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Light-Activated Graphene-Based Microsensor for Low-Power Gas Detection

Journal Description

Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors. Sensors is published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB) and Chinese Society of Micro-Nano Technology (CSMNT) and more are affiliated with Sensors and their members receive a discount on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Chemistry, Analytical) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.6 days after submission; acceptance to publication is undertaken in 2.4 days (median values for papers published in this journal in the second half of 2024).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, JCP and Targets.

Impact Factor: 3.4 (2023); 5-Year Impact Factor: 3.7 (2023)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

11 pages, 4717 KiB

Open AccessArticle

The Effects of Decreasing Foot Strike Angle on Lower Extremity Shock Attenuation Measured with Wearable Sensors

by Lucas Sarantos, David J. Zeppetelli, Cole A. Dempsey, Takashi Nagai and Caleb D. Johnson

Sensors 2025, 25(9), 2656; https://doi.org/10.3390/s25092656 (registering DOI) - 23 Apr 2025

Shock attenuation may be a clinically feasible method to assess changes in lower extremity joint loading induced by gait modifications, such as decreasing foot strike angle (forefoot striking). The purpose of this study was to identify changes in lower extremity shock attenuation between [...] Read more.

Shock attenuation may be a clinically feasible method to assess changes in lower extremity joint loading induced by gait modifications, such as decreasing foot strike angle (forefoot striking). The purpose of this study was to identify changes in lower extremity shock attenuation between habitual and forefoot strike gait conditions. Eighteen participants ran on a treadmill with their habitual gait and an instructed forefoot strike gait. Shock attenuation was measured with inertial measurement units as the ratio of proximal to distal peak resultant/vertical accelerations, with three sensor combinations: ankle to below/above knee (BK/A; AK/A) and AK/BK. Three participants were excluded who were habitual forefoot strikers or failed to decrease their foot strike angle by at least 5° in the forefoot strike condition. The results showed significantly greater resultant shock attenuation in the forefoot strike compared to the habitual condition for BK/A (mean Δ = 0.13, p = 0.004) and AK/A (mean Δ = 0.23, p = 0.007). No significant differences were found for AK/BK or vertical shock attenuation. These results suggest that shock attenuation may not reflect joint-specific loading changes that have been shown for forefoot striking (i.e., increased ankle/shank and decreased knee moments). However, it may capture changes in overall lower extremity loading (i.e., decreased vertical ground reaction forces). Full article

(This article belongs to the Special Issue Sports Sensors for Athlete Motion Tracking and Physiological Monitoring: Second Edition)

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18 pages, 8555 KiB

Open AccessArticle

AgNPs@CeO₂/Nafion Nanocomposite-Modified Electrode for the Sensitive Detection of Trace Lead (II) in Water Samples

by Zhengying Guo, Peng Xu, Shiqing Zhou and Ruoxi Wu

Sensors 2025, 25(9), 2655; https://doi.org/10.3390/s25092655 (registering DOI) - 23 Apr 2025

Excessive levels of heavy metal pollutants in the environment pose significant threats to human health and ecosystem stability. Consequently, the accurate and rapid detection of heavy metal ions is critically important. A AgNPs@CeO₂/Nafion composite was prepared by dispersing nano-ceria (CeO₂ [...] Read more.

Excessive levels of heavy metal pollutants in the environment pose significant threats to human health and ecosystem stability. Consequently, the accurate and rapid detection of heavy metal ions is critically important. A AgNPs@CeO₂/Nafion composite was prepared by dispersing nano-ceria (CeO₂) in a Nafion solution and incorporating silver nanoparticles (AgNPs). The morphology, microstructure, and electrochemical properties of the modified electrode materials were systematically characterized using scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), and cyclic voltammetry (CV). By leveraging the oxygen vacancies and high electron transfer efficiency of CeO₂, the strong adsorption capacity of Nafion, and the superior conductivity of AgNPs, an AgNPs@CeO₂/Nafion/GCE electrochemical sensor was developed. Under optimized conditions, trace Pb²⁺ in water was detected using square wave anodic stripping voltammetry (SWASV). The sensor demonstrated a linear response for Pb²⁺ within the concentration range of 1–100 μg·L⁻¹, with a detection limit of 0.17 μg·L⁻¹ (S/N = 3). When applied to real water samples, the method achieved recovery rates between 93.7% and 110.3%, validating its reliability and practical applicability. Full article

(This article belongs to the Section Electronic Sensors)

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23 pages, 42651 KiB

Open AccessArticle

Research on High-Precision Motion Planning of Large Multi-Arm Rock Drilling Robot Based on Multi-Strategy Sampling Rapidly Exploring Random Tree*

by Qiaoyu Xu and Yansong Lin

Sensors 2025, 25(9), 2654; https://doi.org/10.3390/s25092654 - 22 Apr 2025

In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupled with a forward kinematics error compensation model, is introduced [...] Read more.

In addressing the optimal motion planning issue for multi-arm rock drilling robots, this paper introduces a high-precision motion planning method based on Multi-Strategy Sampling RRT* (MSS-RRT*). A dual Jacobi iterative inverse solution method, coupled with a forward kinematics error compensation model, is introduced to dynamically correct target positions, improving end-effector positioning accuracy. A multi-strategy sampling mechanism is constructed by integrating DRL position sphere sampling, spatial random sampling, and goal-oriented sampling. This mechanism flexibly applies three sampling methods at different stages of path planning, significantly improving the adaptability and search efficiency of the RRT* algorithm. In particular, DRL position sphere sampling is prioritized during the initial phase, effectively reducing the number of invalid sampling points. For training a three-arm DRL model with the twin delayed deep deterministic policy gradient algorithm (TD3), the Hindsight Experience Replay-Obstacle Arm Transfer (HER-OAT) method is used for data replay. The cylindrical bounding box method effectively prevents collisions between arms. The experimental results show that the proposed method improves motion planning accuracy by 94.15% compared to a single Jacobi iteration. MSS-RRT* can plan a superior path in a shorter duration, with the planning time under optimal path conditions being only 20.71% of that required by Informed-RRT*, and with the path length reduced by 21.58% compared to Quick-RRT* under the same time constraints. Full article

(This article belongs to the Section Sensors and Robotics)

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25 pages, 16108 KiB

Open AccessArticle

Hierarchical Motion Field Alignment for Robust Optical Flow Estimation

by Dianbo Ma, Kousuke Imamura, Ziyan Gao, Xiangjie Wang and Satoshi Yamane

Sensors 2025, 25(9), 2653; https://doi.org/10.3390/s25092653 (registering DOI) - 22 Apr 2025

Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a [...] Read more.

Optical flow estimation is a fundamental and long-standing task in computer vision, facilitating the understanding of motion within visual scenes. In this study, we aim to improve optical flow estimation, particularly in challenging scenarios involving small and fast-moving objects. Specifically, we proposed a learning-based model incorporating two key components: the Hierarchical Motion Field Alignment module, which ensures accurate estimation of objects of varying sizes while maintaining manageable computational complexity, and the Correlation Self-Attention module, which effectively handles large displacements, making the model suitable for scenarios with fast-moving objects. Additionally, we introduced a Multi-Scale Correlation Search layer to enhance the four-dimensional cost volume, enabling the model to address various types of motion. Experimental results demonstrate that our model achieves superior generalization performance and significantly improves the estimation of small, fast-moving objects. Full article

(This article belongs to the Section Sensing and Imaging)

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20 pages, 8095 KiB

Open AccessArticle

Low-Cost Hyperspectral Imaging in Macroalgae Monitoring

by Marc C. Allentoft-Larsen, Joaquim Santos, Mihailo Azhar, Henrik C. Pedersen, Michael L. Jakobsen, Paul M. Petersen, Christian Pedersen and Hans H. Jakobsen

Sensors 2025, 25(9), 2652; https://doi.org/10.3390/s25092652 - 22 Apr 2025

This study presents an approach to macroalgae monitoring using a cost-effective hyperspectral imaging (HSI) system and artificial intelligence (AI). Kelp beds are vital habitats and support nutrient cycling, making ongoing monitoring crucial amid environmental changes. HSI emerges as a powerful tool in this [...] Read more.

This study presents an approach to macroalgae monitoring using a cost-effective hyperspectral imaging (HSI) system and artificial intelligence (AI). Kelp beds are vital habitats and support nutrient cycling, making ongoing monitoring crucial amid environmental changes. HSI emerges as a powerful tool in this context, due to its ability to detect pigment-characteristic fingerprints that are often missed altogether by standard RGB cameras. Still, the high costs of these systems are a barrier to large-scale deployment for in situ monitoring. Here, we showcase the development of a cost-effective HSI setup that combines a GoPro camera with a continuous linear variable spectral bandpass filter. We empirically validate the operational capabilities through the analysis of two brown macroalgae, Fucus serratus and Fucus versiculosus, and two red macroalgae, Ceramium sp. and Vertebrata byssoides, in a controlled aquatic environment. Our HSI system successfully captured spectral information from the target species, which exhibit considerable similarity in morphology and spectral profile, making them difficult to differentiate using traditional RGB imaging. Using a one-dimensional convolutional neural network, we reached a high average classification precision, recall, and F1-score of 99.9%, 89.5%, and 94.4%, respectively, demonstrating the effectiveness of our custom low-cost HSI setup. This work paves the way to achieving large-scale and automated ecological monitoring. Full article

(This article belongs to the Section Remote Sensors)

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14 pages, 1173 KiB

Open AccessArticle

A Uniform Funnel Array for DOA Estimation in FANET Using Fibonacci Sampling

by Siwei Huo, Ming Zhang, Yongxi Liu and Shitao Zhu

Sensors 2025, 25(9), 2651; https://doi.org/10.3390/s25092651 - 22 Apr 2025

The Flying Ad-Hoc Network (FANET) is an important component of the 6G communication system. In order to achieve the precise positioning of unmanned aerial vehicle (UAV) nodes in a FANET when satellite navigation signals are unavailable, simple and accurate direction-of-arrival (DOA) estimation methods [...] Read more.

The Flying Ad-Hoc Network (FANET) is an important component of the 6G communication system. In order to achieve the precise positioning of unmanned aerial vehicle (UAV) nodes in a FANET when satellite navigation signals are unavailable, simple and accurate direction-of-arrival (DOA) estimation methods are required. In this paper, we propose an improved correlative interferometer method to estimate the DOAs of the UAVs in a FANET. This method adopts a uniform funnel array (UFA) configuration, which consists of a uniform circular array (UCA) and an additional element located above the center. This configuration improves the estimation accuracy for DOAs with large polar angles because it utilizes the degree of freedom in the vertical aperture. In addition, the Fibonacci sampling strategy is employed to overcome the polar clustering phenomenon exhibited by latitude–longitude sampling. Furthermore, in the interferometer, only partial phase differences are used to reduce the storage burden. When calculating the similarity function, we adopt the triangular function instead of the cosine function to improve computational efficiency. The simulation results show that the proposed UFA improves the DOA estimation accuracy by

65.56 %

over the planar UCA for signals with large polar angles. Moreover, Fibonacci sampling improves the DOA estimation accuracy by

11.54 %

as compared to the latitude–longitude sampling. Full article

(This article belongs to the Special Issue Intelligent Sensor-Integrated Antenna Arrays for 5G and 6G Applications)

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18 pages, 1657 KiB

Open AccessArticle

PatchCTG: A Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring

by M. Jaleed Khan, Manu Vatish and Gabriel Davis Jones

Sensors 2025, 25(9), 2650; https://doi.org/10.3390/s25092650 - 22 Apr 2025

Antepartum Cardiotocography (CTG) is a biomedical sensing technology widely used for fetal health monitoring. While the visual interpretation of CTG traces is highly subjective, with the inter-observer agreement as low as 29% and a false positive rate of approximately 60%, the Dawes–Redman system [...] Read more.

Antepartum Cardiotocography (CTG) is a biomedical sensing technology widely used for fetal health monitoring. While the visual interpretation of CTG traces is highly subjective, with the inter-observer agreement as low as 29% and a false positive rate of approximately 60%, the Dawes–Redman system provides an automated approach to fetal well-being assessments. However, it is primarily designed to rule out adverse outcomes rather than detect them, resulting in a high specificity (90.7%) but low sensitivity (18.2%) in identifying fetal distress. This paper introduces PatchCTG, an AI-enabled biomedical time series transformer for CTG analysis. It employs patch-based tokenisation, instance normalisation, and channel-independent processing to capture essential local and global temporal dependencies within CTG signals. PatchCTG was evaluated on the Oxford Maternity (OXMAT) dataset, which comprises over 20,000 high-quality CTG traces from diverse clinical outcomes, after applying the inclusion and exclusion criteria. With extensive hyperparameter optimisation, PatchCTG achieved an AUC of 0.77, with a specificity of 88% and sensitivity of 57% at Youden’s index threshold, demonstrating its adaptability to various clinical needs. Its robust performance across varying temporal thresholds highlights its potential for both real-time and retrospective analysis in sensor-driven fetal monitoring. Testing across varying temporal thresholds showcased it robust predictive performance, particularly with finetuning on data closer to delivery, achieving a sensitivity of 52% and specificity of 88% for near-delivery cases. These findings suggest the potential of PatchCTG to enhance clinical decision-making in antepartum care by providing a sensor-based, AI-driven, objective tool for reliable fetal health assessment. Full article

(This article belongs to the Section Sensing and Imaging)

18 pages, 4969 KiB

Open AccessArticle

Temporal Decay Loss for Adaptive Log Anomaly Detection in Cloud Environments

by Lelisa Adeba Jilcha, Deuk-Hun Kim and Jin Kwak

Sensors 2025, 25(9), 2649; https://doi.org/10.3390/s25092649 (registering DOI) - 22 Apr 2025

Log anomaly detection in cloud computing environments is essential for maintaining system reliability and security. While sequence modeling architectures such as LSTMs and Transformers have been widely employed to capture temporal dependencies in log messages, their effectiveness deteriorates in zero-shot transfer scenarios due [...] Read more.

Log anomaly detection in cloud computing environments is essential for maintaining system reliability and security. While sequence modeling architectures such as LSTMs and Transformers have been widely employed to capture temporal dependencies in log messages, their effectiveness deteriorates in zero-shot transfer scenarios due to distributional shifts in log structures, terminology, and event frequencies, as well as minimal token overlap across datasets. To address these challenges, we propose an effective detection approach integrating a domain-specific pre-trained language model (PLM) fine-tuned on cybersecurity-adjacent data with a novel loss function, Loss with Decaying Factor (LDF). LDF introduces an exponential time decay mechanism into the training objective, ensuring a dynamic balance between historical context and real-time relevance. Unlike traditional sequence models that often overemphasize outdated information and impose high computational overhead, LDF constrains the training process by dynamically weighing log messages based on their temporal proximity, thereby aligning with the rapidly evolving nature of cloud computing environments. Additionally, the domain-specific PLM mitigates semantic discrepancies by improving the representation of log data across heterogeneous datasets. Extensive empirical evaluations on two supercomputing log datasets demonstrate that this approach substantially enhances cross-dataset anomaly detection performance. The main contributions of this study include: (1) the introduction of a Loss with Decaying Factor (LDF) to dynamically balance historical context with real-time relevance; and (2) the integration of a domain-specific PLM for enhancing generalization in zero-shot log anomaly detection across heterogeneous cloud environments. Full article

(This article belongs to the Special Issue Sensor-Based Computational Intelligence and Security Technologies, and Their Applications)

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19 pages, 1013 KiB

Open AccessArticle

Parameter Identification of Permanent Magnet Synchronous Motor Based on LSOSMO Algorithm

by Songcan Zhang, Zhuangzhuang Zhou, Yi Pu, Yan Li and Yingxi Xu

Sensors 2025, 25(9), 2648; https://doi.org/10.3390/s25092648 - 22 Apr 2025

The exact identification of the parameters of Permanent Magnet Synchronous Motors (PMSMs) is extremely significant to reach servo system’s excellent performance control. So as to solve the problems of slow PMSM parameter identification using the spider monkey algorithm, and easily falling into local [...] Read more.

The exact identification of the parameters of Permanent Magnet Synchronous Motors (PMSMs) is extremely significant to reach servo system’s excellent performance control. So as to solve the problems of slow PMSM parameter identification using the spider monkey algorithm, and easily falling into local optimal and having unstable identification results; the LSOSMO algorithm is put forward in this article, which combines logistic–sine chaotic mapping strategy, dynamic probability adaptive t-distribution method, and an opposition-based learning strategy to determine PMSMs’ electric parameters (stator resistance R_s, dq-axis inductance L_d, L_q, and flux linkage

ψ_{f}

). First, the logistic sinusoidal chaotic mapping strategy was used to enhance the uniformity of the initial population of the spider monkey optimization (SMO) algorithm. Then, in the local leader stage and the local leader decision stage of the SMO, the dynamic probability adaptive T-distribution method and opposition-based learning strategy are used to replace the greedy selection strategy, increase the position disturbance, and balance the global search and local search ability of the algorithm, so as to improve the performance and convergence speed of the algorithm. The simulation results prove that, compared to the other five algorithms’ identification results, the four parameters that are identified by the LSOSMO algorithm exhibit higher stability and accuracy, with errors that are relative to the true values remaining below 1.1%. The effectiveness and reliability of the identification algorithm is further verified by this. Full article

(This article belongs to the Section Electronic Sensors)

17 pages, 3355 KiB

Open AccessArticle

Model Design and Study of a U-Channel Photonic Crystal Fib Optic Sensor for Measuring Glucose Concentration in Blood

by Lei Zhao, Hua Yang, Tangyou Sun, Qianju Song, Zao Yi and Yougen Yi

Sensors 2025, 25(9), 2647; https://doi.org/10.3390/s25092647 - 22 Apr 2025

This research introduces a biosensor utilizing surface plasmon resonance in a photonic crystal fiber (PCF) configuration. PCF uses fused silica as the base material, with a layer of gold placed over the U-channels in the cross-section of the fiber to create a surface [...] Read more.

This research introduces a biosensor utilizing surface plasmon resonance in a photonic crystal fiber (PCF) configuration. PCF uses fused silica as the base material, with a layer of gold placed over the U-channels in the cross-section of the fiber to create a surface plasmon resonance. There are three different sizes of internal fiber optic air hole diameters, with a larger channel circle below the u-channel for the formation of an energy leakage window. COMSOL software 6.0 assisted us in tuning the fiber optic structure and performance for the study, and the structural parameters analyzed mainly include the channel circle diameter, the channel circle spacing, the profundity measurement of the polished layer, and the nanoscale size variation of metal films. The results of the simulation study show that the optical fiber sensor achieves refractive index (RI) responsiveness across the 1.30 to 1.41 range, and in the RI interval of 1.40 to 1.41, the sensor exhibits the largest resonance peak shift, and its highest sensitivity reaches 10,200 nm/RIU, and the smallest full width at half peak (FWHM) corresponds to the RI of 1.34 with a value of 4.8 nm, and the highest figure of merit (FOM) corresponds to the RI of 1.34 with a value of 895.83 (1/RIU). COMSOL 6.0 simulation software, was used to simulate the changes in blood refractive index corresponding to different glucose concentrations, and the detection performance of the sensor for different concentrations of glucose was tested. Then, the results show that the glucose concentration in 75 mg/dL–175 mg/dL with RI detection sensitivity is 3750 nm/RIU, where the maximum refractive index sensitivity is 5455 nm/RIU. It shows that the sensor can be applied in the field of biomedical applications, with its convenience, fast response, and high sensitivity, it has great potential and development prospect in the market. Full article

(This article belongs to the Special Issue Surface Plasmon Resonance Sensors for Biomedical Detection Application)

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24 pages, 2629 KiB

Open AccessArticle

Robust Infrared–Visible Fusion Imaging with Decoupled Semantic Segmentation Network

by Xuhui Zhang, Yunpeng Yin, Zhuowei Wang, Heng Wu, Lianglun Cheng, Aimin Yang and Genping Zhao

Sensors 2025, 25(9), 2646; https://doi.org/10.3390/s25092646 (registering DOI) - 22 Apr 2025

The fusion of infrared and visible images provides complementary information from both modalities and has been widely used in surveillance, military, and other fields. However, most of the available fusion methods have only been evaluated with subjective metrics of visual quality of the [...] Read more.

The fusion of infrared and visible images provides complementary information from both modalities and has been widely used in surveillance, military, and other fields. However, most of the available fusion methods have only been evaluated with subjective metrics of visual quality of the fused images, which are often independent of the following relevant high-level visual tasks. Moreover, as a useful technique especially used in low-light scenarios, the effect of low-light conditions on the fusion result has not been well-addressed yet. To address these challenges, a decoupled and semantic segmentation-driven infrared and visible image fusion network is proposed in this paper, which connects both image fusion and the downstream task to drive the network to be optimized. Firstly, a cross-modality transformer fusion module is designed to learn rich hierarchical feature representations. Secondly, a semantic-driven fusion module is developed to enhance the key features of prominent targets. Thirdly, a weighted fusion strategy is adopted to automatically adjust the fusion weights of different modality features. This effectively merges the thermal characteristics from infrared images and detailed information from visible images. Additionally, we design a refined loss function that employs the decoupling network to constrain the pixel distributions in the fused images and produce more-natural fusion images. To evaluate the robustness and generalization of the proposed method in practical challenge applications, a Maritime Infrared and Visible (MIV) dataset is created and verified for maritime environmental perception, which will be made available soon. The experimental results from both widely used public datasets and the practically collected MIV dataset highlight the notable strengths of the proposed method with the best-ranking quality metrics among its counterparts. Of more importance, the fusion image achieved with the proposed method has over 96% target detection accuracy and a dominant high mAP@[50:95] value that far surpasses all the competitors. Full article

(This article belongs to the Special Issue Deep Learning for Information Fusion and Pattern Recognition—Second Edition)

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14 pages, 9227 KiB

Open AccessArticle

In-Motion Alignment with MEMS-IMU Using Multilocal Linearization Detection

by Yulu Zhong, Xiyuan Chen, Ning Gao and Zhiyuan Jiao

Sensors 2025, 25(9), 2645; https://doi.org/10.3390/s25092645 - 22 Apr 2025

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to [...] Read more.

In-motion alignment is a critical step in obtaining the initial state of an integrated navigation system. This article considers the in-motion initial alignment problem using the multilocal linearization detection method. In contrast to the OBA-based method, which fully relies on satellite signals to estimate the initial state of the Kalman filter, the proposed method utilizes the designed quasi-uniform quaternion generation method to estimate several possible initial states. Then, the proposed method selects the most probable result based on the generalized Schweppe likelihood ratios among multiple hypotheses. The experiment result of the proposed method demonstrates the advantage of estimation performance within poor-quality measurement conditions for the long-duration coarse alignment using MEMS-IMU compared with the OBA-based method. The proposed method has potential applications in alignment tasks for low-cost, small-scale vehicle navigation systems. Full article

(This article belongs to the Special Issue INS/GNSS Integrated Navigation Systems)

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13 pages, 2864 KiB

Open AccessArticle

Performance of Continuous Digital Monitoring of Vital Signs with a Wearable Sensor in Acute Hospital Settings

by Meera Joshi, Fahad M. Iqbal, Mansour Sharabiani, Hutan Ashrafian, Sonal Arora, Kenny McAndrew, Sadia Khan, Graham Cooke and Ara Darzi

Sensors 2025, 25(9), 2644; https://doi.org/10.3390/s25092644 - 22 Apr 2025

Background: Continuous vital sign monitoring using wearable sensors has gained traction for the early detection of patient deterioration, particularly with the advent of virtual wards. Objective: The objective was to evaluate the reliability of a wearable sensor for monitoring heart rate (HR), respiratory [...] Read more.

Background: Continuous vital sign monitoring using wearable sensors has gained traction for the early detection of patient deterioration, particularly with the advent of virtual wards. Objective: The objective was to evaluate the reliability of a wearable sensor for monitoring heart rate (HR), respiratory rate (RR), and temperature in acutely unwell hospital patients and to identify the optimal time window for alert generation. Methods: A prospective cohort study recruited 500 patients in a single hospital. Sensor readings were compared to standard intermittent nurse observations using Bland–Altman plots to assess the limits of agreement. Results: HR demonstrated good agreement with nurse observations (intraclass correlation coefficient [ICC] = 0.66, r = 0.86, p < 0.001), with a mean difference of 3.63 bpm (95% LoA: −10.87 to 18.14 bpm). RR exhibited weaker agreement (ICC = 0.20, r = 0.18, p < 0.001), with a mean difference of −2.72 breaths per minute (95% LoA: −10.91 to 5.47 bpm). Temperature showed poor to fair agreement (ICC = 0.30, r = 0.39, p < 0.001), with a mean difference of −0.57 °C (95% LoA: −1.72 to 0.58 °C). A 10 min averaging window was identified as optimal, balancing data retention and real-time alerting. Conclusions: Wearable sensors demonstrate potential for reliable continuous monitoring of vital signs, supporting their future integration into real-world clinical practice for improved patient safety. Full article

(This article belongs to the Special Issue Applications and Design of Digital Systems for Sensors and Sensing Technologies)

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2 pages, 1279 KiB

Open AccessCorrection

Correction: Khort et al. Automated Mobile Hot Mist Generator: A Quest for Effectiveness in Fruit Horticulture. Sensors 2022, 22, 3164

by Dmitriy Khort, Alexey Kutyrev, Nikolay Kiktev, Taras Hutsol, Szymon Glowacki, Maciej Kuboń, Tomasz Nurek, Anatolii Rud and Zofia Gródek-Szostak

Sensors 2025, 25(9), 2643; https://doi.org/10.3390/s25092643 - 22 Apr 2025

In the original publication [...] Full article

(This article belongs to the Section Smart Agriculture)

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22 pages, 1077 KiB

Open AccessArticle

SECrackSeg: A High-Accuracy Crack Segmentation Network Based on Proposed UNet with SAM2 S-Adapter and Edge-Aware Attention

by Xiyin Chen, Yonghua Shi and Junjie Pang

Sensors 2025, 25(9), 2642; https://doi.org/10.3390/s25092642 - 22 Apr 2025

Crack segmentation is essential for structural health monitoring and infrastructure maintenance, playing a crucial role in early damage detection and safety risk reduction. Traditional methods, including digital image processing techniques have limitations in complex environments. Deep learning-based methods have shown potential, but still [...] Read more.

Crack segmentation is essential for structural health monitoring and infrastructure maintenance, playing a crucial role in early damage detection and safety risk reduction. Traditional methods, including digital image processing techniques have limitations in complex environments. Deep learning-based methods have shown potential, but still face challenges, such as poor generalization with limited samples, insufficient extraction of fine-grained features, feature loss during upsampling, and inadequate capture of crack edge details. This study proposes SECrackSeg, a high-accuracy crack segmentation network that integrates an improved UNet architecture, Segment Anything Model 2 (SAM2), MI-Upsampling, and an Edge-Aware Attention mechanism. The key innovations include: (1) using a SAM2 S-Adapter with a frozen backbone to enhance generalization in low-data scenarios; (2) employing a Multi-Scale Dilated Convolution (MSDC) module to promote multi-scale feature fusion; (3) introducing MI-Upsampling to reduce feature loss during upsampling; and (4) implementing an Edge-Aware Attention mechanism to improve crack edge segmentation precision. Additionally, a custom loss function incorporating weighted binary cross-entropy and weighted IoU loss is utilized to emphasize challenging pixels. This function also applies Multi-Granularity Supervision by optimizing segmentation outputs at three different resolution levels, ensuring better feature consistency and improved model robustness across varying image scales. Experimental results show that SECrackSeg achieves higher precision, recall, F1-score, and mIoU scores on the CFD, Crack500, and DeepCrack datasets compared to state-of-the-art models, demonstrating its excellent performance in fine-grained feature recognition, edge segmentation, and robustness. Full article

(This article belongs to the Collection Sensors and Sensing Technology for Industry 4.0)

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20 pages, 9426 KiB

Open AccessArticle

Automated Recognition and Measurement of Corrugated Pipes for Precast Box Girder Based on RGB-D Camera and Deep Learning

by Jiongyi Zhu, Zixin Huang, Dejiang Wang, Panpan Liu, Haili Jiang and Xiaoqing Du

Sensors 2025, 25(9), 2641; https://doi.org/10.3390/s25092641 - 22 Apr 2025

The accurate installation position of corrugated pipes is critical for ensuring the quality of prestressed concrete box girders. Given that these pipes can span up to 30 m and are deeply embedded within rebars, manual measurement is both labor-intensive and prone to errors. [...] Read more.

The accurate installation position of corrugated pipes is critical for ensuring the quality of prestressed concrete box girders. Given that these pipes can span up to 30 m and are deeply embedded within rebars, manual measurement is both labor-intensive and prone to errors. Meanwhile, automated recognition and measurement methods are hindered by high equipment costs and accuracy issues caused by rebar occlusion. To balance cost effectiveness and measurement accuracy, this paper proposes a method that utilizes an RGB-D camera and deep learning. Firstly, an optimal registration scheme is selected to generate complete point cloud data of pipes from segmented data captured by an RGB-D camera. Next, semantic segmentation is applied to extract the characteristic features of the pipes. Finally, the center points from cross-sectional slices are extracted and curve-fitting is performed to recognize and measure the pipes. A test was conducted in a simulated precast factory environment to validate the proposed method. The results show that under the optimal fitting scheme (BP neural network with circle fitting constraint), the average measurement errors for the three pipes are 2.2 mm, 1.4 mm, and 1.6 mm, with Maximum Errors of −5.8 mm, −4.2 mm, and −5.7 mm, respectively, meeting the standard requirements. The proposed method can accurately locate the pipes, offering a new technical pathway for the automated recognition and measurement of pipes in prefabricated construction. Full article

(This article belongs to the Section Sensing and Imaging)

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16 pages, 5226 KiB

Open AccessCommunication

Flexible Gas Sensor Based on PANI/WO₃/CuO for Room-Temperature Detection of H₂S

by Dongxiang Zhang, Yingmin Liu, Yang Wang, Zhi Li, Dongkun Xiao, Tianhong Zhou and Mojie Sun

Sensors 2025, 25(9), 2640; https://doi.org/10.3390/s25092640 - 22 Apr 2025

Polyaniline (PANI) is currently one of the most extensively studied conductive polymers in the field of flexible gas sensors. However, sensors based on pure PANI generally suffer from problems such as low sensitivity and poor stability. To address these issues, in this work, [...] Read more.

Polyaniline (PANI) is currently one of the most extensively studied conductive polymers in the field of flexible gas sensors. However, sensors based on pure PANI generally suffer from problems such as low sensitivity and poor stability. To address these issues, in this work, a room-temperature hydrogen sulfide gas sensor of polyaniline/tungsten oxide/copper oxide (PANI/WO₃/CuO) was synthesized using in situ polymerization technology. This gas sensor displays a response value of 31.3% to 1 ppm hydrogen sulfide at room temperature, with a response/recovery time of 353/4958 s and a detection limit of 100 ppb. Such an excellent performance is attributed to the high surface area and large adsorption capacity of the ternary composite, as well as the multi-phase interface synergistic effect. Full article

(This article belongs to the Section Chemical Sensors)

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26 pages, 19598 KiB

Open AccessArticle

Validation of Smartphones in Arbitrary Positions Against Force Plate Standard for Balance Assessment

by German Jack Ellsworth, Stephen M. Klisch, Britta Berg-Johansen and Eric Ocegueda

Sensors 2025, 25(9), 2639; https://doi.org/10.3390/s25092639 - 22 Apr 2025

Balance assessment is a key metric for tracking the health and fall risk of individuals with balance impairment. Leveraging wearable sensors and mobile devices can increase clinical accessibility to objective balance metrics. Previous work has been conducted validating center of mass (COM) acceleration [...] Read more.

Balance assessment is a key metric for tracking the health and fall risk of individuals with balance impairment. Leveraging wearable sensors and mobile devices can increase clinical accessibility to objective balance metrics. Previous work has been conducted validating center of mass (COM) acceleration metrics from mobile devices against the gold standard force plate center of pressure (COP) position; however, most studies have been restricted to devices being placed close to the subject’s COM. In this study, rigid body kinematics and the inverted pendulum model were used to develop a novel methodology for calculating COM acceleration using mobile devices in arbitrary positions, as well as an approach for conversion of COM measurements to COP position for direct validation with force plate measurements. Validation of this methodology included a direct comparison of smartphone and force plate results for COM accelerations and COP positions, as well as statistical comparisons using Spearman’s rank correlation. The results show strong analysis performance for both approaches during a subject’s intentional swaying, with more limited results in cases of little motion. The strong performance warrants future work to further improve accessibility by removing dependence on motion capture systems or replacing them with cost-effective alternatives. The accurate tracking of COM acceleration and COP position information for mobile devices at arbitrary positions increases the flexibility for future mobile or at-home balance assessments. Full article

(This article belongs to the Special Issue Wearable Inertial Sensors for Human Movement Analysis)

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19 pages, 3009 KiB

Open AccessArticle

Occupancy Monitoring Using BLE Beacons: Intelligent Bluetooth Virtual Door System

by Nasrettin Koksal, AbdulRahman Ghannoum, William Melek and Patricia Nieva

Sensors 2025, 25(9), 2638; https://doi.org/10.3390/s25092638 - 22 Apr 2025

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that [...] Read more.

Occupancy monitoring (OM) and the localization of individuals within indoor environments using wearable devices offer a very promising data communication solution in applications such as home automation, smart office management, outbreak monitoring, and emergency operating plans. OM is challenging when developing solutions that focus on reduced power consumption and cost. Bluetooth low energy (BLE) technology is energy- and cost-efficient compared to other technologies. Integrating BLE Received Signal Strength Indicator (RSSI) signals with machine learning (ML) introduces a new Artificial Intelligence- (AI-) enhanced OM approach. In this paper, we propose an Intelligent Bluetooth Virtual Door (IBVD) OM system for the indoor/outdoor tracking of individuals using the interaction between a BLE device worn by the occupant and two BLE beacons located at the entrance/exit points of a doorway. ML algorithms are used to perform intelligent OM through pattern detection from the BLE RSSI signal(s). This approach differs from other technologies in that it does not require any floorplan information. The developed OM system achieves a range between 96.6% and 97.3% classification accuracy for all tested ML models, where the error translates to a minor delay in the time in which an individual’s location is classified, introducing a highly reliable indoor/outdoor tracking system. Full article

(This article belongs to the Special Issue Smart Sensor Systems for Positioning and Navigation)

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20 pages, 13841 KiB

Open AccessArticle

Guided Filter-Inspired Network for Low-Light RAW Image Enhancement

by Xinyi Liu and Qian Zhao

Sensors 2025, 25(9), 2637; https://doi.org/10.3390/s25092637 - 22 Apr 2025

Low-light RAW image enhancement (LRIE) has attracted increased attention in recent years due to the demand for practical applications. Various deep learning-based methods have been proposed for dealing with this task, among which the fusion-based ones achieve state-of-the-art performance. However, current fusion-based methods [...] Read more.

Low-light RAW image enhancement (LRIE) has attracted increased attention in recent years due to the demand for practical applications. Various deep learning-based methods have been proposed for dealing with this task, among which the fusion-based ones achieve state-of-the-art performance. However, current fusion-based methods do not sufficiently explore the physical correlations between source images and thus fail to sufficiently exploit the complementary information delivered by different sources. To alleviate this issue, we propose a Guided Filter-inspired Network (GFNet) for the LRIE task. The proposed GFNet is designed to fuse sources in a guided filter (GF)-like manner, with the coefficients inferred by the network, within both the image and feature domains. Inheriting the advantages of GF, the proposed method is able to capture more intrinsic correlations between source images and thus better fuse the contextual and textual information extracted from them, facilitating better detail preservation and noise reduction for LRIE. Experiments on benchmark LRIE datasets demonstrate the superiority of the proposed method. Furthermore, the extended applications of GFNet to guided low-light image enhancement tasks indicate its broad applicability. Full article

(This article belongs to the Section Sensing and Imaging)

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