IEEE Sensors Letters最新文献_第7页

Generalizing Perceived Fatigue Estimation Across Diverse Upper Limb Tasks Using Minimal Wearable Sensors 基于最小可穿戴传感器的不同上肢任务感知疲劳估计

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-07 DOI: 10.1109/LSENS.2025.3596719

Malik Muhammad Qirtas;Marco Sica;Merve Nur Yasar;Patricia O'Sullivan;Brendan O'Flynn;Salvatore Tedesco;Matteo Menolotto;Andrea Visentin

引用次数: 0

Photodetection and Imaging Applications of BA2MA4Pb5I16 Ruddlesden–Popper Perovskite BA2MA4Pb5I16 rudlesden - popper钙钛矿的光探测与成像应用

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-07 DOI: 10.1109/LSENS.2025.3596587

Min Zhang;Yuancheng Wang;Xiao Wang;Dingyu Yang

引用次数: 0

Multimodal Wearable-Based Automated Driver Inattention State Assessment Using Multidevices and Novel Cross-Modal Attention Framework 基于多设备和新型跨模态注意框架的多模态可穿戴自动驾驶驾驶员注意力不集中状态评估

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-07 DOI: 10.1109/LSENS.2025.3596610

Kaveti Pavan;Ankit Singh;Digvijay S. Pawar;Nagarajan Ganapathy

{"title":"Multimodal Wearable-Based Automated Driver Inattention State Assessment Using Multidevices and Novel Cross-Modal Attention Framework","authors":"Kaveti Pavan;Ankit Singh;Digvijay S. Pawar;Nagarajan Ganapathy","doi":"10.1109/LSENS.2025.3596610","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3596610","url":null,"abstract":"Driver inattention detection remains a critical challenge in driver's well-being, requiring robust systems that can distinguish stress-induced mental load during naturalistic driving. Current approaches face limitations in multiple wearable-based data fusion and real-time biosignals assessment. This letter proposes a novel cross-squeeze-and-excitation convolution neural network (CNN) framework to process simultaneously acquired multiple wearable from 15 participants in controlled driving scenarios. The multimodal signals are applied to multistage attention mechanisms [electrocardiogram (ECG)<inline-formula><tex-math>$leftrightarrow$</tex-math></inline-formula>electrodermal activity (EDA), ECG <inline-formula><tex-math>$rightarrow$</tex-math></inline-formula> EDA, EDA <inline-formula><tex-math>$rightarrow$</tex-math></inline-formula> ECG] with 1D-CNN blocks, optimized for 10-s signal segments. The proposed approach is able to classify drive inattention state. It is observed that ECG <inline-formula><tex-math>$rightarrow$</tex-math></inline-formula> EDA attention achieves 76.54% average accuracy using leave-one-subject-out cross validation, outperforming unimodal approaches by 12.4% and bidirectional attention by 4.8%. Feature visualizations confirm enhanced pattern discrimination in inattention conditions. This letter advances driver health monitoring systems through effective wearable integration and adaptive feature weighting, with potential for edge deployment and clinical stress assessment applications","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Wideband Near-Field Microwave Imaging With Optimal Standoff Using a Metasurface-Enhanced Printed Dipole Antenna 基于超表面增强印刷偶极子天线的最佳距离宽带近场微波成像

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596310

Soumya Chakravarty;Anwesha Khasnobish;M. Jaleel Akhtar

引用次数: 0

Bi4Ti3O12-Based Sol-Gel Composite Ultrasonic Transducer for High-Temperature Measurements 高温测量用bi4ti3o12溶胶-凝胶复合超声换能器

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596330

Mako Nakamura;Makiko Kobayashi

{"title":"Bi4Ti3O12-Based Sol-Gel Composite Ultrasonic Transducer for High-Temperature Measurements","authors":"Mako Nakamura;Makiko Kobayashi","doi":"10.1109/LSENS.2025.3596330","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3596330","url":null,"abstract":"In this letter, Bi4Ti3O12 (BiT)/TiO2 + SrCO3 (TO+Sr) sol-gel composites were characterized to develop a lead-free, highly sensitive, low-temperature sintering ultrasonic transducer material for nondestructive testing in high-temperature environments. Samples of BiT/TO+Sr and, as a comparison, a 50-μm-thick film of lead-containing material BiT/ Pb(Zr,Ti)O3 (PZT) were fired at 200°C, 400°C, and 650°C, respectively, and their ultrasonic response properties were evaluated from room temperature to 500°C. In the room-temperature performance test, BiT/TO+Sr with specified firing temperature showed higher <italic>d33 and sensitivity than BiT/PZT with the same firing temperature. In the high-temperature cycling test, every sample exhibited a stable ultrasonic response up to 500°C. The sensitivity of BiT/TO+Sr is equivalent to that of BiT/PZT. These results indicate that BiT/TO+Sr sol-gel composites, which can be sintered at low temperatures and have high sensitivity, are promising high-temperature piezoelectric materials that can replace the conventional lead-containing material, such as BiT/PZT.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144896800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Development of a Temperature and Pressure Transmitter Using a Laser-Induced Graphene Sensor 使用激光诱导石墨烯传感器的温度和压力变送器的设计与开发

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596529

Alan Cuenca Sánchez;Fernando Pantoja-Suárez;Melvin Chilig;Johan Mena

{"title":"Design and Development of a Temperature and Pressure Transmitter Using a Laser-Induced Graphene Sensor","authors":"Alan Cuenca Sánchez;Fernando Pantoja-Suárez;Melvin Chilig;Johan Mena","doi":"10.1109/LSENS.2025.3596529","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3596529","url":null,"abstract":"The design and development of a temperature and pressure transmitter using a laser-induced graphene (LIG) sensor represents a significant advance in precision measurement technology. LIG, characterized by its unique porous structure and tailored flaw engineering, exhibits exceptional electrical and thermal conductivity, high mechanical strength, and flexibility, making it ideal for highly sensitive sensing applications. In this study, a sensor was directly patterned with LIG on a flexible substrate to enable real-time monitoring of temperature and pressure changes. Temperature is measured through LIG's intrinsic resistance variation, while pressure is sensed via its enhanced piezoresistive properties arising from its engineered porosity and defect structure. A signal conditioning and processing circuit was implemented for calibration and data visualization, featuring a modular design that supports long-distance data transmission via a standard 4–20 mA current loop. Moreover, the LIG fabrication process is inherently simple, cost-effective, and environmentally friendly. The single-step laser-induced patterning method eliminates the need for high temperatures, chemical solvents, and complex processing, thereby reducing energy consumption, production costs, and environmental impact. This approach positions LIG-based sensors as a promising low-cost and sustainable alternative to traditional sensor technologies. Experimental results demonstrate high accuracy, fast response times, and low power consumption, underscoring the potential of LIG technology in next-generation industrial and biomedical sensing applications.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11118290","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reinforcement Learning-Based Autonomous UAV Navigation for CO Source Localization 基于强化学习的无人机自主导航CO源定位

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596388

Pritika Marik;Harshit Kumar Sahu;Chiranjib Ghosh;Amit Ruidas;Soumajit Pramanik;Avishek Adhikary

引用次数: 0

A Quantitative Sputtering Method for Accurate Composition Calibration of 2.5 µm Thin Au–Sn Metallization Enabling WLP SLID Bonding 2.5µm薄金锡金属化WLP滑动键合的定量溅射精确成分校准方法

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596510

Jianjun Ma;Qi Wei;Bin Zhou;Rong Zhang

{"title":"A Quantitative Sputtering Method for Accurate Composition Calibration of 2.5 µm Thin Au–Sn Metallization Enabling WLP SLID Bonding","authors":"Jianjun Ma;Qi Wei;Bin Zhou;Rong Zhang","doi":"10.1109/LSENS.2025.3596510","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3596510","url":null,"abstract":"This letter presents a quantitative approach for 2.5 <italic>µm Au–Sn metallization with accurate mass calibration, which is applied to solid–liquid interdiffusion bonding of microelectromechanical systems (MEMS) wafer-level packaging (WLP) devices, particularly those that demand a micron-level gap of out-of-plane electrodes. For Au–Sn metallization, traditional deposition methods, such as electroplating, are unable to achieve micron-level thickness and accurate composition due to poor robustness to varying process conditions. In this study, we further improve the composition of the deposited Au–Sn alloy through direct mass measurement and calibration during the cosputtering process. In the experiment, a high-precision balance with a resolution of 0.1 mg was utilized to measure the mass increment of sputtered Au and Sn in the clean room. Subsequently, the sputtering rate of the Sn target was calculated and applied to calibrate the final Au–Sn composition. According to the energy-dispersive X-ray spectrum (EDS) results, the difference between the measured Au–Sn mass composition and the set value is 1.1%, which is significantly lower than the typical 5%–10% composition deviation of electroplated Au–Sn solder, demonstrating a strengthened robustness to varying conditions by the effective calibration. The test results show that the shear strength of the WLP structure reaches 31.8 MPa, and the cross-sectional EDS results of the as-bonded Au–Sn alloy are consistent with the designed Au–Sn composition. The proposed calibration method can also be applied to other alloy depositions that require precise mass composition and micron-level thickness with a better robustness to varying conditions.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144887801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LtXe-EnsNet: A Lightweight and Explainable Ensembled Deep Learning Model for Heart Sound Abnormality Classification From Sensor Data ltxe - ennet：基于传感器数据的心音异常分类的轻量级可解释集成深度学习模型

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-06 DOI: 10.1109/LSENS.2025.3596256

MD Toufiqur Rahman;Celia Shahnaz

{"title":"LtXe-EnsNet: A Lightweight and Explainable Ensembled Deep Learning Model for Heart Sound Abnormality Classification From Sensor Data","authors":"MD Toufiqur Rahman;Celia Shahnaz","doi":"10.1109/LSENS.2025.3596256","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3596256","url":null,"abstract":"Cardiovascular diseases (CVDs), characterized by abnormalities in the heart, must be detected with high precision and in real-time. Phonocardiogram (PCG) signals are utilized for the detection of cardiac irregularities, thereby providing a crucial indicator for heart state monitoring in a noninvasive manner. The proposed research focuses on accurately identifying cardiovascular health by classifying the heart sounds in real-time sensor data. In this letter, an explainable and deep learning-based lightweight multifeature ensemble approach is proposed for the automated identification of CVDs from PCG signals collected using a digital stethoscope. Our method leverages the combined strengths of spectrogram and mel-frequency cepstral coefficient (MFCC) features to perform a multiclass classification task, with Grad-CAM providing visual explanations for model decisions. The proposed approach integrates both the spectrogram and MFCC features as inputs, channeling them through dedicated deep neural network-based feature extraction modules. The attention-based “MFCC-Module’’ extracts significant features from MFCC, while the spectro-module captures essential information from the spectrogram. By fusing these two feature sets, the architecture effectively classifies the signals. Our proposed robust lightweight model surpasses all other models, achieving an impressive accuracy of 99.5% for five-class classifications of PCG signal data from the sensor.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144867583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An Intelligent Camera-Based Contactless Driver Stress State Monitoring Using Multimodality Fusion 基于多模态融合的智能摄像头非接触式驾驶员应力状态监测

IF 2.2

IEEE Sensors Letters Pub Date : 2025-08-05 DOI: 10.1109/LSENS.2025.3595917

Swarubini P J;Thomas M. Deserno;Nagarajan Ganapathy

{"title":"An Intelligent Camera-Based Contactless Driver Stress State Monitoring Using Multimodality Fusion","authors":"Swarubini P J;Thomas M. Deserno;Nagarajan Ganapathy","doi":"10.1109/LSENS.2025.3595917","DOIUrl":"https://doi.org/10.1109/LSENS.2025.3595917","url":null,"abstract":"Driver stress involves complex psychological, physiological, and behavioral responses to stressors across different mobility spaces, which leads to road accidents. Recently, noncontact sensing-derived biosignals have been explored in mental health assessment. However, camera-based biosignals in mobility environments is still challenging. In this study, we aim to classify the driver stress using imaging photoplethysmography (iPPG) signals, facial keypoints, and fusion-based convolutional neural network (CNN). For this, we acquired infrared facial videos from healthy subjects (<italic>N=20) in simulated driving. iPPG signals and facial keypoints were extracted using the local group invariance method and CNN, respectively. The iPPG signals were processed with a 1-D CNN, and facial keypoints with a 2-D CNN for feature learning. The proposed approach is able to classify between the drivers' stress states. Experimental results show that the proposed fusion approach achieved an mean classification accuracy (ACC) and F1-score of 87.00% and 86.33%, respectively. The iPPG signals demonstrated a better mean ACC (90.00%) and F1-score (90.33%) among the individual models. Thus, the framework could be extended for driver stress detection in real-time scenarios enabling early stress detection.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 9","pages":"1-4"},"PeriodicalIF":2.2,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144892373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0