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Volume 25
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Sensors, Volume 25, Issue 13 (July-1 2025) – 405 articles

Cover Story (view full-size image): In this work, we introduce a soft pneumatic actuator designed with two independent bending chambers to enable customized finger joint movement, particularly for rehabilitation purposes. A real-time camera-based system estimates bending angles using a deep learning model, offering a sensor-free solution that simplifies wearable design. By eliminating the need for traditional hardware-based sensors and reducing wiring complexity, this approach enhances comfort and usability. The system demonstrates high precision in detecting finger joint angles and supports natural movement, making it promising for rehabilitation and human–robot interaction. View this paper
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20 pages, 2572 KiB  
Article
A Study on Distributed Multi-Sensor Fusion for Nonlinear Systems Under Non-Overlapping Fields of View
by Liu Wang, Yang Zhou, Wenjia Li, Lijuan Shi, Jian Zhao and Haiyan Wang
Sensors 2025, 25(13), 4241; https://doi.org/10.3390/s25134241 - 7 Jul 2025
Viewed by 532
Abstract
To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial [...] Read more.
To explore how varying viewpoints influence the accuracy of distributed fusion in asynchronous, nonlinear visual-field systems, this study investigates fusion strategies for multi-target tracking. The primary focus is on how different sensor perspectives affect the fusion of nonlinear moving-target data and the spatial segmentation of such targets. We propose a differential-view nonlinear multi-target tracking approach that integrates the Gaussian mixture, jump Markov nonlinear system, and the cardinalized probability hypothesis density (GM-JMNS-CPHD). The method begins by partitioning the observation space based on the boundaries of distinct viewpoints. Next, it applies a combined technique—the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and SOS (stochastic outlier selection)—to identify outliers near these boundaries. To achieve accurate detection, the posterior intensity is split into several sub-intensities, followed by reconstructing the multi-Bernoulli cardinality distribution to model the target population in each subregion. The algorithm’s computational complexity remains on par with the standard GM-JMNS-CPHD filter. Simulation results confirm the proposed method’s robustness and accuracy, demonstrating a lower error rate compared to other benchmark algorithms. Full article
(This article belongs to the Section Sensing and Imaging)
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15 pages, 5527 KiB  
Article
Screen Printing Conductive Inks on Textiles: Impact of Plasma Treatment
by Julia Guérineau, Jollan Ton and Mariia Zhuldybina
Sensors 2025, 25(13), 4240; https://doi.org/10.3390/s25134240 - 7 Jul 2025
Viewed by 525
Abstract
Textile-based wearable devices are rapidly gaining traction in the Internet of Things paradigm and offer distinct advantages for data collection and analysis across a wide variety of applications. Seamlessly integrating electronics in textiles remains a technical challenge, especially when the textiles’ essential properties, [...] Read more.
Textile-based wearable devices are rapidly gaining traction in the Internet of Things paradigm and offer distinct advantages for data collection and analysis across a wide variety of applications. Seamlessly integrating electronics in textiles remains a technical challenge, especially when the textiles’ essential properties, such as comfort, breathability, and flexibility, are meant to be preserved. This article investigates screen printing as a textile post-processing technique for electronic integration, and highlights its versatility, cost-effectiveness, and adaptability in terms of design and customization. The study examines two silver-based inks screen-printed on an Oxford polyester textile substrate with a focus on substrate preparation and treatment. Before printing, the textile samples were cleaned with nitrogen gas and then subjected to low-pressure oxygen plasma treatment. For comparative analysis, two samples printed on polyethylene terephthalate (PET) serve as a reference. The findings highlight the importance of plasma treatment in optimizing the printability of textiles and demonstrate that it notably improves the electrical properties of conductive inks. Despite some remaining challenges, the study indicates that screen-printed electronics show promising potential for advancing the development of e-textiles and sensor-integrated wearables. Full article
(This article belongs to the Section Sensors Development)
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15 pages, 604 KiB  
Article
Converging Minds: EEG Synchrony During Communication About Moral Decision-Making in Dyadic Interactions
by Roberta A. Allegretta, Katia Rovelli and Michela Balconi
Sensors 2025, 25(13), 4239; https://doi.org/10.3390/s25134239 - 7 Jul 2025
Viewed by 492
Abstract
Communication about moral decision-making involves complex emotional and cognitive processes, especially in critical situations. This study adopted a hyperscanning paradigm to explore neural convergence during moral negotiation. Twenty-six healthy young adults (mean age = 23.59 years; 16 women, 10 men), with no neurological [...] Read more.
Communication about moral decision-making involves complex emotional and cognitive processes, especially in critical situations. This study adopted a hyperscanning paradigm to explore neural convergence during moral negotiation. Twenty-six healthy young adults (mean age = 23.59 years; 16 women, 10 men), with no neurological or psychiatric conditions, were paired into 13 same-gender dyads at the Università Cattolica del Sacro Cuore. Each dyad discussed a medical moral dilemma while their electrophysiological (EEG) activity was simultaneously recorded. Participants were first categorized according to their Dominant Reasoning Profile (DRP) (cognitive or affective), and subsequently convergence in DRP within the dyads was established. EEG band dissimilarities within each dyad were analyzed across frontal, temporo-central, and parieto-occipital regions. The results revealed significantly greater dissimilarity in frontal delta-band activity compared to parieto-occipital areas, regardless of the dyad’s DRP. Such results might suggest different emotional and motivational reactions between the two individuals, reflecting a broader gap in how the moral decision-making process was interpreted and internalized by each member, despite their DRP. The EEG hyperscanning paradigm proves useful in the study and understanding of the neural mechanisms involved in social interaction about morally sensitive decisions and provides novel insights into dyadic brain dynamics. Full article
(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)
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21 pages, 518 KiB  
Article
Bilevel Optimization for ISAC Systems with Proactive Eavesdropping Capabilities
by Tingyue Xue, Wenhao Lu, Mianyi Zhang, Yinghui He, Yunlong Cai and Guanding Yu
Sensors 2025, 25(13), 4238; https://doi.org/10.3390/s25134238 - 7 Jul 2025
Viewed by 304
Abstract
Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of [...] Read more.
Integrated sensing and communication (ISAC) has attracted extensive attention as a key technology to improve spectrum utilization and system performance for future wireless sensor networks. At the same time, active surveillance, as a legitimate means of surveillance, can improve the success rate of surveillance by sending interference signals to suspicious receivers, which is important for crime prevention and public safety. In this paper, we investigate the joint optimization of performance of both ISAC and active surveillance. Specifically, we formulate a bilevel optimization problem where the upper-level objective aims to maximize the probability of successful eavesdropping while the lower-level objective aims to optimize the localization performance of the radar on suspicious transmitters. By employing the Rayleigh quotient, introducing a decoupling strategy, and adding penalty terms, we propose an algorithm to solve the bilevel problem where the lower-level objective is convex. With the help of the proposed algorithm, we obtain the optimal solution of the analog transmit beamforming matrix and the digital beamforming vector. Performance analysis and discussion of key insights, such as the trade-off between eavesdropping success probability and radar localization accuracy, are also provided. Finally, comprehensive simulation results validate the effectiveness of our proposed algorithm in enhancing both the eavesdropping success probability and the accuracy of radar localization. Full article
(This article belongs to the Section Communications)
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22 pages, 4828 KiB  
Article
High-Fidelity Interactive Motorcycle Driving Simulator with Motion Platform Equipped with Tension Sensors
by Josef Svoboda, Přemysl Toman, Petr Bouchner, Stanislav Novotný and Vojtěch Thums
Sensors 2025, 25(13), 4237; https://doi.org/10.3390/s25134237 - 7 Jul 2025
Viewed by 562
Abstract
The paper presents the innovative approach to a high-fidelity motorcycle riding simulator based on VR (Virtual Reality)-visualization, equipped with a Gough-Stewart 6-DOF (Degrees of Freedom) motion platform. Such a solution integrates a real-time tension sensor system as a source for highly realistic motion [...] Read more.
The paper presents the innovative approach to a high-fidelity motorcycle riding simulator based on VR (Virtual Reality)-visualization, equipped with a Gough-Stewart 6-DOF (Degrees of Freedom) motion platform. Such a solution integrates a real-time tension sensor system as a source for highly realistic motion cueing control as well as the servomotor integrated into the steering system. Tension forces are measured at four points on the mock-up chassis, allowing a comprehensive analysis of rider interaction during various maneuvers. The simulator is developed to simulate realistic riding scenarios with immersive motion and visual feedback, enhanced with the simulation of external influences—headwind. This paper presents results of a validation study—pilot experiments conducted to evaluate selected riding scenarios and validate the innovative simulator setup, focusing on force distribution and system responsiveness to support further research in motorcycle HMI (Human–Machine Interaction), rider behavior, and training. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Traffic, Driving and Transportation)
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27 pages, 5697 KiB  
Review
Optical Non-Invasive Glucose Monitoring Using Aqueous Humor: A Review
by Haolan Xi and Yiqing Gong
Sensors 2025, 25(13), 4236; https://doi.org/10.3390/s25134236 - 7 Jul 2025
Viewed by 1056
Abstract
This review explores optical technologies for non-invasive glucose monitoring (NIGM) using aqueous humor (AH) as media, addressing the limitations of traditional invasive methods in diabetes management. It analyzes key techniques such as Raman spectroscopy, polarimetry, and mid- and near-infrared spectral methods, highlighting their [...] Read more.
This review explores optical technologies for non-invasive glucose monitoring (NIGM) using aqueous humor (AH) as media, addressing the limitations of traditional invasive methods in diabetes management. It analyzes key techniques such as Raman spectroscopy, polarimetry, and mid- and near-infrared spectral methods, highlighting their respective challenges, alongside emerging hybrid approaches like photoacoustic spectroscopy and optical coherence tomography. Crucially, the practical realization of these optical methods for portable NIGM hinges on advanced instrumentation. Therefore, this review also details progress in compact NIR spectrometers. While conventional systems often lack suitability, significant advancements in on-chip technologies—including miniaturized dispersive spectrometers and various on-chip Fourier transform systems (e.g., spatial heterodyne, stationary wave integral, and temporally modulated FT systems)—utilizing integration platforms like SOI and SiN are promising. Such innovations offer the potential for high spectral resolution, large bandwidth, and miniaturization, which are essential for developing practical AH-based NIGM systems to improve diabetes care. Full article
(This article belongs to the Special Issue Advances in Miniaturization and Power Efficiency of Optical Sensors)
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41 pages, 7199 KiB  
Article
Entropy, Irreversibility, and Time-Series Deep Learning of Kinematic and Kinetic Data for Gait Classification in Children with Cerebral Palsy, Idiopathic Toe Walking, and Hereditary Spastic Paraplegia
by Alfonso de Gorostegui, Massimiliano Zanin, Juan-Andrés Martín-Gonzalo, Javier López-López, David Gómez-Andrés, Damien Kiernan and Estrella Rausell
Sensors 2025, 25(13), 4235; https://doi.org/10.3390/s25134235 - 7 Jul 2025
Viewed by 422
Abstract
The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some [...] Read more.
The use of gait analysis to differentiate among paediatric populations with neurological and developmental conditions such as idiopathic toe walking (ITW), cerebral palsy (CP), and hereditary spastic paraplegia (HSP) remains challenging due to the insufficient precision of current diagnostic approaches, leading in some cases to misdiagnosis. Existing methods often isolate the analysis of gait variables, overlooking the whole complexity of biomechanical patterns and variations in motor control strategies. While previous studies have explored the use of statistical physics principles for the analysis of impaired gait patterns, gaps remain in integrating both kinematic and kinetic information or benchmarking these approaches against Deep Learning models. This study evaluates the robustness of statistical physics metrics in differentiating between normal and abnormal gait patterns and quantifies how the data source affects model performance. The analysis was conducted using gait data sets from two research institutions in Madrid and Dublin, with a total of 81 children with ITW, 300 with CP, 20 with HSP, and 127 typically developing children as controls. From each kinematic and kinetic time series, Shannon’s entropy, permutation entropy, weighted permutation entropy, and time irreversibility metrics were derived and used with Random Forest models. The classification accuracy of these features was compared to a ResNet Deep Learning model. Further analyses explored the effects of inter-laboratory comparisons and the spatiotemporal resolution of time series on classification performance and evaluated the impact of age and walking speed with linear mixed models. The results revealed that statistical physics metrics were able to differentiate among impaired gait patterns, achieving classification scores comparable to ResNet. The effects of walking speed and age on gait predictability and temporal organisation were observed as disease-specific patterns. However, performance differences across laboratories limit the generalisation of the trained models. These findings highlight the value of statistical physics metrics in the classification of children with different toe walking conditions and point towards the need of multimetric integration to improve diagnostic accuracy and gain a more comprehensive understanding of gait disorders. Full article
(This article belongs to the Special Issue Sensor Technologies for Gait Analysis: 2nd Edition)
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17 pages, 7292 KiB  
Article
QP-Adaptive Dual-Path Residual Integrated Frequency Transformer for Data-Driven In-Loop Filter in VVC
by Cheng-Hsuan Yeh, Chi-Ting Ni, Kuan-Yu Huang, Zheng-Wei Wu, Cheng-Pin Peng and Pei-Yin Chen
Sensors 2025, 25(13), 4234; https://doi.org/10.3390/s25134234 - 7 Jul 2025
Viewed by 428
Abstract
As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these [...] Read more.
As AI-enabled embedded systems such as smart TVs and edge devices demand efficient video processing, Versatile Video Coding (VVC/H.266) becomes essential for bandwidth-constrained Multimedia Internet of Things (M-IoT) applications. However, its block-based coding often introduces compression artifacts. While CNN-based methods effectively reduce these artifacts, maintaining robust performance across varying quantization parameters (QPs) remains challenging. Recent QP-adaptive designs like QA-Filter show promise but are still limited. This paper proposes DRIFT, a QP-adaptive in-loop filtering network for VVC. DRIFT combines a lightweight frequency fusion CNN (LFFCNN) for local enhancement and a Swin Transformer-based global skip connection for capturing long-range dependencies. LFFCNN leverages octave convolution and introduces a novel residual block (FFRB) that integrates multiscale extraction, QP adaptivity, frequency fusion, and spatial-channel attention. A QP estimator (QPE) is further introduced to mitigate double enhancement in inter-coded frames. Experimental results demonstrate that DRIFT achieves BD rate reductions of 6.56% (intra) and 4.83% (inter), with an up to 10.90% gain on the BasketballDrill sequence. Additionally, LFFCNN reduces the model size by 32% while slightly improving the coding performance over QA-Filter. Full article
(This article belongs to the Special Issue Multimodal Sensing Technologies for IoT and AI-Enabled Systems)
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21 pages, 3406 KiB  
Article
ResNet-SE-CBAM Siamese Networks for Few-Shot and Imbalanced PCB Defect Classification
by Chao-Hsiang Hsiao, Huan-Che Su, Yin-Tien Wang, Min-Jie Hsu and Chen-Chien Hsu
Sensors 2025, 25(13), 4233; https://doi.org/10.3390/s25134233 - 7 Jul 2025
Viewed by 646
Abstract
Defect detection in mass production lines often involves small and imbalanced datasets, necessitating the use of few-shot learning methods. Traditional deep learning-based approaches typically rely on large datasets, limiting their applicability in real-world scenarios. This study explores few-shot learning models for detecting product [...] Read more.
Defect detection in mass production lines often involves small and imbalanced datasets, necessitating the use of few-shot learning methods. Traditional deep learning-based approaches typically rely on large datasets, limiting their applicability in real-world scenarios. This study explores few-shot learning models for detecting product defects using limited data, enhancing model generalization and stability. Unlike previous deep learning models that require extensive datasets, our approach effectively performs defect detection with minimal data. We propose a Siamese network that integrates Residual blocks, Squeeze and Excitation blocks, and Convolution Block Attention Modules (ResNet-SE-CBAM Siamese network) for feature extraction, optimized through triplet loss for embedding learning. The ResNet-SE-CBAM Siamese network incorporates two primary features: attention mechanisms and metric learning. The recently developed attention mechanisms enhance the convolutional neural network operations and significantly improve feature extraction performance. Meanwhile, metric learning allows for the addition or removal of feature classes without the need to retrain the model, improving its applicability in industrial production lines with limited defect samples. To further improve training efficiency with imbalanced datasets, we introduce a sample selection method based on the Structural Similarity Index Measure (SSIM). Additionally, a high defect rate training strategy is utilized to reduce the False Negative Rate (FNR) and ensure no missed defect detections. At the classification stage, a K-Nearest Neighbor (KNN) classifier is employed to mitigate overfitting risks and enhance stability in few-shot conditions. The experimental results demonstrate that with a good-to-defect ratio of 20:40, the proposed system achieves a classification accuracy of 94% and an FNR of 2%. Furthermore, when the number of defective samples increases to 80, the system achieves zero false negatives (FNR = 0%). The proposed metric learning approach outperforms traditional deep learning models, such as parametric-based YOLO series models in defect detection, achieving higher accuracy and lower miss rates, highlighting its potential for high-reliability industrial deployment. Full article
(This article belongs to the Special Issue Artificial Intelligence and Sensor-Enhanced Fault Diagnosis for Industrial Application)
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17 pages, 1374 KiB  
Article
Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors
by Jiri Konecny, Jaromir Konecny, Kamil Bancik, Miroslav Mikus, Jan Choutka, Jiri Koziorek, Ibrahim A. Hameed, Algimantas Valinevicius, Darius Andriukaitis and Michal Prauzek
Sensors 2025, 25(13), 4232; https://doi.org/10.3390/s25134232 - 7 Jul 2025
Viewed by 394
Abstract
Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising [...] Read more.
Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors. Full article
(This article belongs to the Special Issue Advances in Energy Harvesting and Sensor Systems)
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18 pages, 1123 KiB  
Article
Corrosion Risk Assessment in Coastal Environments Using Machine Learning-Based Predictive Models
by Marta Terrados-Cristos, Marina Diaz-Piloneta, Francisco Ortega-Fernández, Gemma Marta Martinez-Huerta and José Valeriano Alvarez-Cabal
Sensors 2025, 25(13), 4231; https://doi.org/10.3390/s25134231 - 7 Jul 2025
Viewed by 574
Abstract
Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there [...] Read more.
Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure. Full article
(This article belongs to the Special Issue Advanced Sensor Technologies for Corrosion Monitoring)
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17 pages, 3035 KiB  
Article
Data-Driven Image-Based Protocol for Brain PET Image Harmonization
by Eva Štokelj, Urban Simončič and for the Alzheimer’s Disease Neuroimaging Initiative
Sensors 2025, 25(13), 4230; https://doi.org/10.3390/s25134230 - 7 Jul 2025
Viewed by 448
Abstract
Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain [...] Read more.
Quantitative FDG-PET brain imaging across multiple centers is challenged by inter-scanner variability, impacting the comparability of neuroimaging data. This study proposes a data-driven image-based harmonization protocol to address these discrepancies without relying on traditional phantom scans. The protocol uses spatially normalized FDG-PET brain images to estimate scanner-specific Gaussian smoothing filters, optimizing parameters via the structural similarity index (SSIM). Validation was performed using images from cognitively normal individuals and Alzheimer’s disease patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. Results demonstrated robust harmonization at moderate target resolutions (8 and 10 mm FWHM), with filter estimates consistently within 1.2 mm of phantom-derived ground truths. However, at higher resolutions (6 mm FWHM), discrepancies reached up to 3 mm, reflecting reduced accuracy. These deviations were particularly evident for high-resolution scanners like HRRT, likely due to elevated noise levels and smaller sample sizes. The presented harmonization method effectively reduces inter-scanner variability in retrospective FDG-PET studies, especially valuable when phantom scans are unavailable. Nonetheless, the current limitations at finer resolutions underline the necessity for methodological refinements to meet the demands of evolving high-resolution PET imaging technologies. Full article
(This article belongs to the Special Issue Energy-Efficient and Privacy-Preserving Biomedical Signal and Image Processing)
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14 pages, 6318 KiB  
Article
Multiplexing and Demultiplexing of Aperture-Modulated OAM Beams
by Wanjun Wang, Liguo Wang, Lei Gong, Zhiqiang Yang, Ligong Yang, Yao Li and Zhensen Wu
Sensors 2025, 25(13), 4229; https://doi.org/10.3390/s25134229 - 7 Jul 2025
Viewed by 416
Abstract
A multiplexing method for orbital angular momentum (OAM) beams was proposed. The aperture size as a new information carrier was provided, and it could be modulated by the external variable aperture. The field of the beams propagating through turbulence was derived and discretized [...] Read more.
A multiplexing method for orbital angular momentum (OAM) beams was proposed. The aperture size as a new information carrier was provided, and it could be modulated by the external variable aperture. The field of the beams propagating through turbulence was derived and discretized with Gauss–Legendre quadrature formulas. Based on this, the demultiplexing method was improved, and the beam OAM states, amplitude, Gaussian spot radius and aperture radius were decoded. Moreover, the influence of turbulence on the multiplexing parameters was also analyzed, and the decoding precision of the aperture radius was higher than that of other parameters. The aperture radius was recommended as an extra carrier for multiplexing communication. This study provides a simple method to modulate the information carried by OAM beams, and it has promising applications in large capacity laser communication. Full article
(This article belongs to the Special Issue Cutting-Edge Developments in Optical Communications, Perception and Computing)
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17 pages, 1765 KiB  
Article
Automated Sidewalk Surface Detection Using Wearable Accelerometry and Deep Learning
by Do-Eun Park, Jong-Hoon Youn and Teuk-Seob Song
Sensors 2025, 25(13), 4228; https://doi.org/10.3390/s25134228 - 7 Jul 2025
Viewed by 459
Abstract
Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Typically, pedestrian interviews and surveys are used to evaluate walkability. However, these methods can be costly to implement at scale, as they [...] Read more.
Walking-friendly cities not only promote health and environmental benefits but also play crucial roles in urban development and local economic revitalization. Typically, pedestrian interviews and surveys are used to evaluate walkability. However, these methods can be costly to implement at scale, as they demand considerable time and resources. To address the limitations in current methods for evaluating pedestrian pathways, we propose a novel approach utilizing wearable sensors and deep learning. This new method provides benefits in terms of efficiency and cost-effectiveness while ensuring a more objective and consistent evaluation of sidewalk surfaces. In the proposed method, we used wearable accelerometers to capture participants’ acceleration along the vertical (V), anterior-posterior (AP), and medio-lateral (ML) axes. This data is then transformed into the frequency domain using Fast Fourier Transform (FFT), a Kalman filter, a low-pass filter, and a moving average filter. A deep learning model is subsequently utilized to classify the conditions of the sidewalk surfaces using this transformed data. The experimental results indicate that the proposed model achieves a notable accuracy rate of 95.17%. Full article
(This article belongs to the Special Issue Sensors for Unsupervised Mobility Assessment and Rehabilitation)
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19 pages, 4784 KiB  
Article
Accurate and Fast Numerical Estimation of Pattern Uncertainty for Mechanical Alignment Errors in High-Accuracy Spherical Near-Field Antenna Measurements
by Kyriakos Kaslis, Samel Arslanagic and Olav Breinbjerg
Sensors 2025, 25(13), 4227; https://doi.org/10.3390/s25134227 - 7 Jul 2025
Viewed by 306
Abstract
Every experimental measurement is affected by random and/or systematic error sources, causing the measurand to have an associated uncertainty quantified in terms of a confidence interval and confidence level. For high-accuracy spherical near-field antenna measurements, there are approximately 20 error sources whose individual [...] Read more.
Every experimental measurement is affected by random and/or systematic error sources, causing the measurand to have an associated uncertainty quantified in terms of a confidence interval and confidence level. For high-accuracy spherical near-field antenna measurements, there are approximately 20 error sources whose individual contributions to the measurand uncertainty must be estimated for each antenna under test; thus, this uncertainty estimation is a required task in each measurement project. The error sources associated with the mechanical alignment of the antenna under test are of particular importance, not only because the consequential pattern uncertainty differs significantly for different antennas under test, but also because the common practice of experimental uncertainty estimation is very time-consuming with separate uncertainty measurements, thus requiring the antenna under test as well as the measurement facility. We propose a numerical pattern uncertainty estimation for mechanical alignment errors based on a nominal full-sphere measurement without the need for separate uncertainty measurements. Thus, it does not occupy either the antenna under test or the measurement facilities. In addition, numerical uncertainty estimation enables the isolation of individual error sources and their contributions to pattern uncertainties. Full article
(This article belongs to the Special Issue Recent Advances in Antenna Measurement Techniques)
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15 pages, 3329 KiB  
Article
Identification of Chicken Bone Paste in Starch-Based Sausages Using Laser-Induced Breakdown Spectroscopy
by Haoyu Li, Li Shen, Xiang Han, Yu Liu and Yutong Wang
Sensors 2025, 25(13), 4226; https://doi.org/10.3390/s25134226 - 7 Jul 2025
Viewed by 438
Abstract
This study aims to rapidly in situ identify starch sausage samples with the improper addition of chicken bone paste. Chicken bones play important roles in building materials, biomass fuels, and as food additives after enzymatic hydrolysis, but no current research indicates that chicken [...] Read more.
This study aims to rapidly in situ identify starch sausage samples with the improper addition of chicken bone paste. Chicken bones play important roles in building materials, biomass fuels, and as food additives after enzymatic hydrolysis, but no current research indicates that chicken bones can be directly added to food for consumption. Especially in starch sausages, the addition of chicken bone paste is highly controversial due to potential risks of esophageal laceration and religious concerns. This paper first uses laser-induced breakdown spectroscopy (LIBS) to investigate the elemental differences between starch sausages and chicken bone paste. By preparing mixtures of starch sausages and chicken bone paste at different ratios, the relationships between the spectral peak intensities of elements, such as Ca, Ba, and Sr, and the proportion of chicken bone paste were determined. Through processing methods such as normalization with reference spectral lines, selection of the signal of the second laser pulse at the same position, and electron temperature correction, the determination coefficients (R2) of each element’s spectral lines have significantly improved. Specifically, the R2 values for Ca I, Ca II, Ba II, and Sr II have increased from 0.302, 0.694, 0.857, and 0.691 to 0.972, 0.952, 0.970, and 0.982, respectively. Finally, principal component analysis (PCA) was used to distinguish starch sausages, chicken bone paste, and their mixtures at different ratios, with further effective differentiation achieved through t-distributed stochastic neighbor embedding (t-SNE). The results show that LIBS technology can serve as an effective and rapid method for detecting elemental composition in food and distinguishing different food products, providing safety guarantees for food production and supervision. Full article
(This article belongs to the Special Issue Optical Sensing Technologies for Food Quality and Safety)
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19 pages, 6293 KiB  
Article
Restoring Anomalous Water Surface in DOM Product of UAV Remote Sensing Using Local Image Replacement
by Chunjie Wang, Ti Zhang, Liang Tao and Jiayuan Lin
Sensors 2025, 25(13), 4225; https://doi.org/10.3390/s25134225 - 7 Jul 2025
Viewed by 432
Abstract
In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the [...] Read more.
In the production of a digital orthophoto map (DOM) from unmanned aerial vehicle (UAV)-acquired overlapping images, some anomalies such as texture stretching or data holes frequently occur in water areas due to the lack of significant textural features. These anomalies seriously affect the visual quality and data integrity of the resulting DOMs. In this study, we attempted to eliminate the water surface anomalies in an example DOM via replacing the entire water area with an intact one that was clipped out from one single UAV image. The water surface scope and boundary in the image was first precisely achieved using the multisource seed filling algorithm and contour-finding algorithm. Next, the tie points were selected from the boundaries of the normal and anomalous water surfaces, and employed to realize their spatial alignment using affine plane coordinate transformation. Finally, the normal water surface was overlaid onto the DOM to replace the corresponding anomalous water surface. The restored water area had good visual effect in terms of spectral consistency, and the texture transition with the surrounding environment was also sufficiently natural. According to the standard deviations and mean values of RGB pixels, the quality of the restored DOM was greatly improved in comparison with the original one. These demonstrated that the proposed method had a sound performance in restoring abnormal water surfaces in a DOM, especially for scenarios where the water surface area is relatively small and can be contained in a single UAV image. Full article
(This article belongs to the Special Issue Remote Sensing and UAV Technologies for Environmental Monitoring)
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28 pages, 4149 KiB  
Article
A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification
by Lilu Wang, Yongqi Li, Haiyuan Liu and Taihui Liu
Sensors 2025, 25(13), 4224; https://doi.org/10.3390/s25134224 - 6 Jul 2025
Viewed by 663
Abstract
Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. This limitation hinders [...] Read more.
Remaining Useful Life (RUL) prediction is a crucial task in predictive maintenance. Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. This limitation hinders their deployment on resource-constrained edge devices. To address this issue, we propose TBiGNet, a lightweight Transformer-based classification network model for RUL prediction. TBiGNet features an encoder–decoder architecture that outperforms traditional Transformer models by achieving over 15% higher accuracy while reducing computational load, memory access, and parameter size by more than 98%. In the encoder, we optimize the attention mechanism by integrating the individual linear mappings of queries, keys, and values into an efficient operation, reducing memory access overhead by 60%. Additionally, an adaptive feature pruning module is introduced to dynamically select critical features based on their importance, reducing redundancy and enhancing model accuracy by 6%. The decoder innovatively fuses two different types of features and leverages BiGRU to compensate for the limitations of the attention mechanism in capturing degradation features, resulting in a 7% accuracy improvement. Extensive experiments on the C-MAPSS dataset demonstrate that TBiGNet surpasses existing methods in terms of computational accuracy, model size, and memory access, showcasing significant technical advantages and application potential. Experiments on the C-MPASS dataset show that TBiGNet is superior to the existing methods in terms of calculation accuracy, model size and throughput, showing significant technical advantages and application potential. Full article
(This article belongs to the Section Intelligent Sensors)
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17 pages, 2430 KiB  
Article
Multimodal Navigation and Virtual Companion System: A Wearable Device Assisting Blind People in Independent Travel
by Jingjing Xu, Caiyi Wang, Yancheng Li, Xuantuo Huang, Meina Zhao, Zhuoqun Shen, Yiding Liu, Yuxin Wan, Fengrong Sun, Jianhua Zhang and Shengyong Xu
Sensors 2025, 25(13), 4223; https://doi.org/10.3390/s25134223 - 6 Jul 2025
Viewed by 573
Abstract
Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution [...] Read more.
Visual impairment or even loss seriously affects quality of life. Benefited by the rapid development of sound/laser detection, Global Positioning System (GPS)/Beidou positioning, machine vision and other technologies, the quality of life of blind people is expected to be improved through visual substitution technology. The existing visual substitution devices still have limitations in terms of safety, robustness, and ease of operation. The remote companion system developed here fully utilizes multimodal navigation and remote communication technologies, and the positioning and interaction functions of commercial mobile phones. Together with the accumulated judgment of backend personnel, it can provide real-time, safe, and reliable navigation services for blind people, helping them complete daily activities such as independent travel, circulation, and shopping. The practical results show that the system not only has strong operability and is easy to use, but also can provide users with a strong sense of security and companionship, making it suitable for promotion. In the future, this system can also be promoted for other vulnerable groups such as the elderly. Full article
(This article belongs to the Section Wearables)
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41 pages, 2392 KiB  
Review
How Beyond-5G and 6G Makes IIoT and the Smart Grid Green—A Survey
by Pal Varga, Áron István Jászberényi, Dániel Pásztor, Balazs Nagy, Muhammad Nasar and David Raisz
Sensors 2025, 25(13), 4222; https://doi.org/10.3390/s25134222 - 6 Jul 2025
Viewed by 1006
Abstract
The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. [...] Read more.
The convergence of next-generation wireless communication technologies and modern energy infrastructure presents a promising path toward sustainable and intelligent systems. This survey explores how beyond-5G and 6G communication technologies can support the greening of Industrial Internet of Things (IIoT) systems and smart grids. It highlights the critical challenges in achieving energy efficiency, interoperability, and real-time responsiveness across different domains. The paper reviews key enablers such as LPWAN, wake-up radios, mobile edge computing, and energy harvesting techniques for green IoT, as well as optimization strategies for 5G/6G networks and data center operations. Furthermore, it examines the role of 5G in enabling reliable, ultra-low-latency data communication for advanced smart grid applications, such as distributed generation, precise load control, and intelligent feeder automation. Through a structured analysis of recent advances and open research problems, the paper aims to identify essential directions for future research and development in building energy-efficient, resilient, and scalable smart infrastructures powered by intelligent wireless networks. Full article
(This article belongs to the Special Issue Feature Papers in the Internet of Things Section 2025)
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24 pages, 15534 KiB  
Article
Quantifying Root Cohesion Spatial Heterogeneity Using Remote Sensing for Improved Landslide Susceptibility Modeling: A Case Study of Caijiachuan Landslides
by Zelang Miao, Yaopeng Xiong, Zhiwei Cheng, Bin Wu, Wei Wang and Zuwu Peng
Sensors 2025, 25(13), 4221; https://doi.org/10.3390/s25134221 - 6 Jul 2025
Viewed by 484
Abstract
This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies [...] Read more.
This study investigates the influence of root cohesion spatial heterogeneity on rainfall-induced landslide distribution across the Loess Plateau, addressing limitations in existing methods that oversimplify root reinforcement. Leveraging Landsat and GaoFen satellite images, we developed a regional root cohesion inversion model that quantifies spatial heterogeneity using tree height (derived from time series Landsat imagery) and above-ground biomass (from 30 m resolution satellite products). This approach, integrated with land use-specific hydrological parameters and an infinite slope stability model, significantly improves landslide susceptibility predictions compared to models ignoring root cohesion or using uniform assignments. High-resolution pre- and post-rainfall GaoFen satellite imagery validated landslide inventories, revealing dynamic susceptibility patterns: farmland exhibited the highest risk, followed by artificial and secondary forests, with susceptibility escalating post-rainfall. This study underscores the critical role of remote sensing-driven root cohesion mapping in landslide risk assessment, offering actionable insights for land use planning and disaster mitigation on the Loess Plateau. Full article
(This article belongs to the Special Issue Advanced Sensors for Monitoring and Detection in Geotechnical Engineering)
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36 pages, 11404 KiB  
Article
Synchronous Acquisition and Processing of Electro- and Phono-Cardiogram Signals for Accurate Systolic Times’ Measurement in Heart Disease Diagnosis and Monitoring
by Roberto De Fazio, Ilaria Cascella, Şule Esma Yalçınkaya, Massimo De Vittorio, Luigi Patrono, Ramiro Velazquez and Paolo Visconti
Sensors 2025, 25(13), 4220; https://doi.org/10.3390/s25134220 - 6 Jul 2025
Viewed by 2095
Abstract
Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient [...] Read more.
Cardiovascular diseases remain one of the leading causes of mortality worldwide, highlighting the importance of effective monitoring and early diagnosis. While electrocardiography (ECG) is the standard technique for evaluating the heart’s electrical activity and detecting rhythm and conduction abnormalities, it alone is insufficient for identifying certain conditions, such as valvular disorders. Phonocardiography (PCG) allows the recording and analysis of heart sounds and improves the diagnostic accuracy when combined with ECG. In this study, ECG and PCG signals were simultaneously acquired from a resting adult subject using a compact system comprising an analog front-end (model AD8232, manufactured by Analog Devices, Wilmington, MA, USA) for ECG acquisition and a digital stethoscope built around a condenser electret microphone (model HM-9250, manufactured by HMYL, Anqing, China). Both the ECG electrodes and the microphone were positioned on the chest to ensure the spatial alignment of the signals. An adaptive segmentation algorithm was developed to segment PCG and ECG signals based on their morphological and temporal features. This algorithm identifies the onset and peaks of S1 and S2 heart sounds in the PCG and the Q, R, and S waves in the ECG, enabling the extraction of the systolic time intervals such as EMAT, PEP, LVET, and LVST parameters proven useful in the diagnosis and monitoring of cardiovascular diseases. Based on the segmented signals, the measured averages (EMAT = 74.35 ms, PEP = 89.00 ms, LVET = 244.39 ms, LVST = 258.60 ms) were consistent with the reference standards, demonstrating the reliability of the developed method. The proposed algorithm was validated on synchronized ECG and PCG signals from multiple subjects in an open-source dataset (BSSLAB Localized ECG Data). The systolic intervals extracted using the proposed method closely matched the literature values, confirming the robustness across different recording conditions; in detail, the mean Q–S1 interval was 40.45 ms (≈45 ms reference value, mean difference: −4.85 ms, LoA: −3.42 ms and −6.09 ms) and the R–S1 interval was 14.09 ms (≈15 ms reference value, mean difference: −1.2 ms, LoA: −0.55 ms and −1.85 ms). In conclusion, the results demonstrate the potential of the joint ECG and PCG analysis to improve the long-term monitoring of cardiovascular diseases. Full article
(This article belongs to the Special Issue ECG Signal Processing and Analysis, Computational Technology and Applications: 2nd Edition)
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12 pages, 1443 KiB  
Article
The Influence of School Backpack Load on Dynamic Gait Parameters in 7-Year-Old Boys and Girls
by Paulina Tomal, Anna Fryzowicz, Jarosław Kabaciński, Dominika Witt, Przemysław Lisiński and Lechosław B. Dworak
Sensors 2025, 25(13), 4219; https://doi.org/10.3390/s25134219 - 6 Jul 2025
Viewed by 600
Abstract
School-aged children are routinely exposed to additional physical stress due to carrying school backpacks. These backpacks often exceed recommended limits and can contain not only books and notebooks but also laptops, water bottles, and other personal items. The present study aimed to evaluate [...] Read more.
School-aged children are routinely exposed to additional physical stress due to carrying school backpacks. These backpacks often exceed recommended limits and can contain not only books and notebooks but also laptops, water bottles, and other personal items. The present study aimed to evaluate the impact of different backpack loads (10%, 15%, and 20% of body weight) on dynamic gait parameters in 7-year-old girls and boys. Twenty-six children (13 girls, 13 boys) participated in the study. Gait analysis was performed using the Footscan® system (RSscan International, Olen, Belgium; 2 m × 0.4 m × 0.02 m, 16,384 sensors) equipped with Footscan software version 7 (Gait 2nd generation), examining peak force (FMAX), peak pressure (PMAX), contact area (CA), and time to peak force (Time to FMAX) across five anatomical foot zones. The study revealed significant changes in all parameters, particularly at loads of 15% and 20% of body weight. Increases in plantar pressure, contact area, and asymmetry were observed, along with delays in time to peak force. These findings support the recommendation that children’s backpack loads should not exceed 10% of their body weight to prevent potential adverse effects on postural and musculoskeletal development. Full article
(This article belongs to the Section Intelligent Sensors)
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34 pages, 746 KiB  
Article
Detecting Important Features and Predicting Yield from Defects Detected by SEM in Semiconductor Production
by Umberto Amato, Anestis Antoniadis, Italia De Feis, Anastasiia Doinychko, Irène Gijbels, Antonino La Magna, Daniele Pagano, Francesco Piccinini, Easter Selvan Suviseshamuthu, Carlo Severgnini, Andres Torres and Patrizia Vasquez
Sensors 2025, 25(13), 4218; https://doi.org/10.3390/s25134218 - 6 Jul 2025
Viewed by 593
Abstract
A key step to optimize the tests of semiconductors during the production process is to improve the prediction of the final yield from the defects detected on the wafers during the production process. This study investigates the link between the defects detected by [...] Read more.
A key step to optimize the tests of semiconductors during the production process is to improve the prediction of the final yield from the defects detected on the wafers during the production process. This study investigates the link between the defects detected by a Scanning Electron Microscope (SEM) and the electrical failure of the final semiconductors, with two main objectives: (a) to identify the best layers to inspect by SEM; (b) to develop a model that predicts electrical failures of the semiconductors from the detected defects. The first objective has been reached by a model based on Odds Ratio that gave a (ranked) list of the layers that best predict the final yield. This allows process engineers to concentrate inspections on a few important layers. For the second objective, a regression/classification model based on Gradient Boosting has been developed. As a by-product, this latter model confirmed the results obtained by Odds Ratio analysis. Both models take account of the high lacunarity of the data and have been validated on two distinct datasets from STMicroelectronics. Full article
(This article belongs to the Special Issue Sensors for Predictive Maintenance of Machines)
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21 pages, 2578 KiB  
Article
Coverage Hole Recovery in Hybrid Sensor Networks Based on Key Perceptual Intersections for Emergency Communications
by He Li, Shixian Sun, Chuang Dong, Qinglei Qi, Cong Zhao, Zufeng Fu, Peng Yu and Jiajia Liu
Sensors 2025, 25(13), 4217; https://doi.org/10.3390/s25134217 - 6 Jul 2025
Viewed by 400
Abstract
Wireless sensor networks (WSNs) have found extensive applications in a variety of fields, including military surveillance, wildlife monitoring, industrial process monitoring, and more. The gradual energy depletion of sensor nodes with limited battery energy leads to the dysfunction of some of the nodes, [...] Read more.
Wireless sensor networks (WSNs) have found extensive applications in a variety of fields, including military surveillance, wildlife monitoring, industrial process monitoring, and more. The gradual energy depletion of sensor nodes with limited battery energy leads to the dysfunction of some of the nodes, thus creating coverage holes in the monitored area. Coverage holes can cause the network to fail to deliver high-quality data and can also affect network performance and the quality of service. Therefore, the detection and recovery of coverage holes are major issues in WSNs. In response to these issues, we propose a method for detecting and recovering coverage holes in wireless sensor networks. This method first divides the network into equally sized units, and then selects a representative node for each unit based on two conditions, called an agent. Then, the percentage of each unit covered by nodes can be accurately calculated and holes can be detected. Finally, the holes are recovered using the average of the key perceptual intersections as the initial value of the global optimal point of the particle swarm optimization algorithm. Simulation experiments show that the algorithm proposed in this paper reduces network energy consumption by 6.68%, decreases the distance traveled by mobile nodes by 8.51%, and increases the percentage of network hole recovery by 2.16%, compared with other algorithms. Full article
(This article belongs to the Section Sensor Networks)
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18 pages, 9571 KiB  
Article
TCN-MAML: A TCN-Based Model with Model-Agnostic Meta-Learning for Cross-Subject Human Activity Recognition
by Chih-Yang Lin, Chia-Yu Lin, Yu-Tso Liu, Yi-Wei Chen, Hui-Fuang Ng and Timothy K. Shih
Sensors 2025, 25(13), 4216; https://doi.org/10.3390/s25134216 - 6 Jul 2025
Viewed by 406
Abstract
Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation [...] Read more.
Human activity recognition (HAR) using Wi-Fi-based sensing has emerged as a powerful, non-intrusive solution for monitoring human behavior in smart environments. Unlike wearable sensor systems that require user compliance, Wi-Fi channel state information (CSI) enables device-free recognition by capturing variations in signal propagation caused by human motion. This makes Wi-Fi sensing highly attractive for ambient healthcare, security, and elderly care applications. However, real-world deployment faces two major challenges: (1) significant cross-subject signal variability due to physical and behavioral differences among individuals, and (2) limited labeled data, which restricts model generalization. To address these sensor-related challenges, we propose TCN-MAML, a novel framework that integrates temporal convolutional networks (TCN) with model-agnostic meta-learning (MAML) for efficient cross-subject adaptation in data-scarce conditions. We evaluate our approach on a public Wi-Fi CSI dataset using a strict cross-subject protocol, where training and testing subjects do not overlap. The proposed TCN-MAML achieves 99.6% accuracy, demonstrating superior generalization and efficiency over baseline methods. Experimental results confirm the framework’s suitability for low-power, real-time HAR systems embedded in IoT sensor networks. Full article
(This article belongs to the Special Issue Sensors and Sensing Technologies for Object Detection and Recognition)
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24 pages, 8671 KiB  
Review
Tactile Interaction with Socially Assistive Robots for Children with Physical Disabilities
by Leila Mouzehkesh Pirborj, Caroline Mills, Robert Gorkin III and Karthick Thiyagarajan
Sensors 2025, 25(13), 4215; https://doi.org/10.3390/s25134215 - 6 Jul 2025
Viewed by 644
Abstract
Children with physical disabilities are increasingly using socially assistive robots (SARs) as part of therapy to enhance motivation, engagement, enjoyment, and adherence. Research on SARs in rehabilitation has primarily focused on verbal and visual interaction, but little is known about tactile interaction (physical [...] Read more.
Children with physical disabilities are increasingly using socially assistive robots (SARs) as part of therapy to enhance motivation, engagement, enjoyment, and adherence. Research on SARs in rehabilitation has primarily focused on verbal and visual interaction, but little is known about tactile interaction (physical touch). The objective of this scoping review was to examine empirical studies published between 2010 and 2024 focusing on tactile interaction between SARs and children with physical disabilities, such as cerebral palsy (CP). Nine studies were identified as being eligible after a rigorous selection process, showing that although touch-based SAR interventions have been used in pediatric rehabilitation, structured methodologies and standardized tools are lacking for measuring tactile engagement. In light of the studies’ findings, it is evident that few studies evaluate the therapeutic effects of touch-sensitive SARs, underscoring the need for validated frameworks to assess their efficacy. In this review, SAR and tactile sensing researchers, rehabilitation specialists, and designers are given critical insights into how tactile interaction can enhance the role of SARs in physical therapy. Full article
(This article belongs to the Special Issue Advanced Sensors for Human Health Management)
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22 pages, 9776 KiB  
Article
Detection and Tracking of Environmental Sensing System for Construction Machinery Autonomous Operation Application
by Junyi Chen, Qipeng Cai, Xinhai Hu, Qihuai Chen, Tianliang Lin and Haoling Ren
Sensors 2025, 25(13), 4214; https://doi.org/10.3390/s25134214 - 6 Jul 2025
Viewed by 409
Abstract
There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully [...] Read more.
There are a large number of unstructured scenes and special targets in the construction machinery application scene, which brings greater interference to the environment sensing system for Construction Machinery Autonomous Operation Application. The conventional mature sensing scheme in passenger cars is not fully applicable to construction machinery. By taking the environmental characteristics and operating conditions of construction machinery into consideration, a set of environmental sensing algorithms based on LiDAR for construction machinery scenarios is studied. Real-time target detection of the environment, trajectory tracking, and prediction for dynamic targets are achieved. Decision instructions are provided for upstream detection information for the subsequent behavioral decision-making, motion planning, and other modules. To test the effectiveness of the information exchange between the proposed algorithm and the overall machine interface, the early warning and emergency braking for autonomous operation is implemented. Experiments are carried out through an excavator test platform. The superiority of the optimized detection model is verified through real-time target detection tests at different speeds and under different states. Information exchange between the environmental sensing and the machine interface based on safety warning and braking is achieved. Full article
(This article belongs to the Special Issue Spectral Detection Technology, Sensors and Instruments, 2nd Edition)
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18 pages, 8992 KiB  
Article
Flexible Bioelectrodes-Integrated Miniaturized System for Unconstrained ECG Monitoring
by Yaoliang Zhan, Xue Wang and Jin Yang
Sensors 2025, 25(13), 4213; https://doi.org/10.3390/s25134213 - 6 Jul 2025
Viewed by 469
Abstract
The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. [...] Read more.
The electrocardiogram (ECG) signal plays a crucial role in medical diagnosis, home care, and exercise intensity assessment. However, traditional ECG monitoring devices are difficult to blend with users’ daily routines due to their lack of portability, poor wearability, and inconvenient electrode usage methods. Therefore, utilizing reusable and cost-effective flexible bioelectrodes (with a signal-to-noise ratio of 33 dB), a miniaturized wearable system (MWS) is proposed for unconstrained ECG monitoring, which holds a size of 65 × 52 × 12 mm3 and a weight of 69 g. Given these compelling features, this system enables reliable and high-quality ECG signal monitoring in individuals’ daily activities, including sitting, walking, and cycling, with the acquired signals exhibiting distinguishable QRS characteristics. Furthermore, an exercise intensity classification model was developed based on ECG characteristics and a fully connected neural network (FCNN) algorithm, with an evaluation accuracy of 98%. These results exhibit the promising potential of the MWS in tracking individuals’ physiological signals and assessing exercise intensity. Full article
(This article belongs to the Special Issue Feature Papers in Electronic Sensors 2025)
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30 pages, 3588 KiB  
Article
Optimising Sensor Placement in Heritage Buildings: A Comparison of Model-Based and Data-Driven Approaches
by Estefanía Chaves, Alberto Barontini, Nuno Mendes and Víctor Compán
Sensors 2025, 25(13), 4212; https://doi.org/10.3390/s25134212 - 6 Jul 2025
Viewed by 433
Abstract
The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet [...] Read more.
The long-term preservation of heritage structures relies on effective Structural Health Monitoring (SHM) systems, where sensor placement is key to ensuring early damage detection and guiding conservation efforts. Optimal Sensor Placement (OSP) methods offer a systematic framework to identify efficient sensor configurations, yet their application in historical buildings remains limited. Typically, OSP is driven by numerical models; however, in the context of heritage structures, these models are often affected by substantial uncertainties due to irregular geometries, heterogeneous materials, and unknown boundary conditions. In this scenario, data-driven approaches become particularly attractive as they eliminate the need for potentially unreliable models by relying directly on experimentally identified dynamic properties. This study investigates how the choice of input data influences OSP outcomes, using the Church of Santa Ana in Seville, Spain, as a representative case. Three data sources are considered: an uncalibrated numerical model, a calibrated model, and a data-driven set of modal parameters. Several OSP methods are implemented and systematically compared. The results underscore the decisive impact of the input data on the optimisation process. Although calibrated models may improve certain modal parameters, they do not necessarily translate into better sensor configurations. This highlights the potential of data-driven strategies to enhance the robustness and applicability of SHM systems in the complex and uncertain context of heritage buildings. Full article
(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)
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