelerometer becomes the only reliable sensor for deformation monitoring, but relying on only accelerometer data may lead to rapid error accumulation due to its potential baseline shift error. To mitigate this challenge, especially in the slow-moving deformation scenarios, we propose a baseline correction prediction algorithm named CNN-based baseline correction (CNN-BC), based on convolutional neural networks. This algorithm utilizes high-frequency acceleration and baseline correction as input and output features, respectively. The baseline correction of the training dataset is derived from the accelerometer and GNSS coupled algorithm. By incorporating the reliable prediction from the network, we can correct the original accelerometer data and reduce error accumulation. To further address the divergence in deformation velocity, we develop a convolutional neural network (CNN)-dVel, which uses high-frequency acceleration and velocity difference as input and output features, respectively. We validated the proposed algorithms through two slow deformation experiments utilizing both high-precision and low-cost accelerometers. The results demonstrate that the CNN-BC can predict reliable baseline correction, with an average root mean square (rms) of <inline-formula> <tex-math>$boldsymbol {textbf {0}.textbf {37}~textbf {cm}/textbf {s}^{textbf {2}}}$ </tex-math></inline-formula>, and the CNN-dVel achieves nondivergent deformation velocity prediction, with an average rms of 0.42 cm/s. Furthermore, optimizing the training dataset with an acceleration standard deviation (STD) basis enhances prediction accuracy.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11982-11994"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Electrochemical Impedance-Based Detection of Pancreatic Cancer Biomarker Glypican1 and Mucin1 Using Electric Field-Lysed Extracellular Vesicles for Analysis: A Proof of Concept","authors":"Nusrat Praween;Pammi Guru Krishna Thej;Palash Kumar Basu","doi":"10.1109/JSEN.2025.3542298","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542298","url":null,"abstract":"Glypican1 and mucin1 antigens are prominent biomarkers for the prognosis and diagnosis of pancreatic cancer. Their presence within the extracellular vesicles (EVs) opens the possibilities for oncology care through the development of minimally invasive biomarker-assisted screening tools. Traditionally, EV antigen quantification relies on ultracentrifugation (UC) and chemical lysis, which are time-consuming, equipment-dependent, and often compromise EV integrity, damaging surface intact biomarkers. This study integrates EV isolation and electric field (EF) lysis into a unified platform. The lysates were then analyzed using an electrochemical impedance spectroscopy (EIS)-based sensor to detect glypican-1 (GPC1) and mucin-1 (MUC1). ELISA confirms the EF lysis of the immobilized EV and shows an increase in the antigen concentration by 2.5 times (compared to the pre-lysed sample). Hence, EF lysis makes the sensor more sensitive than traditional methods. To enhance the electric lysis process, we applied varying voltages of a sinusoidal signal to the screen printed gold electrode (SPGE)-immobilized EVs. The lysate was subsequently used to quantify the GPC1 and MUC1 antigens through EIS. The results indicate that a 50-mV sinusoidal signal is sufficient to effectively lyse EVs, confirmed by western blotting. The nanoparticle tracking analyzer (NTA) results showed the successful isolation of <inline-formula> <tex-math>$10^{{9}}$ </tex-math></inline-formula> EVs from <inline-formula> <tex-math>$100~mu $ </tex-math></inline-formula>L of serum using CD63 antibody. The developed EIS sensor can detect GPC1 and MUC1 with an LOD of 0.053 and 0.033 pg/mL, respectively, from EV lysate, showing minimal nonspecific binding in the negative control. Beyond GPC1 and MUC1, the approach is adaptable for detecting other EV-associated biomarkers, enabling broader applications in early cancer detection and disease monitoring.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10566-10574"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Fully 3-D-Printed Piezoresistive Bionic Seal Whisker Integrating Multiple Liquid Metal Tunnels for Enhanced Sensitivity in Hydrodynamic Flow Sensing","authors":"Yanbo Xu;Zengxing Zhang;Weihong Ouyang;Jiangong Cui;Xingxu Zhang;Chenyang Xue","doi":"10.1109/JSEN.2025.3543614","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543614","url":null,"abstract":"Fish use their lateral line organs to sense wake changes in the flow field environment to conduct hunting, avoidance, and communication activities. Seals efficiently enhance their sensitivity to prey’s wake-induced vibrations (WIVs) by suppressing vortex-induced vibrations (VIVs) with undulating whiskers. In this study, we proposed a fully 3-D-printed piezoresistive bionic flow sensor. The bionic whisker simulates the geometrical dimensions of a real gray harbor whisker. The piezoresistive sensing unit made of liquid metal simulates the synaptic electromechanical transitions of hair follicle cells to acquire flow field information. Uniform flow field experiments and simulation results revealed that the sensor can effectively suppress VIVs at an angle of attack (AOA) of 0° and achieve a minimum flow velocity of 0.03 m/s at AOA of 90°. The sensing unit’s directional arrangement realizes the discrimination of the flow direction. In addition, the sensor demonstrated to determine the wake generated by the upstream cylinders and direct information about the upstream cylinders. Therefore, the sensors’ experimental results of the bionic whisker sensor can be applied to underwater robots to perceive diverse flow field information.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"10811-10819"},"PeriodicalIF":4.3,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact Force Reconstruction in Composite Structures Using Wavelet Transform and Low-Frequency Response Components","authors":"Minghua Wang;Bowen Zhao;Yi Zhang;Di Wu;Yue Wang;Xinlin Qing;Yishou Wang","doi":"10.1109/JSEN.2025.3543362","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3543362","url":null,"abstract":"Aircraft composite structures are vulnerable to barely visible impact damage (BVID) caused by external impacts. Timely identification of impact forces through sparse sensor networks is critical for structural maintenance and flight safety. This article proposes an impact force reconstruction method based on wavelet transform (WT) and low-frequency response components (LRCs). The impact forces at unknown locations can be identified using the limited training data. The method extracts LRCs via WT, ensuring stable system modeling by avoiding high-frequency disturbances. A similarity-based decision strategy adaptively selects sensor combinations and LRCs for interpolation, enabling effective impact force reconstruction through sparse networks. The approach is applicable to both reinforced and flat structural areas, offering a balanced solution between monitoring cost and reconstruction capability. Validation is provided through low-velocity impact experiments on composite stiffened panels.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11697-11709"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Connectivity for Non-Critical In-Vehicle Systems Using EnRF24L01","authors":"J. N. V. R. Swarup Kumar;Kuna Venkateswararao;Umashankar Ghugar;Sourav Kumar Bhoi;Kshira Sagar Sahoo","doi":"10.1109/JSEN.2025.3542869","DOIUrl":"https://doi.org/10.1109/JSEN.2025.3542869","url":null,"abstract":"There has been a significant paradigm shift from wired to wireless technology in in-vehicular networks. This shift is driven by the need for greater scalability, cost-effectiveness, and flexibility. In the automotive industry, traditional wired protocols such as the local interconnect network (LIN) and media-oriented systems transport (MOST) for non-critical systems (NCSs) add complexity to installation and maintenance, incur higher material costs, and offer limited scalability and mobility. NCSs, such as infotainment and weather forecast systems, do not require low latency and do not impair vehicle function when unavailable. This article presents an advanced methodology for enhancing connectivity in noncritical in-vehicular networks using Nordic Semiconductor’s (nRFs) enhanced-nRF24L01 (EnRF24L01) module. The EnRF24L01 module is the nRF24L01 module that incorporated the Sensor-Medium Access Control (S-MAC) algorithm for energy-efficient communication. The proposed method enables seamless communication between NCSs using a tree-based primary and secondary architecture, where the primary is the actuator and the secondary is the sensor node. To optimize energy efficiency using synchronized sleep/wake schedules, reduce power consumption, and enhance scalability, the S-MAC protocol was incorporated. Comprehensive experiments were conducted in simulated environments using optimized network engineering tool (OPNET) and Proteus Circuit Simulators, analyzing critical performance metrics: latency, jitter, throughput, packet delivery ratio (PDR), and energy efficiency. The results indicate that the proposed method supports a greater number of nodes with enhanced data transmission rates and operates at lower voltages, thereby extending the communication range and reducing overall power consumption. Additionally, hardware simulation results demonstrate the successful integration of EnRF24L01 modules with Arduino for wireless data transmission, showing significant improvements in scalability, energy efficiency, and adaptability, as well as architectural and operational costs and maintenance efficiency.","PeriodicalId":447,"journal":{"name":"IEEE Sensors Journal","volume":"25 7","pages":"11955-11962"},"PeriodicalIF":4.3,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}