Sensors

24 pages, 5004 KiB

Open AccessArticle

A Wearable Sensor Node for Measuring Air Quality Through Citizen Science Approach: Insights from the SOCIO-BEE Project

by Nicole Morresi, Maite Puerta-Beldarrain, Diego López-de-Ipiña, Alex Barco, Oihane Gómez-Carmona, Carlos López-Gomollon, Diego Casado-Mansilla, Maria Kotzagiani, Sara Casaccia, Sergi Udina and Gian Marco Revel

Sensors 2025, 25(12), 3739; https://doi.org/10.3390/s25123739 (registering DOI) - 15 Jun 2025

Abstract

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless [...] Read more.

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

(This article belongs to the Special Issue Sensors Network and Wearables for People Activities and Wellbeing Monitoring)

22 pages, 4129 KiB

Open AccessArticle

Ultrafast Time-Stretch Optical Coherence Tomography Using Reservoir Computing for Fourier-Free Signal Processing

by Weiqing Liao, Tianxiang Luan, Yuanli Yue and Chao Wang

Sensors 2025, 25(12), 3738; https://doi.org/10.3390/s25123738 (registering DOI) - 15 Jun 2025

Abstract

Swept-source optical coherence tomography (SS-OCT) is a widely used imaging technique, particularly in medical diagnostics, due to its ability to provide high-resolution cross-sectional images. However, one of the main challenges in SS-OCT systems is the nonlinearity in wavelength sweeping, which leads to degraded [...] Read more.

Swept-source optical coherence tomography (SS-OCT) is a widely used imaging technique, particularly in medical diagnostics, due to its ability to provide high-resolution cross-sectional images. However, one of the main challenges in SS-OCT systems is the nonlinearity in wavelength sweeping, which leads to degraded depth resolution after Fourier transform. Correcting for this nonlinearity typically requires complex re-sampling and chirp compensation methods. In this paper, we introduce the first ultrafast time-stretch optical coherence tomography (TS-OCT) system that utilizes reservoir computing (RC) to perform direct temporal signal analysis without relying on Fourier transform techniques. By focusing solely on the temporal characteristics of the interference signal, regardless of frequency chirp, we demonstrate a more efficient solution to address the nonlinear wavelength sweeping issue. By leveraging the dynamic temporal processing capabilities of RC, the proposed system effectively bypasses the challenges faced by Fourier analysis, maintaining high-resolution depth measurement without being affected by chirp-introduced spectral broadening. The system operates by categorizing the interference signals generated by variations in sample position. This classification-based approach simplifies the data processing pipeline. We developed an RC-based model to interpret the temporal patterns in the interferometric signals, achieving high classification accuracy. A proof-of-the-concept experiment demonstrated that this method allows for precise depth resolution, independent of system chirp. With an A-scan rate of 50 MHz, the classification model yielded 100% accuracy with a root mean square error (RMSE) of 0.2416. This approach offers a robust alternative to Fourier-based analysis, particularly in systems prone to nonlinearities during signal acquisition. Full article

(This article belongs to the Special Issue Advanced Optical Technologies for Communications, Perception, and Chips)

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15 pages, 2403 KiB

Open AccessArticle

Semi-Physical Simulation Method for Stellar Maps with Color Temperature Information

by Yu Zhang, Bin Zhao, Ke Zhang, Jian Zhang, Songzhou Yang, Dongpeng Yang, Taiyang Ren, Dianwu Ren, Junjie Yang and Jingrui Sun

Sensors 2025, 25(12), 3737; https://doi.org/10.3390/s25123737 (registering DOI) - 14 Jun 2025

Abstract

Existing stellar map simulators lack color temperature information, have complex system structures, and cannot independently control the color temperatures of stars. Therefore, this study developed an OLED-based semi-physical simulation method and a simulation algorithm for stellar maps with color temperature information to realize [...] Read more.

Existing stellar map simulators lack color temperature information, have complex system structures, and cannot independently control the color temperatures of stars. Therefore, this study developed an OLED-based semi-physical simulation method and a simulation algorithm for stellar maps with color temperature information to realize a semi-physical simulation of stellar maps close to the real situation in space. The study also aimed to independently control the color temperature of each star. The simulation effect of the stellar map with color temperature information was verified using four stellar maps. The developed simulator achieved independent and controllable color temperature information for each star in the stellar map. Full article

(This article belongs to the Section Optical Sensors)

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15 pages, 744 KiB

Open AccessArticle

Validation of a Commercially Available IMU-Based System Against an Optoelectronic System for Full-Body Motor Tasks

by Giacomo Villa, Serena Cerfoglio, Alessandro Bonfiglio, Paolo Capodaglio, Manuela Galli and Veronica Cimolin

Sensors 2025, 25(12), 3736; https://doi.org/10.3390/s25123736 (registering DOI) - 14 Jun 2025

Abstract

Inertial measurement units (IMUs) have gained popularity as portable and cost-effective alternatives to optoelectronic motion capture systems for assessing joint kinematics. This study aimed to validate a commercially available multi-sensor IMU-based system against a laboratory-grade motion capture system across lower limb, trunk, and [...] Read more.

Inertial measurement units (IMUs) have gained popularity as portable and cost-effective alternatives to optoelectronic motion capture systems for assessing joint kinematics. This study aimed to validate a commercially available multi-sensor IMU-based system against a laboratory-grade motion capture system across lower limb, trunk, and upper limb movements. Fifteen healthy participants performed a battery of single- and multi-joint tasks while motion data were simultaneously recorded by both systems. Range of motion (ROM) values were extracted from the two systems and compared. The IMU-based system demonstrated high concurrent validity, with non-significant differences in most tasks, root mean square error values generally below 7°, percentage of similarity greater than 97%, and strong correlations (r ≥ 0.77) with the reference system. Systematic biases were trivial (≤3.9°), and limits of agreement remained within clinically acceptable thresholds. The findings indicate that the tested IMU-based system provides ROM estimates statistically and clinically comparable to those obtained with optical reference systems. Given its portability, ease of use, and affordability, the IMU-based system presents a promising solution for motion analysis in both clinical and remote rehabilitation contexts, although future research should extend validation to pathological populations and longer monitoring periods. Full article

(This article belongs to the Special Issue IMU and Innovative Sensors for Healthcare)

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29 pages, 19551 KiB

Open AccessArticle

Let’s Go Bananas: Beyond Bounding Box Representations for Fisheye Camera-Based Object Detection in Autonomous Driving

by Senthil Yogamani, Ganesh Sistu, Patrick Denny and Jane Courtney

Sensors 2025, 25(12), 3735; https://doi.org/10.3390/s25123735 (registering DOI) - 14 Jun 2025

Abstract

Object detection is a mature problem in autonomous driving, with pedestrian detection being one of the first commercially deployed algorithms. It has been extensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near-field sensing. [...] Read more.

Object detection is a mature problem in autonomous driving, with pedestrian detection being one of the first commercially deployed algorithms. It has been extensively studied in the literature. However, object detection is relatively less explored for fisheye cameras used for surround-view near-field sensing. The standard bounding-box representation fails in fisheye cameras due to heavy radial distortion, particularly in the periphery. In this paper, a generic object detection framework is implemented using the base YOLO (You Only Look Once) detector to systematically explore various object representations using the public WoodScape dataset. First, we implement basic representations, namely the standard bounding box, the oriented bounding box, and the ellipse. Secondly, we implement a generic polygon and propose a novel curvature-adaptive polygon, which obtains an improvement of 3 mAP (mean average precision) points. A polygon is expensive to annotate and complex to use in downstream tasks; thus, it is not practical to use it in real-world applications. However, we utilize it to demonstrate that the accuracy gap between the polygon and the bounding box representation is very high due to strong distortion in fisheye cameras. This motivates the design of a distortion-aware optimal representation of the bounding box for fisheye images, which tend to be banana-shaped near the periphery. We derive a novel representation called a curved box and improve it further by leveraging vanishing-point constraints. The proposed curved box representations outperform the bounding box by 3 mAP points and the oriented bounding box by 1.6 mAP points. In addition, the camera geometry tensor is formulated to provide adaptation to non-linear fisheye camera distortion characteristics and improves the performance further by 1.4 mAP points. Full article

(This article belongs to the Special Issue Design, Communication, and Control of Autonomous Vehicle Systems)

21 pages, 16189 KiB

Open AccessArticle

SGDO-SLAM: A Semantic RGB-D SLAM System with Coarse-to-Fine Dynamic Rejection and Static Weighted Optimization

by Qiming Hu, Shuwen Wang, Nanxing Chen, Wei Li, Jiayu Yuan, Enhui Zheng, Guirong Wang and Weimin Chen

Sensors 2025, 25(12), 3734; https://doi.org/10.3390/s25123734 (registering DOI) - 14 Jun 2025

Abstract

Vision sensor-based simultaneous localization and mapping (SLAM) systems are essential for mobile robots to locate and generate spatial models of their surroundings. However, the majority of visual SLAM systems assume static settings, leading to significant accuracy degradation in dynamic scenes. We present SGDO-SLAM, [...] Read more.

Vision sensor-based simultaneous localization and mapping (SLAM) systems are essential for mobile robots to locate and generate spatial models of their surroundings. However, the majority of visual SLAM systems assume static settings, leading to significant accuracy degradation in dynamic scenes. We present SGDO-SLAM, a real-time RGB-D semantic-aware SLAM framework, building upon ORB-SLAM2 to address non-static environments. Firstly, a multi-constraint dynamic rejection method from coarse to fine is proposed. The method starts with coarse rejection by combining semantic and geometric information, followed by detailed rejection using depth information, where static quality weights are quantified based on depth consistency constraints. The method achieves accurate dynamic scene perceptions and improves the accuracy of the system’s positioning. Then, a position optimization method driven by static quality weights is proposed, which prioritizes high-quality static features to enhance pose estimation. Finally, a visualized dense point cloud map is established. We performed experimental evaluations on the TUM RGB-D dataset and the Bonn dataset. The experimental results demonstrate that SGDO-SLAM reduces the absolute trajectory error performance metrics by 95% compared to the ORB-SLAM2 algorithm, while maintaining real-time efficiency and achieving state-of-the-art accuracy in dynamic scenarios. Full article

(This article belongs to the Section Navigation and Positioning)

21 pages, 4140 KiB

Open AccessArticle

Comparability of Methods for Remotely Assessing Gait Quality

by Natasha Hassija, Edward Hill, Helen Dawes and Nancy E. Mayo

Sensors 2025, 25(12), 3733; https://doi.org/10.3390/s25123733 (registering DOI) - 14 Jun 2025

Abstract

Advancements in remote gait analysis technologies enable efficient, cost-effective, and personalized real-time assessments at home. This study aims to contribute evidence as to the comparability of gait quality metrics of three methods of remote gait assessment in individuals with Parkinson’s disease (PD): (1) [...] Read more.

Advancements in remote gait analysis technologies enable efficient, cost-effective, and personalized real-time assessments at home. This study aims to contribute evidence as to the comparability of gait quality metrics of three methods of remote gait assessment in individuals with Parkinson’s disease (PD): (1) observation, (2) a wearable sensor, and (3) pose estimation. A cross-sectional, multiple case series study was conducted remotely. Twenty participants submitted videos performing a modified TUG test with the Heel2Toe^TM wearable. Each video was analysed by six raters using the checklist specific to PD developed for this study and the MediaPipe Pose Landmarker task estimation library. The observational ratings agreed with the Heel2Toe^TM on detecting heel strike 64% of the time and 28.5% of the time on detecting push-off. The difference in the ranks of paired observations based on the Wilcoxon signed rank sum test between the pairs of methods compared was significant for all parameters, except for push-off when estimates from MediaPipe were compared to the ratings from the Observational Checklist, W = 86 (p = 0.498). A combination of digital technologies for remote gait analysis, such as wearable sensors and pose estimation, can detect subtle nuances in gait impairments that may be overlooked by the human eye. Full article

(This article belongs to the Special Issue Innovative Applications of Wearable Sensors in Musculoskeletal Biomechanics and Rehabilitation)

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19 pages, 2829 KiB

Open AccessSystematic Review

Use of Smartphones and Wrist-Worn Devices for Motor Symptoms in Parkinson’s Disease: A Systematic Review of Commercially Available Technologies

by Gabriele Triolo, Daniela Ivaldi, Roberta Lombardo, Angelo Quartarone and Viviana Lo Buono

Sensors 2025, 25(12), 3732; https://doi.org/10.3390/s25123732 (registering DOI) - 14 Jun 2025

Abstract

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales [...] Read more.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor symptoms such as tremors, rigidity, and bradykinesia. The accurate and continuous monitoring of these symptoms is essential for optimizing treatment strategies and improving patient outcomes. Traditionally, clinical assessments have relied on scales and methods that often lack the ability for continuous, real-time monitoring and can be subject to interpretation bias. Recent advancements in wearable technologies, such as smartphones, smartwatches, and activity trackers (ATs), present a promising alternative for more consistent and objective monitoring. This review aims to evaluate the use of smartphones and smart wrist devices, like smartwatches and activity trackers, in the management of PD, assessing their effectiveness in symptom evaluation and monitoring and physical performance improvement. Studies were identified by searching in PubMed, Scopus, Web of Science, and Cochrane Library. Only 13 studies of 1027 were included in our review. Smartphones, smartwatches, and activity trackers showed a growing potential in the assessment, monitoring, and improvement of motor symptoms in people with PD, compared to clinical scales and research-grade sensors. Their relatively low cost, accessibility, and usability support their integration into real-world clinical practice and exhibit validity to support PD management. Full article

(This article belongs to the Section Wearables)

28 pages, 7909 KiB

Open AccessArticle

Transformer-Based Air-to-Ground mmWave Channel Characteristics Prediction for 6G UAV Communications

by Borui Huang, Zhichao Xin, Fan Yang, Yuyang Zhang, Yu Liu, Jie Huang and Ji Bian

Sensors 2025, 25(12), 3731; https://doi.org/10.3390/s25123731 (registering DOI) - 14 Jun 2025

Abstract

With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel [...] Read more.

With the increasing development of 6th-generation (6G) air-to-ground (A2G) communications, the combination of millimeter-wave (mmWave) and multiple-input multiple-output (MIMO) technologies can offer unprecedented bandwidth and capacity for unmanned aerial vehicle (UAV) communications. The introduction of new technologies will also make the UAV channel characteristics more complex and variable, posing higher requirements for UAV channel modeling. This paper presents a novel predictive channel modeling method based on Transformer architecture by integrating data-driven approaches with UAV air-to-ground channel modeling. By introducing the mmWave and MIMO into UAV communications, the channel data of UAVs at various flight altitudes is first collected. Based on the Transformer network, the typical UAV channel characteristics, such as received power, delay spread, and angular spread, are then predicted and analyzed. The results indicate that the proposed predictive method exhibits excellent performance in prediction accuracy and stability, effectively addressing the complexity and variability of channel characteristics caused by mmWave bands and MIMO technology. This method not only provides strong support for the design and optimization of future 6G UAV communication systems but also lays a solid communication foundation for the widespread application of UAVs in intelligent transportation, logistics, and other fields in the future. Full article

(This article belongs to the Special Issue Advanced Millimeter Wave Antenna Systems for 5G and beyond 5G Wireless Communications)

22 pages, 1269 KiB

Open AccessArticle

Semi-Supervised Learned Autoencoder for Classification of Events in Distributed Fibre Acoustic Sensors

by Artem Kozmin, Oleg Kalashev, Alexey Chernenko and Alexey Redyuk

Sensors 2025, 25(12), 3730; https://doi.org/10.3390/s25123730 (registering DOI) - 14 Jun 2025

Abstract

The global market for infrastructure security systems based on distributed acoustic sensors is rapidly expanding, driven by the need for timely detection and prevention of potential threats. However, deploying these systems is challenging due to the high costs associated with dataset creation. Additionally, [...] Read more.

The global market for infrastructure security systems based on distributed acoustic sensors is rapidly expanding, driven by the need for timely detection and prevention of potential threats. However, deploying these systems is challenging due to the high costs associated with dataset creation. Additionally, advanced signal processing algorithms are necessary for accurately determining the location and nature of detected events. In this paper, we present an enhanced approach based on semi-supervised learning for developing event classification models tailored for real-time and continuous perimeter monitoring of infrastructure facilities. The proposed method leverages a hybrid architecture combining an autoencoder and a classifier to enhance the accuracy and efficiency of event classification. The autoencoder extracts essential features from raw data using unlabeled data, improving the model’s ability to learn meaningful representations. The classifier, trained on labeled data, recognizes and classifies specific events based on these features. The integrated loss function incorporates elements from both the autoencoder and the classifier, guiding the autoencoder to extract features relevant for accurate event classification. Validation using real-world datasets demonstrates that the proposed method achieves recognition performance comparable to the baseline model, while requiring less labeled data and employing a simpler architecture. These results offer practical insights for reducing deployment costs, enhancing system performance, and increasing throughput for new deployments. Full article

(This article belongs to the Special Issue Fiber Optic Sensing and Applications)

17 pages, 1538 KiB

Open AccessArticle

AI-Driven Adaptive Communications for Energy-Efficient Underwater Acoustic Sensor Networks

by A. Ur Rehman, Laura Galluccio and Giacomo Morabito

Sensors 2025, 25(12), 3729; https://doi.org/10.3390/s25123729 (registering DOI) - 14 Jun 2025

Abstract

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework [...] Read more.

Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment. Full article

(This article belongs to the Special Issue Energy Efficient Design in Wireless Ad Hoc and Sensor Networks)

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22 pages, 2433 KiB

Open AccessArticle

FedEmerge: An Entropy-Guided Federated Learning Method for Sensor Networks and Edge Intelligence

by Koffka Khan

Sensors 2025, 25(12), 3728; https://doi.org/10.3390/s25123728 (registering DOI) - 14 Jun 2025

Abstract

Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelligence, where data privacy, bandwidth constraints, and data locality [...] Read more.

Introduction: Federated Learning (FL) is a distributed machine learning paradigm where a global model is collaboratively trained across multiple decentralized clients without exchanging raw data. This is especially important in sensor networks and edge intelligence, where data privacy, bandwidth constraints, and data locality are paramount. Traditional FL methods like FedAvg struggle with highly heterogeneous (non-IID) client data, which is common in these settings. Background: Traditional FL aggregation methods, such as FedAvg, weigh client updates primarily by dataset size, potentially overlooking the informativeness or diversity of each client’s contribution. These limitations are especially pronounced in sensor networks and IoT environments, where clients may hold sparse, unbalanced, or single-modality data. Methods: We propose FedEmerge, an entropy-guided aggregation approach that adjusts each client’s impact on the global model based on the information entropy of its local data distribution. This formulation introduces a principled way to quantify and reward data diversity, enabling an emergent collective learning dynamic in which globally informative updates drive convergence. Unlike existing methods that weigh updates by sample count or heuristics, FedEmerge prioritizes clients with more representative, high-entropy data. The FedEmerge algorithm is presented with full mathematical detail, and we prove its convergence under the Polyak–Łojasiewicz (PL) condition. Results: Theoretical analysis shows that FedEmerge achieves linear convergence to the optimal model under standard assumptions (smoothness and PL condition), similar to centralized gradient descent. Empirically, FedEmerge improves global model accuracy and convergence speed on highly skewed non-IID benchmarks, and it reduces performance disparities among clients compared to FedAvg. Evaluations on CIFAR-10 (non-IID), Federated EMNIST, and Shakespeare datasets confirm its effectiveness in practical edge-learning settings. Conclusions: This entropy-guided federated strategy demonstrates that weighting client updates by data diversity enhances learning outcomes in heterogeneous networks. The approach preserves privacy like standard FL and adds minimal computation overhead, making it a practical solution for real-world federated systems. Full article

(This article belongs to the Section Sensor Networks)

19 pages, 4767 KiB

Open AccessArticle

Risk Mitigation of a Heritage Bridge Using Noninvasive Sensors

by Ricky W. K. Chan and Takahiro Iwata

Sensors 2025, 25(12), 3727; https://doi.org/10.3390/s25123727 (registering DOI) - 14 Jun 2025

Abstract

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old [...] Read more.

Bridges are fundamental components of transportation infrastructure, facilitating the efficient movement of people and goods. However, the conservation of heritage bridges introduces additional challenges, encompassing environmental, social, cultural, and economic dimensions of sustainability. This study investigates risk mitigation strategies for a heritage-listed, 120-year-old reinforced concrete bridge in Australia—one of the nation’s earliest examples of reinforced concrete construction, which remains operational today. The structure faces multiple risks, including passage of overweight vehicles, environmental degradation, progressive crack development due to traffic loading, and potential foundation scouring from an adjacent stream. Due to the heritage status and associated legal constraints, only non-invasive testing methods were employed. Ambient vibration testing was conducted to identify the bridge’s dynamic characteristics under normal traffic conditions, complemented by non-contact displacement monitoring using laser distance sensors. A digital twin structural model was subsequently developed and validated against field data. This model enabled the execution of various “what-if” simulations, including passage of overweight vehicles and loss of foundation due to scouring, providing quantitative assessments of potential risk scenarios. Drawing on insights gained from the case study, the article proposes a six-phase Incident Response Framework tailored for heritage bridge management. This comprehensive framework incorporates remote sensing technologies for incident detection, digital twin-based structural assessment, damage containment and mitigation protocols, recovery planning, and documentation to prevent recurrence—thus supporting the long-term preservation and functionality of heritage bridge assets. Full article

(This article belongs to the Special Issue Feature Papers in Physical Sensors 2025)

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24 pages, 11104 KiB

Open AccessArticle

HGCS-Det: A Deep Learning-Based Solution for Localizing and Recognizing Household Garbage in Complex Scenarios

by Houkui Zhou, Chang Chen, Zhongyi Xia, Qifeng Ding, Qinqin Liao, Qun Wang, Huimin Yu, Haoji Hu, Guangqun Zhang, Junguo Hu and Tao He

Sensors 2025, 25(12), 3726; https://doi.org/10.3390/s25123726 (registering DOI) - 14 Jun 2025

Abstract

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces [...] Read more.

With the rise of deep learning technology, intelligent garbage detection provides a new idea for garbage classification management. However, due to the interference of complex environments, coupled with the influence of the irregular features of garbage, garbage detection in complex scenarios still faces significant challenges. Moreover, some of the existing research suffer from shortcomings in either their precision or real-time performance, particularly when applied to complex garbage detection scenarios. Therefore, this paper proposes a model based on YOLOv8, namely HGCS-Det, for detecting garbage in complex scenarios. The HGCS-Det model is designed as follows: Firstly, the normalization attention module is introduced to calibrate the model’s attention to targets and to suppress the environmental noise interference information. Additionally, to weigh the attention-feature contributions, an Attention Feature Fusion module is employed to complement the attention weights of each channel. Subsequently, an Instance Boundary Reinforcement module is established to capture the fine-grained features of garbage by combining strong gradient information with semantic information. Finally, the Slide Loss function is applied to dynamically weight hard samples arising from the complex detection environments to improve the recognition accuracy of hard samples. With only a slight increase in parameters (3.02M), HGCS-Det achieves a 93.6% mean average precision (mAP) and 86 FPS on the public HGI30 dataset, which is a 3.33% higher mAP value than from YOLOv12, and outperforms the state-of-the-art (SOTA) methods in both efficiency and applicability. Notably, HGCS-Det maintains a lightweight architecture while enhancing the detection accuracy, enabling real-time performance even in resource-constrained environments. These characteristics significantly improve its practical applicability, making the model well suited for deployment in embedded devices and real-world garbage classification systems. This method can serve as a valuable technical reference for the engineering application of garbage classification. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

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13 pages, 2636 KiB

Open AccessArticle

Viscoelastic Response of Sugar Beet Root Tissue in Quasi-Static and Impact Loading Conditions

by Paweł Kołodziej, Krzysztof Gołacki and Zbigniew Stropek

Sensors 2025, 25(12), 3725; https://doi.org/10.3390/s25123725 (registering DOI) - 14 Jun 2025

Abstract

This paper presents the results of quasi-static tests carried out using a texturometer and of impact tests combined with stress relaxation on a stand equipped with a heavy pendulum of the hammer type. The tests were carried out using fresh roots and those [...] Read more.

This paper presents the results of quasi-static tests carried out using a texturometer and of impact tests combined with stress relaxation on a stand equipped with a heavy pendulum of the hammer type. The tests were carried out using fresh roots and those stored at 20 °C for 120 h. The impact velocities V_d were 0.001, 0.002, 0.01, 0.02, 0.75, and 1.25 m·s⁻¹. Compiling the relaxation times T₁ for V_d indicated their large drops for both fresh and stored roots. The largest average values T₁ were obtained in the range from 0.197 s to 0.111 s at the small velocities of deformation 0.001–0.02 m·s⁻¹ and the smallest ones in the range from 0.0252 to 0.0228 s at the V_d equal to 0.75 and 1.25 m·s⁻¹. A decrease in T₂ values was observed in the average range of 8.02–4.27 s at V_d = 0.001–0.02 m·s⁻¹ for fresh beets. For the velocities 0.75 m·s⁻¹ and 1.25 m·s⁻¹ and stored roots, the range of average values was smaller and ranged from 6.13 s to 4.54 s. The reaction forces of the F_p sample reached the highest average levels from 168.2 N to 190.8 N for fresh roots and 46.5 to 56.2 N for 5-day-old roots. However, the lowest F_p was recorded at speeds (0.001–0.02 ms⁻¹) 57.5–62.3 N for the fresh roots and 46.5–56.2 N for the 5-day-old roots. For the velocities greater than 0.75 m·s⁻¹ and 1.25 m·s⁻¹, the values of reaction forces increased at the average values 168.2–190.8 N for the fresh roots and 158.2–175.4 N for 5-day-old ones. Full article

(This article belongs to the Special Issue Current Advances in Sensor Design, Innovation, and Their Industry Applications)

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22 pages, 9997 KiB

Open AccessArticle

Skin-Inspired Magnetoresistive Tactile Sensor for Force Characterization in Distributed Areas

by Francisco Mêda, Fabian Näf, Tiago P. Fernandes, Alexandre Bernardino, Lorenzo Jamone, Gonçalo Tavares and Susana Cardoso

Sensors 2025, 25(12), 3724; https://doi.org/10.3390/s25123724 (registering DOI) - 13 Jun 2025

Abstract

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with [...] Read more.

Touch is a crucial sense for advanced organisms, particularly humans, as it provides essential information about the shape, size, and texture of contacting objects. In robotics and automation, the integration of tactile sensors has become increasingly relevant, enabling devices to properly interact with their environment. This study aimed to develop a biomimetic, skin-inspired tactile sensor device capable of sensing applied force, characterizing it in three dimensions, and determining the point of application. The device was designed as a 4 × 4 matrix of tunneling magnetoresistive sensors, which provide a higher sensitivity in comparison to the ones based on the Hall effect, the current standard in tactile sensors. These detect magnetic field changes along a single axis, wire-bonded to a PCB and encapsulated in epoxy. This sensing array detects the magnetic field from an overlayed magnetorheological elastomer composed of Ecoflex and 5 µm neodymium–iron–boron ferromagnetic particles. Structural integrity tests showed that the device could withstand forces above 100 N, with an epoxy coverage of 0.12 mL per sensor chip. A 3D movement stage equipped with an indenting tip and force sensor was used to collect device data, which was then used to train neural network models to predict the contact location and 3D magnitude of the applied force. The magnitude-sensing model was trained on 31,260 data points, being able to accurately characterize force with a mean absolute error ranging between 0.07 and 0.17 N. The spatial sensitivity model was trained on 171,008 points and achieved a mean absolute error of 0.26 mm when predicting the location of applied force within a sensitive area of 25.5 mm × 25.5 mm using sensors spaced 4.5 mm apart. For points outside the testing range, the mean absolute error was 0.63 mm. Full article

(This article belongs to the Special Issue Smart Magnetic Sensors and Application)

19 pages, 1839 KiB

Open AccessArticle

Dynamic Monitoring of a Bridge from GNSS-RTK Sensor Using an Improved Hybrid Denoising Method

by Chunbao Xiong, Zhi Shang, Meng Wang and Sida Lian

Sensors 2025, 25(12), 3723; https://doi.org/10.3390/s25123723 (registering DOI) - 13 Jun 2025

Abstract

This study focused on the monitoring of a bridge using the global navigation satellite system real-time kinematic (GNSS-RTK) sensor. An improved hybrid denoising method was developed to enhance the GNSS-RTK’s accuracy. The improved hybrid denoising method consists of the improved complete ensemble empirical [...] Read more.

This study focused on the monitoring of a bridge using the global navigation satellite system real-time kinematic (GNSS-RTK) sensor. An improved hybrid denoising method was developed to enhance the GNSS-RTK’s accuracy. The improved hybrid denoising method consists of the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), the detrended fluctuation analysis (DFA), and an improved wavelet threshold denoising method. The stability experiment demonstrated the superiority of the improved wavelet threshold denoising method in reducing the noise of the GNSS-RTK. A noisy simulation signal was created to assess the performance of the proposed method. Compared to the ICEEMDAN method and the CEEMDAN-WT method, the proposed method achieves lower RMSE and higher SNR. The signal obtained by the proposed method is similar to the original signal. Then, GNSS-RTK was used to monitor a bridge in maintenance and rehabilitation construction. The bridge monitoring experiment lasted for four hours. (Considering the space limitation of the article, only representative 600 s data is displayed in the paper.) The bridge is located in Tianjin, China. The original displacement ranges are −14.9~19.3 in the north–south direction; −26.9~24.7 in the east–west direction; and −46.7~52.3 in the vertical direction. The displacement ranges processed by the proposed method are −12.3~17.2 in the north–south direction; −24.6~24.1 in the east–west direction; and −46.7~51.1 in the vertical direction. The proposed method processed fewer displacements than the initial monitoring displacements. It indicates the proposed method reduces noise significantly when monitoring the bridge based on the GNSS-RTK sensor. The average sixth-order frequency from PSD is 1.0043 Hz. The difference between the PSD and FEA is only 0.99%. The sixth-order frequency from the PSD is similar to that from the FEA. The lower modes’ natural frequencies from the PSD are smaller than those from the FEA. It illustrates the fact that, during the repair process, the missing load-bearing rods made the bridge less stiff and strong. The smaller natural frequencies of the bridge, the complex construction environment, the diversity of workers’ operations, and some unforeseen circumstances occurring in the construction all bring risks to the safety of the bridge. We should pay more attention to the dynamic monitoring of the bridge during construction in order to understand the structural status in time to prevent accidents. Full article

(This article belongs to the Section Intelligent Sensors)

15 pages, 6650 KiB

Open AccessArticle

Signal Enhancement in Magnetoelastic Ribbons Through Thermal Annealing: Evaluation of Magnetic Signal Output in Different Metglas Materials

by Georgios Samourgkanidis, Dimitris Kouzoudis, Panagiotis Charalampous and Eyad Adnan

Sensors 2025, 25(12), 3722; https://doi.org/10.3390/s25123722 - 13 Jun 2025

Abstract

This study explores the impact of thermal annealing on the magnetic signal enhancement of three distinct Metglas ribbon materials: 2826MB3, 2605SA1, and 2714A. Each material underwent a systematic annealing process under a range of temperatures (50–500

^{\circ}

C) and durations (10–60 min) to [...] Read more.

This study explores the impact of thermal annealing on the magnetic signal enhancement of three distinct Metglas ribbon materials: 2826MB3, 2605SA1, and 2714A. Each material underwent a systematic annealing process under a range of temperatures (50–500

^{\circ}

C) and durations (10–60 min) to evaluate the influence of thermal treatment on their magnetic signal response. The experimental setup applied a constant excitation frequency of 20 kHz, allowing for direct comparison under identical measurement conditions. The results show that while all three alloys benefit from annealing, their responses differ in magnitude, stability, and sensitivity. The 2826MB3 and 2605SA1 ribbons exhibited similar enhancement patterns, with maximum normalized voltage increases of 75.8% and approximately 70%, respectively. However, 2605SA1 displayed a more abrupt signal drop at elevated temperatures, suggesting reduced thermal stability. In contrast, 2714A reached the highest enhancement at 86.8% but also demonstrated extreme sensitivity to over-annealing, losing its magnetic response rapidly at higher temperatures. The findings highlight the critical role of carefully optimized annealing parameters in maximizing sensor performance and offer practical guidance for the development of advanced magnetoelastic sensing systems. Full article

(This article belongs to the Special Issue Advanced Magnetic and Fluorescent Nanomaterial Sensors: Design, Development, and Application)

20 pages, 2150 KiB

Open AccessArticle

Industrial Image Anomaly Detection via Synthetic-Anomaly Contrastive Distillation

by Junxian Li, Mingxing Li, Shucheng Huang, Gang Wang and Xinjing Zhao

Sensors 2025, 25(12), 3721; https://doi.org/10.3390/s25123721 - 13 Jun 2025

Abstract

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies [...] Read more.

Industrial image anomaly detection plays a critical role in intelligent manufacturing by automatically identifying defective products through visual inspection. While unsupervised approaches eliminate dependency on annotated anomaly samples, current teacher–student framework-based methods still face two fundamental limitations: insufficient discriminative capability for structural anomalies and suboptimal anomaly feature decoupling efficiency. To address these challenges, we propose a Synthetic-Anomaly Contrastive Distillation (SACD) framework for industrial anomaly detection. SACD comprises two pivotal components: (1) a reverse distillation (RD) paradigm whereby a pre-trained teacher network extracts hierarchically structured representations, subsequently guiding the student network with inverse architectural configuration to establish hierarchical feature alignment; (2) a group of feature calibration (FeaCali) modules designed to refine the student’s outputs by eliminating anomalous feature responses. During training, SACD adopts a dual-branch strategy, where one branch encodes multi-scale features from defect-free images, while a Siamese anomaly branch processes synthetically corrupted counterparts. FeaCali modules are trained to strip out a student’s anomalous patterns in anomaly branches, enhancing the student network’s exclusive modeling of normal patterns. We construct a dual-objective optimization integrating cross-model distillation loss and intra-model contrastive loss to train SACD for feature alignment and discrepancy amplification. At the inference stage, pixel-wise anomaly scores are computed through multi-layer feature discrepancies between the teacher’s representations and the student’s refined outputs. Comprehensive evaluations on the MVTec AD and BTAD benchmark demonstrate that our method is effective and superior to current knowledge distillation-based approaches. Full article

(This article belongs to the Section Industrial Sensors)

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