Sensors

23 pages, 5770 KiB

Open AccessReview

Are Wearable ECG Devices Ready for Hospital at Home Application?

by Jorge Medina-Avelino, Ricardo Silva-Bustillos and Juan A. Holgado-Terriza

Sensors 2025, 25(10), 2982; https://doi.org/10.3390/s25102982 (registering DOI) - 9 May 2025

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the role of wearable devices with electrocardiogram (ECG) [...] Read more.

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the role of wearable devices with electrocardiogram (ECG) capabilities for continuous cardiac monitoring, a crucial aspect for the timely detection and management of various cardiac conditions. The functionality of current wearable technology is scrutinized to determine its effectiveness in meeting clinical needs, employing a proposed ABCD guide (accuracy, benefit, compatibility, and data governance) for evaluation. While smartwatches show promise in detecting arrhythmias like atrial fibrillation, their broader diagnostic capabilities, including the potential for monitoring corrected QT (QTc) intervals during pharmacological interventions and approximating multi-lead ECG information for improved myocardial infarction detection, are also explored. Recent advancements in machine learning and deep learning for cardiac health monitoring are highlighted, alongside persistent challenges, particularly concerning signal quality and the need for further validation for widespread adoption in older adults and Hospital at Home settings. Ongoing improvements are necessary to overcome current limitations and fully realize the potential of wearable ECG technology in providing optimal care for high-risk patients. Full article

(This article belongs to the Special Issue Wearable Sensors for Human Health Monitoring in Clinical and Ecologic Scenarios)

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13 pages, 1689 KiB

Open AccessArticle

The Reliability and Validity of an Isometric Knee Strength Measurement Device in Older Adult Individuals

by Jae-Soo Hong, Jeong-Bae Ko, Myeong-Min Ju, Byoung-Kwon Lee, Dae-Sung Park and Su-Ha Lee

Sensors 2025, 25(10), 2981; https://doi.org/10.3390/s25102981 - 8 May 2025

Abstract

This study aims to evaluate the reliability and validity of the Leg Strength Analyzer (IB-LS) in assessing isometric knee flexion and extension strength in elderly adults, compare its performance with that of the CSMI dynamometer, and examine its agreement with isokinetic knee strength [...] Read more.

This study aims to evaluate the reliability and validity of the Leg Strength Analyzer (IB-LS) in assessing isometric knee flexion and extension strength in elderly adults, compare its performance with that of the CSMI dynamometer, and examine its agreement with isokinetic knee strength measurements. A total of 21 elderly participants (mean age: 65.10 ± 4.56 years) were recruited. Participants underwent knee flexion and extension strength assessments using both the IB-LS and CSMI devices, with isokinetic strength at 60°/s measured in a follow-up session at least one day later. The IB-LS demonstrated high test–retest reliability in elderly adults (ICC = 0.856–0.987). The validity analysis comparing IB-LS isometric peak torque with CSMI isometric peak torque showed moderate to high validity (ICC = 0.826–0.946). Furthermore, IB-LS isometric peak torque and CSMI isokinetic 60°/s peak torque demonstrated high agreement (ICC = 0.775–0.881), demonstrating its strong association with isokinetic strength assessments. Bland–Altman analysis revealed that mean differences between IB-LS and CSMI isometric peak torque values ranged from 13.2 to 93.5 Nm, with limits of agreement (LoA) spanning from −55.8 to 192.5 Nm. When comparing IB-LS isometric peak torque with CSMI isokinetic 60°/s peak torque, mean differences ranged from 31.0 to 56.0 Nm, with LoA from −28.5 to 138.9 Nm. The IB-LS is a reliable and valid tool for evaluating isometric knee strength in elderly adults. Its strong agreement with the CSMI dynamometer and close correlation with isokinetic strength measurements indicate that IB-LS can be a feasible alternative for assessing knee strength in clinical and research settings focused on elderly populations. Full article

(This article belongs to the Collection Sensors in Biomechanics)

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22 pages, 7459 KiB

Open AccessArticle

Robust Line Feature Matching via Point–Line Invariants and Geometric Constraints

by Chenyang Zhang, Yunfei Xiang, Qiyuan Wang, Shuo Gu, Jianghua Deng and Rongchun Zhang

Sensors 2025, 25(10), 2980; https://doi.org/10.3390/s25102980 - 8 May 2025

Abstract

Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, [...] Read more.

Line feature matching is a crucial aspect of computer vision and image processing tasks, attracting significant research attention. Most line matching algorithms predominantly rely on local feature descriptors or deep learning modules, which often suffer from low robustness and poor generalization. In response, this paper presents a novel line feature matching approach grounded in point–line invariants through spatial invariant relationships. By leveraging a robust point feature matching algorithm, an initial set of point feature matches is acquired. Subsequently, the line feature supporting area is partitioned, and a constant ratio invariant is formulated based on the distances from point to line features within corresponding neighborhood domains. Additionally, a direction vector invariant is also introduced, jointly constructing a dual invariant for line matching. An initial matching matrix and line feature match pairs are derived using this dual invariant. Subsequent geometric constraints within line feature matches eliminate residual outliers. Comprehensive evaluations under diverse imaging conditions, along with comparisons to several state-of-the-art algorithms, demonstrate that our proposal achieved remarkable performance in terms of both accuracy and robustness. Our implementation code will be publicly released upon the acceptance of this paper. Full article

(This article belongs to the Special Issue Multi-Modal Data Sensing and Processing)

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20 pages, 14255 KiB

Open AccessArticle

Building Damage Visualization Through Three-Dimensional Reconstruction and Window Detection

by Ittetsu Kuniyoshi, Itsuki Nagaike, Sachie Sato and Yue Bao

Sensors 2025, 25(10), 2979; https://doi.org/10.3390/s25102979 - 8 May 2025

Abstract

This study proposes a non-contact method for assessing building inclination and damage by integrating 3D point cloud data with image recognition techniques. Conventional approaches, such as plumb bobs, require physical contact, posing safety risks and practical challenges, especially in densely built urban areas. [...] Read more.

This study proposes a non-contact method for assessing building inclination and damage by integrating 3D point cloud data with image recognition techniques. Conventional approaches, such as plumb bobs, require physical contact, posing safety risks and practical challenges, especially in densely built urban areas. The proposed method utilizes a 3D scanner to capture point cloud data and images, which are processed to extract building surfaces, detect inclination, and assess secondary structural components such as window frames. Experiments were conducted on prefabricated structures, detached houses, and dense residential areas to validate the method’s accuracy. Results show that the proposed approach achieved measurement accuracy comparable to or better than traditional methods, with an error reduction of approximately 19% in prefabricated structures and 21.72% in detached houses. Additionally, the method successfully identified window frame deformations, contributing to a comprehensive assessment of structural integrity. By applying gradient-based color mapping, damage severity was visualized intuitively. The findings demonstrate that this system can replace conventional measurement techniques, enabling safe, efficient, and large-scale post-disaster assessments. Future work will focus on enhancing point cloud interpolation and refining machine learning-based damage classification for broader applicability. Full article

(This article belongs to the Section Sensing and Imaging)

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30 pages, 1588 KiB

Open AccessArticle

Gearbox Fault Diagnosis Under Noise and Variable Operating Conditions Using Multiscale Depthwise Separable Convolution and Bidirectional Gated Recurrent Unit with a Squeeze-and-Excitation Attention Mechanism

by Xiaoteng Ma, Kejia Zhai, Nana Luo, Yehui Zhao and Guangming Wang

Sensors 2025, 25(10), 2978; https://doi.org/10.3390/s25102978 - 8 May 2025

Abstract

Gearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise [...] Read more.

Gearbox condition monitoring is essential for ensuring the reliability of power transmission systems. However, the existing methods are constrained by shallow feature extraction and unidirectional temporal modeling. To address these limitations, this study proposes a novel fault diagnosis framework that integrates multiscale depthwise separable convolution, bidirectional gated recurrent units, and a squeeze-and-excitation attention mechanism. This approach enables multiscale feature extraction from vibration signals, bidirectional temporal modeling, and the enhancement of critical fault-related information. The experimental results demonstrate that the proposed method significantly outperforms conventional models in terms of fault diagnosis accuracy, noise robustness, and adaptability to varying operating conditions. The attention mechanism effectively suppresses noise interference, while bidirectional temporal modeling accurately captures fault propagation characteristics, thereby improving adaptability to dynamic conditions. This research provides a highly robust solution for intelligent gearbox fault diagnosis in complex industrial environments. Full article

(This article belongs to the Special Issue Fault Diagnosis and Vibration Signal Processing in Rotor Systems)

16 pages, 1435 KiB

Open AccessArticle

Vehicle Re-Identification Method Based on Efficient Self-Attention CNN-Transformer and Multi-Task Learning Optimization

by Yu Wang, Rui Li and Yihan Shao

Sensors 2025, 25(10), 2977; https://doi.org/10.3390/s25102977 - 8 May 2025

Abstract

To address the challenges of low accuracy in vehicle re-identification caused by intra-class variations, inter-class similarities and environmental factors, this paper proposes a CNN-Transformer architecture (IBNT-Net) for vehicle re-identification. The method builds upon a ResNet50-IBN backbone network and incorporates an improved multi-head self-attention [...] Read more.

To address the challenges of low accuracy in vehicle re-identification caused by intra-class variations, inter-class similarities and environmental factors, this paper proposes a CNN-Transformer architecture (IBNT-Net) for vehicle re-identification. The method builds upon a ResNet50-IBN backbone network and incorporates an improved multi-head self-attention mechanism to aggregate contextual information. It constructs a multi-branch vehicle re-identification network that combines both global and local features. Furthermore, a multi-task learning strategy is adopted, creating specialized learning pathways for classification tasks and metric learning tasks. Group convolution techniques are utilized to reduce model complexity, making it suitable for resource-constrained environments. On the VeRi-776 and VehicleID dataset, the proposed method achieves state-of-the-art performance with less parameters. The experimental results show that the proposed method has better re-identification performance and the extracted features are more discriminative. Full article

(This article belongs to the Special Issue Advances in Sensing, Imaging and Computing for Autonomous Driving: 2nd Edition)

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18 pages, 22040 KiB

Open AccessArticle

Robotic Hand–Eye Calibration Method Using Arbitrary Targets Based on Refined Two-Step Registration

by Zining Song, Chenglong Sun, Yunquan Sun and Lizhe Qi

Sensors 2025, 25(10), 2976; https://doi.org/10.3390/s25102976 - 8 May 2025

Abstract

To optimize the structure and workflow of the 3D measurement robot system, reduce the dependence on specific calibration targets or high-precision calibration objects, and improve the versatility of the system’s self-calibration, this paper proposes a robot hand–eye calibration algorithm based on irregular targets. [...] Read more.

To optimize the structure and workflow of the 3D measurement robot system, reduce the dependence on specific calibration targets or high-precision calibration objects, and improve the versatility of the system’s self-calibration, this paper proposes a robot hand–eye calibration algorithm based on irregular targets. By collecting the 3D structural information of an object in space at different positions, a random sampling consistency evaluation based on the fast point feature histogram (FPFH) is adopted, and the iterative closest point (ICP) registration algorithm with the introduction of a probability model and covariance optimization is combined to iteratively solve the spatial relationship between point clouds, and the hand–eye calibration equation group is constructed through spatial relationship analysis to solve the camera’s hand–eye matrix. In the experiment, we use arbitrary objects as targets to execute the hand–eye calibration algorithm and verify the effectiveness of the method. Full article

(This article belongs to the Section Sensors and Robotics)

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18 pages, 6276 KiB

Open AccessArticle

MF-YOLOv10: Research on the Improved YOLOv10 Intelligent Identification Algorithm for Goods

by Quanwei Wang, Xiaoyang Wang, Jiayi Hou, Xuying Liu, Hao Wen and Ziya Ji

Sensors 2025, 25(10), 2975; https://doi.org/10.3390/s25102975 - 8 May 2025

Abstract

To enhance the accuracy of identifying parts and goods in automated loading and unloading machines, this study proposes a lightweight detection model, MF-YOLOv10, based on intelligent recognition of goods’ shape, color, position, and environmental interference. The algorithm significantly improves the feature extraction and [...] Read more.

To enhance the accuracy of identifying parts and goods in automated loading and unloading machines, this study proposes a lightweight detection model, MF-YOLOv10, based on intelligent recognition of goods’ shape, color, position, and environmental interference. The algorithm significantly improves the feature extraction and detection capabilities by replacing the traditional IoU loss function with the MPDIoU and introducing the SCSA attention module. These enhancements improve the detection performance of multi-scale targets, enabling the improved YOLOv10 model to achieve precise recognition of goods’ shape and quantity. Experimental results demonstrate that the MF-YOLOv10 model achieves accuracy, recall, mAP50, and F1 scores of 92.12%, 84.20%, 92.24%, and 87.98%, respectively, in complex environments. These results represent improvements of 7.11%, 11.29%, 8.51%, and 9.48% over the original YOLOv10 network. Therefore, MF-YOLOv10 exhibits superior detection accuracy and real-time performance in complex working environments, demonstrating significant engineering practicality. Full article

(This article belongs to the Section Intelligent Sensors)

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23 pages, 604 KiB

Open AccessReview

A Comprehensive Review on Stability Analysis of Hybrid Energy System

by Namita Kumari, Binh Tran, Ankush Sharma and Damminda Alahakoon

Sensors 2025, 25(10), 2974; https://doi.org/10.3390/s25102974 - 8 May 2025

Abstract

Hybrid Energy Systems (HES) are pivotal in modern power systems. They incorporate conventional and renewable energy sources, energy storage, and main grids to deliver reliable and sustainable power. To ensure the smooth functioning of such systems, stability analysis is essential, particularly in dynamic [...] Read more.

Hybrid Energy Systems (HES) are pivotal in modern power systems. They incorporate conventional and renewable energy sources, energy storage, and main grids to deliver reliable and sustainable power. To ensure the smooth functioning of such systems, stability analysis is essential, particularly in dynamic and unpredictable situations. Despite tremendous progress, the stability analysis of HES is still complex due to challenges such as nonlinearity, system complexity, and uncertainty in renewable energy generation. A thorough understanding of stability analysis for HES is crucial to ensure the reliable and efficient design of these complex power systems. Particularly in the current data-intensive era, vast volumes of data are being collected through advanced sensors and communication technologies. However, no thorough and organised discussion of every facet of HES stability analysis is available in the literature. This paper aims to review various types and techniques for analysing frequency, transient, small-signal, and converter-driven stability, and to assess the importance and challenges of such analyses for HES. By emphasising the need for innovative approaches for stability enhancement, the paper also discusses the importance of continued research in optimising the operation and reliability of hybrid energy systems. Full article

(This article belongs to the Special Issue Sensors, Systems and Methods for Power Quality Measurements)

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18 pages, 1276 KiB

Open AccessArticle

Cross-PLC: An I3oT Cross Platform to Manage Communications for Applications in Real Factories

by Antonio Lacasa, Javier Llopis, Nicolás Montés, Ivan Peinado-Asensi and Eduardo Garcia

Sensors 2025, 25(10), 2973; https://doi.org/10.3390/s25102973 - 8 May 2025

Abstract

Recently, a new concept has emerged for the development of Industrial Internet of Things (IIoT) applications, the Industrializable Industrial Internet of Things (I3oT). As a criterion for the design of industrial applications, the I3oT imposes the exclusive use of pre-installed elements in the [...] Read more.

Recently, a new concept has emerged for the development of Industrial Internet of Things (IIoT) applications, the Industrializable Industrial Internet of Things (I3oT). As a criterion for the design of industrial applications, the I3oT imposes the exclusive use of pre-installed elements in the company such as PLCs, sensors, IT/OT networks, etc., trying to minimize the impact on the factories and guaranteeing a cheap and assumable scalability for companies, something that cannot be implemented with the vast majority of IIoT applications available in the market. In our previous work, we have used I3oT applications for predictive maintenance on different components: cylinders, presses, welding clamps and also energy-saving tools, detection of bottlenecks and sub-bottlenecks, etc., all of them generalized for the entire factory. However, the main drawback comes from the flow of data through the IT/OT network. This article presents the Cross-PLC, a tool to allow massive data extraction using the company’s IT/OT network by communicating with any type of PLC or brand existing in the market. The Cross-PLC performs passive listening, and through different communication criteria, the Cross-PLC becomes a virtual PLC containing all the parameters necessary for the I3oT applications developed. This article presents the design of this tool, its implementation and use at Ford Factory in Almussafes (Valencia). Full article

(This article belongs to the Special Issue Architectures, Protocols and Algorithms of Sensor Networks—Second Edition)

21 pages, 13034 KiB

Open AccessReview

Security in Wireless Sensor Networks Using OMNET++: Literature Review

by Maahiya Shaik and Sung Won Kim

Sensors 2025, 25(10), 2972; https://doi.org/10.3390/s25102972 (registering DOI) - 8 May 2025

Abstract

With the essential increase in the use of wireless sensor networks, security is a major concern in every field. Intrusions have become frequent and present a significant challenge in today’s world. It is valuable to explore the feasibility of designing and rigorously assessing [...] Read more.

With the essential increase in the use of wireless sensor networks, security is a major concern in every field. Intrusions have become frequent and present a significant challenge in today’s world. It is valuable to explore the feasibility of designing and rigorously assessing intrusion detection systems within network simulation environments. Wireless sensor network security risk prediction is a key aspect of wireless network security technology. Analyzing the current state of wireless networks, security is a crucial step in ongoing research in the field of network security. In this paper, we discuss how OMNET++ is used for intrusion detection for different types of attacks in wireless sensor networks, what frameworks and protocols are used in OMNET++, and why OMNET++ is used, along with a few security attacks in wireless networks. Full article

(This article belongs to the Section Sensor Networks)

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23 pages, 2583 KiB

Open AccessArticle

Dynamic Multi-Behaviour, Orientation-Invariant Re-Identification of Holstein-Friesian Cattle

by Maarten Perneel, Ines Adriaens, Jan Verwaeren and Ben Aernouts

Sensors 2025, 25(10), 2971; https://doi.org/10.3390/s25102971 - 8 May 2025

Abstract

To perform reliable animal re-identification, most available algorithms require standardised animal poses. However, this lack of versatility prevents widespread application of these algorithms in behavioural research and commercial environments. To circumvent this, we incorporated information about the orientation and behaviour of the animals [...] Read more.

To perform reliable animal re-identification, most available algorithms require standardised animal poses. However, this lack of versatility prevents widespread application of these algorithms in behavioural research and commercial environments. To circumvent this, we incorporated information about the orientation and behaviour of the animals in an embedding-based algorithm to re-identify Holstein-Friesian cattle. After all, the orientation and behaviour of an animal determine which body parts of an animal are visible from the camera’s perspective. We evaluated our approach using a dataset with more than 11,000 instance segments of Holstein-Friesian cattle, but our methodology is readily generalisable to different animal species. Our results show that incorporation of informative metadata parameters in the re-identification procedure increases the rank-1 re-identification accuracy from 0.822 to 0.894, corresponding to a 40% reduction in the number of incorrectly identified animals. Full article

(This article belongs to the Section Smart Agriculture)

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28 pages, 85416 KiB

Open AccessArticle

ENGDM: Enhanced Non-Isotropic Gaussian Diffusion Model for Progressive Image Editing

by Xi Yu, Xiang Gu, Xin Hu and Jian Sun

Sensors 2025, 25(10), 2970; https://doi.org/10.3390/s25102970 - 8 May 2025

Abstract

Diffusion models have made remarkable progress in image generation, leading to advancements in the field of image editing. However, balancing editability with faithfulness remains a significant challenge. Motivated by the fact that more novel content will be generated when larger variance noise is [...] Read more.

Diffusion models have made remarkable progress in image generation, leading to advancements in the field of image editing. However, balancing editability with faithfulness remains a significant challenge. Motivated by the fact that more novel content will be generated when larger variance noise is applied to the image, in this paper, we propose an Enhanced Non-isotropic Gaussian Diffusion Model (ENGDM) for progressive image editing, which introduces independent Gaussian noise with varying variances to each pixel based on its editing needs. To enable efficient inference without retraining, ENGDM is rectified into an isotropic Gaussian diffusion model (IGDM) by assigning different total diffusion times to different pixels. Furthermore, we introduce reinforced text embeddings, using a novel editing reinforcement loss in the latent space to optimize text embeddings for enhanced editability. And we introduce optimized noise variances by employing a structural consistency loss to dynamically adjust the denoising time steps for each pixel for better faithfulness. Experimental results on multiple datasets demonstrate that ENGDM achieves state-of-the-art performance in image-editing tasks, effectively balancing faithfulness to the source image and alignment with the desired editing target. Full article

(This article belongs to the Section Sensing and Imaging)

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32 pages, 7616 KiB

Open AccessArticle

ANCHOR-Grid: Authenticating Smart Grid Digital Twins Using Real-World Anchors

by Mohsen Hatami, Qian Qu, Yu Chen, Javad Mohammadi, Erik Blasch and Erika Ardiles-Cruz

Sensors 2025, 25(10), 2969; https://doi.org/10.3390/s25102969 - 8 May 2025

Abstract

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ [...] Read more.

Integrating digital twins (DTs) into smart grid systems within the Internet of Smart Grid Things (IoSGT) ecosystem brings novel opportunities but also security challenges. Specifically, advanced machine learning (ML)-based Deepfake technologies enable adversaries to create highly realistic yet fraudulent DTs, threatening critical infrastructures’ reliability, safety, and integrity. In this paper, we introduce Authenticating Networked Computerized Handling of Representations for Smart Grid security (ANCHOR-Grid), an innovative authentication framework that leverages Electric Network Frequency (ENF) signals as real-world anchors to secure smart grid DTs at the frontier against Deepfake attacks. By capturing distinctive ENF variations from physical grid components and embedding these environmental fingerprints into their digital counterparts, ANCHOR-Grid provides a robust mechanism to ensure the authenticity and trustworthiness of virtual representations. We conducted comprehensive simulations and experiments within a virtual smart grid environment to evaluate ANCHOR-Grid. We crafted both authentic and Deepfake DTs of grid components, with the latter attempting to mimic legitimate behavior but lacking correct ENF signatures. Our results show that ANCHOR-Grid effectively differentiates between authentic and fraudulent DTs, demonstrating its potential as a reliable security layer for smart grid systems operating in the IoSGT ecosystem. In our virtual smart grid simulations, ANCHOR-Grid achieved a detection rate of 99.8% with only 0.2% false positives for Deepfake DTs at a sparse attack rate (1 forged packet per 500 legitimate packets). At a higher attack frequency (1 forged packet per 50 legitimate packets), it maintained a robust 97.5% detection rate with 1.5% false positives. Against replay attacks, it detected 94% of 5 s-old signatures and 98.5% of 120 s-old signatures. Even with 5% injected noise, detection remained at 96.5% (dropping to 88% at 20% noise), and under network latencies from <5 ms to 200 ms, accuracy ranged from 99.9% down to 95%. These results demonstrate ANCHOR-Grid’s high reliability and practical viability for securing smart grid DTs. These findings highlight the importance of integrating real-world environmental data into authentication processes for critical infrastructure and lay the foundation for future research on leveraging physical world cues to secure digital ecosystems. Full article

(This article belongs to the Special Issue Security, Cryptography and Privacy-Preserving Computation Architectures for Wireless Sensors Networks and Communications)

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18 pages, 3491 KiB

Open AccessArticle

RT-4M: Real-Time Mosaicing Manager for Manual Microscopy System

by Nobuhito Mori, Yoshihiro Miyazaki, Tatsuya Oda and Yasuyuki S. Kida

Sensors 2025, 25(10), 2968; https://doi.org/10.3390/s25102968 - 8 May 2025

Abstract

The creation of virtual slides, i.e., high-resolution digital images of biological samples, is expensive, and existing manual methods often suffer from stitching errors and additional reimaging costs. To address these issues, we propose a real-time mosaicing manager for manual microscopy (RT-4M) that performs [...] Read more.

The creation of virtual slides, i.e., high-resolution digital images of biological samples, is expensive, and existing manual methods often suffer from stitching errors and additional reimaging costs. To address these issues, we propose a real-time mosaicing manager for manual microscopy (RT-4M) that performs real-time stitching and allows users to preview slides during imaging using existing manual microscopy systems, thereby reducing the need for reimaging. We install it on two different microscopy systems, successfully creating virtual slides of hematoxylin and eosin- and fluorescent-stained tissues obtained from humans and mice. The fluorescent-stained tissues consist of two colors, requiring the manual switching of the filter and an exposure time of 1.6 s per color. Even in the case of the largest dataset in this study (over 900 images), the entire sample is captured without any omissions. Moreover, RT-4M exhibits a processing time of less than one second per registration, indicating that it does not hinder the user’s imaging workflow. Additionally, the composition process reduces the misalignment rate by a factor of 20 compared to existing software. We believe that the proposed software will prove useful in the fields of pathology and bio-research, particularly for facilities with relatively limited budgets. Full article

(This article belongs to the Special Issue Applications of Biomedical Imaging and Sensing Technologies in Disease Diagnosis)

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18 pages, 10917 KiB

Open AccessArticle

A Novel Parallel Multi-Scale Attention Residual Network for the Fault Diagnosis of a Train Transmission System

by Yong Chang, Tengfei Gao, Juanhua Yang, Zongyao Liu and Biao Wang

Sensors 2025, 25(10), 2967; https://doi.org/10.3390/s25102967 - 8 May 2025

Abstract

The data-driven intelligent fault diagnosis method has shown great potential in improving the safety and reliability of train operation. However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model [...] Read more.

The data-driven intelligent fault diagnosis method has shown great potential in improving the safety and reliability of train operation. However, the noise interference and multi-scale signal characteristics generated by the train transmission system under non-stationary conditions make it difficult for the network model to effectively learn fault features, resulting in a decrease in the accuracy and robustness of the network. This results in the requirements of train fault diagnosis tasks not being met. Therefore, a novel parallel multi-scale attention residual neural network (PMA-ResNet) for a train transmission system is proposed in this paper. Firstly, multi-scale learning modules (MLMods) with different structures and convolutional kernel sizes are designed by combining a residual neural network (ResNet) and an Inception network, which can automatically learn multi-scale fault information from vibration signals. Secondly, a parallel network structure is constructed to improve the generalization ability of the proposed network model for the entire train transmission system. Finally, by using a self-attention mechanism to assign different weight values to the relative importance of different feature information, the learned fault features are further integrated and enhanced. In the experimental section, a train transmission system fault simulation platform is constructed, and experiments are carried out on train transmission systems with different faults under non-stationary conditions to verify the effectiveness of the proposed network. The experimental results and comparisons with five state-of-the-art methods demonstrate that the proposed PMA-ResNet can diagnose 19 different faults with greater accuracy. Full article

(This article belongs to the Section Fault Diagnosis & Sensors)

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42 pages, 3314 KiB

Open AccessSystematic Review

A Systematic Review of Sensor-Based Methods for Measurement of Eating Behavior

by Delwar Hossain, J. Graham Thomas, Megan A. McCrory, Janine Higgins and Edward Sazonov

Sensors 2025, 25(10), 2966; https://doi.org/10.3390/s25102966 - 8 May 2025

Abstract

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The [...] Read more.

The dynamic process of eating—including chewing, biting, swallowing, food items, eating time and rate, mass, environment, and other metrics—may characterize behavioral aspects of eating. This article presents a systematic review of the use of sensor technology to measure and monitor eating behavior. The PRISMA 2020 guidelines were followed to review the full texts of 161 scientific manuscripts. The contributions of this review article are twofold: (i) A taxonomy of sensors for quantifying various aspects of eating behavior is established, classifying the types of sensors used (such as acoustic, motion, strain, distance, physiological, cameras, and others). (ii) The accuracy of measurement devices and methods is assessed. The review highlights the advantages and limitations of methods that measure and monitor different eating metrics using a combination of sensor modalities and machine learning algorithms. Furthermore, it emphasizes the importance of testing these methods outside of restricted laboratory conditions, and it highlights the necessity of further research to develop privacy-preserving approaches, such as filtering out non-food-related sounds or images, to ensure user confidentiality and comfort. The review concludes with a discussion of challenges and future trends in the use of sensors for monitoring eating behavior. Full article

(This article belongs to the Special Issue Smart Sensing for Dietary Monitoring)

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20 pages, 2857 KiB

Open AccessArticle

NeuroSafeDrive: An Intelligent System Using fNIRS for Driver Distraction Recognition

by Ghazal Bargshady, Hakki Gokalp Ustun, Yasaman Baradaran, Houshyar Asadi, Ravinesh C Deo, Jeroen Van Boxtel and Raul Fernandez Rojas

Sensors 2025, 25(10), 2965; https://doi.org/10.3390/s25102965 - 8 May 2025

Abstract

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike [...] Read more.

Driver distraction remains a critical factor in road accidents, necessitating intelligent systems for real-time detection. This study introduces a novel fNIRS-based method to to classify varying levels of driver distraction across diverse simulated scenarios, including cognitive, visual–manual, and auditory sources of inattention. Unlike previous work, we evaluated multiple neurophysiological metrics—including oxygenated, deoxygenated, and combined haemoglobin—to identify the most reliable biomarker for distraction detection. Neurophysiological data were collected, and three multi-class classifiers (SVM, KNN, decision tree) were applied across different fNIRS metrics. Our results show that oxygenated haemoglobin outperforms other signals in distinguishing distracted from non-distracted states, while the combined signal performs best in differentiating distraction from baseline. The proposed SVM model achieved ≈ 77.9% accuracy in detecting distracted and relaxed driving states based on brain oxygen levels. Our findings also show that increased distraction correlates with elevated activity in the dorsolateral prefrontal cortex and premotor cortex, whereas driving without distraction exhibits lower neurovascular engagement. This study contributes to affective computing and intelligent transportation systems and could support the development of future driver distraction monitoring systems for safer and more adaptive vehicle control. Full article

(This article belongs to the Special Issue Sensor Technologies and Intelligent Computing for Biometric Signal Analysis and Pattern Recognition)

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10 pages, 373 KiB

Open AccessArticle

Detection of Falls and Frailty in Older Adults with Oldfry: Associated Risk Factors

by Eva Martí-Marco, Enrique J. Vera-Remartínez, Aurora Esteve-Clavero, Irene Carmona-Fortuño, Martín Flores-Saldaña, Jorge Vila-Pascual, Malena Barba-Muñoz and María Pilar Molés-Julio

Sensors 2025, 25(10), 2964; https://doi.org/10.3390/s25102964 - 8 May 2025

Abstract

Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja [...] Read more.

Objective: To describe the characteristics and outcomes of using the Oldfry technology application in older adults, analyzing changes in frailty and fall risk after its implementation. Design and Methods: Observational, analytical, prospective, cross-sectional, and multicenter study conducted in residential centers in Plana Baja (Castellón, Spain). A total of 156 older adults over 65 years old participated, selected based on specific criteria and voluntary consent. Sociodemographic, anthropometric, and clinical variables were collected, including fall history, sensory problems, medication use, and standardized cognitive, nutritional, and functional assessment scales. The study was approved by the Ethics Committee of Universitat Jaume I. Results: The sample included 156 individuals (median age: 84 years). Women showed greater functional dependence (Barthel scale) and cognitive impairment (Pfeiffer scale). The Oldfry device detected frailty with statistically significant differences. A direct relationship was found between greater functional dependence and higher fall risk, as well as between higher comorbidity and increased fall risk. An adequate nutritional status was associated with a lower fall risk. Conclusion: The use of Oldfry is crucial for assessing frailty and fall risk in older adults. Factors such as functionality, comorbidities, and nutritional status directly influence fall prevention, highlighting the importance of technological tools in monitoring these risks. Full article

(This article belongs to the Special Issue Fall Detection Based on Wearable Sensors)

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