Sensors

16 pages, 6282 KB

Open AccessEditor’s ChoiceArticle

Color QR Codes for Smartphone-Based Analysis of Free Chlorine in Drinking Water

by María González-Gómez, Ismael Benito-Altamirano, Hanna Lizarzaburu-Aguilar, David Martínez-Carpena, Joan Daniel Prades and Cristian Fàbrega

Sensors 2025, 25(11), 3251; https://doi.org/10.3390/s25113251 - 22 May 2025

Viewed by 2027

Abstract

Free chlorine (FC) plays a crucial role in ensuring the safety of drinking water by effectively inactivating pathogenic microorganisms. However, traditional methods for measuring FC levels often require specialized equipment and laboratory settings, limiting their accessibility and practicality for on-site or point-of-use monitoring. [...] Read more.

Free chlorine (FC) plays a crucial role in ensuring the safety of drinking water by effectively inactivating pathogenic microorganisms. However, traditional methods for measuring FC levels often require specialized equipment and laboratory settings, limiting their accessibility and practicality for on-site or point-of-use monitoring. QR Codes are powerful machine-readable patterns that are used worldwide to encode information (i.e., URLs or IDs), but their computer vision features allow QR Codes to act as carriers of other features for several applications. Often, this capability is used for aesthetics, e.g., embedding a logo in the QR Code. In this work, we propose using our technique to build back-compatible Color QR Codes, which can embed dozens of colorimetric references, to assist in the color correction to readout sensors. Specifically, we target two well-known products in the HORECA (hotel/restaurant/café) sector that qualitatively measure chlorine levels in samples of water. The two targeted methods were a BTB strip and a DPD powder. First, the BTB strip was a pH-based indicator distributed by Sensafe^®, which uses the well-known bromothymol blue as a base-reactive indicator; second, the DPD powder was a colorimetric test distributed by Hach^®, which employs diethyl-p-phenylenediamine (DPD) to produce a pink coloration in the presence of free chlorine. Custom Color QR Codes were created for both color palettes and exposed to several illumination conditions, captured with three different mobile devices and tested over different water samples. Results indicate that both methods could be correctly digitized in real-world conditions with our technology, rendering a 88.10% accuracy for the BTB strip measurement, and 84.62% for the DPD powder one. Full article

(This article belongs to the Special Issue Colorimetric Sensors: Methods and Applications (2nd Edition))

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20 pages, 4672 KB

Open AccessEditor’s ChoiceArticle

Industrial-Grade Graphene Films as Distributed Temperature Sensors

by Francesco Siconolfi, Gabriele Cavaliere, Sarah Sibilia, Francesco Cristiano, Gaspare Giovinco and Antonio Maffucci

Sensors 2025, 25(10), 3227; https://doi.org/10.3390/s25103227 - 21 May 2025

Cited by 2 | Viewed by 1582

Abstract

This paper investigates the feasibility of a multi-purpose use of thin films of industrial-grade graphene, adopted initially to realize advanced coatings for thermal management or electromagnetic shielding. Indeed, it is demonstrated that such coatings can be conveniently used as distributed temperature sensors based [...] Read more.

This paper investigates the feasibility of a multi-purpose use of thin films of industrial-grade graphene, adopted initially to realize advanced coatings for thermal management or electromagnetic shielding. Indeed, it is demonstrated that such coatings can be conveniently used as distributed temperature sensors based on the sensitivity of their electrical resistance to temperature. The study is carried out by characterizing three nanomaterials differing in the percentage of graphene nanoplatelets in the temperature range from −40 °C to +60 °C. The paper demonstrates the presence of a reproducible and linear negative temperature coefficient behavior, with a temperature coefficient of the resistance of the order of

- 1.5 \cdot 10^{- 3}

{° C}^{- 1}

. A linear sensor model is then developed and validated through an uncertainty-based approach, yielding a temperature prediction uncertainty of approximately ±2 °C. Finally, the robustness of the sensor concerning moderate environmental variations is verified, as the errors introduced by relative humidity values in the range from 40% to 60% are included in the model’s uncertainty bounds. These results suggest the realistic possibility of adding temperature-sensing capabilities to these graphene coatings with minimal increase in complexity and cost. Full article

(This article belongs to the Section Nanosensors)

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20 pages, 2347 KB

Open AccessEditor’s ChoiceArticle

Oxygen Uptake Prediction for Timely Construction Worker Fatigue Monitoring Through Wearable Sensing Data Fusion

by Srikanth Sagar Bangaru, Chao Wang, Fereydoun Aghazadeh, Shashank Muley and Sueed Willoughby

Sensors 2025, 25(10), 3204; https://doi.org/10.3390/s25103204 - 20 May 2025

Cited by 7 | Viewed by 2019

Abstract

The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO₂) while performing the work. The objective of this study is to develop a [...] Read more.

The physical workload evaluation of construction activities will help to prevent excess physical fatigue or overexertion. The workload determination involves measuring physiological responses such as oxygen uptake (VO₂) while performing the work. The objective of this study is to develop a procedure for automatic oxygen uptake prediction using the worker’s forearm muscle activity and motion data. The fused IMU and EMG data were analyzed to build a bidirectional long-short-term memory (BiLSTM) model to predict VO₂. The results show a strong correlation between the IMU and EMG features and oxygen uptake (R = 0.90, RMSE = 1.257 mL/kg/min). Moreover, measured (9.18 ± 1.97 mL/kg/min) and predicted (9.22 ± 0.09 mL/kg/min) average oxygen consumption to build one scaffold unit are significantly the same. This study concludes that the fusion of IMU and EMG features resulted in high model performance compared to IMU and EMG alone. The results can facilitate the continuous monitoring of the physiological status of construction workers and early detection of any potential occupational risks. Full article

(This article belongs to the Special Issue The Use of Artificial Intelligence and Machine Learning with Sensor Data for Human Healthcare Applications)

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22 pages, 3466 KB

Open AccessEditor’s ChoiceArticle

Hardware-Efficient Phase Demodulation for Digital ϕ-OTDR Receivers with Baseband and Analytic Signal Processing

by Shangming Du, Tianwei Chen, Can Guo, Yuxing Duan, Song Wu and Lei Liang

Sensors 2025, 25(10), 3218; https://doi.org/10.3390/s25103218 - 20 May 2025

Cited by 1 | Viewed by 2110

Abstract

This paper presents hardware-efficient phase demodulation schemes for FPGA-based digital phase-sensitive optical time-domain reflectometry (

ϕ

-OTDR) receivers. We first derive a signal model for the heterodyne

ϕ

-OTDR frontend, then propose and analyze three demodulation methods: (1) a baseband reconstruction approach via [...] Read more.

This paper presents hardware-efficient phase demodulation schemes for FPGA-based digital phase-sensitive optical time-domain reflectometry (

ϕ

-OTDR) receivers. We first derive a signal model for the heterodyne

ϕ

-OTDR frontend, then propose and analyze three demodulation methods: (1) a baseband reconstruction approach via zero-IF downconversion, (2) an analytic signal generation technique using the Hilbert transform (HT), and (3) a wavelet transform (WT)-based alternative for analytic signal extraction. Algorithm-hardware co-design implementations are detailed for both RFSoC and conventional FPGA platforms, with resource utilization comparisons. Additionally, we introduce an incremental DC-rejected phase unwrapper (IDRPU) algorithm to jointly address phase unwrapping and DC drift removal, minimizing computational overhead while avoiding numerical overflow. Experiments on simulated and real-world

ϕ

-OTDR data show that the HT method matches the performance of zero-IF demodulation with simpler hardware and lower resource usage, while the WT method offers enhanced robustness against fading noise (3.35–22.47 dB SNR improvement in fading conditions), albeit with slightly ambiguous event boundaries and higher hardware utilization. These findings provide actionable insights for demodulator design in distributed acoustic sensing (DAS) applications and advance the development of single-chip DAS systems. Full article

(This article belongs to the Special Issue Advances in Optical Sensing, Instrumentation and Systems: 2nd Edition)

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16 pages, 3593 KB

Open AccessEditor’s ChoiceArticle

Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions

by Thilini S. Karunarathna and Zilu Liang

Sensors 2025, 25(10), 3207; https://doi.org/10.3390/s25103207 - 20 May 2025

Cited by 6 | Viewed by 6863

Abstract

Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasive alternatives—but many still depend on manually logged food intake and activity, [...] Read more.

Continuous monitoring of glucose levels is important for diabetes management and prevention. While traditional glucose monitoring methods are often invasive and expensive, recent approaches using machine learning (ML) models have explored non-invasive alternatives—but many still depend on manually logged food intake and activity, which is burdensome and impractical for everyday use. In this study, we propose a novel approach that eliminates the need for manual input by utilizing only passively collected, automatically recorded multi-modal data from non-invasive wearable sensors. This enables practical and continuous glucose prediction in real-world, free-living environments. We used the BIG IDEAs Lab Glycemic Variability and Wearable Device Data (BIGIDEAs) dataset, which includes approximately 26,000 CGM readings, simultaneous ly collected wearable data, and demographic information. A total of 236 features encompassing physiological, behavioral, circadian, and demographic factors were constructed. Feature selection was conducted using random-forest-based importance analysis to select the most relevant features for model training. We evaluated the effectiveness of various ML regression techniques, including linear regression, ridge regression, random forest regression, and XGBoost regression, in terms of prediction and clinical accuracy. Biological sex, circadian rhythm, behavioral features, and tonic features of electrodermal activity (EDA) emerged as key predictors of glucose levels. Tree-based models outperformed linear models in both prediction and clinical accuracy. The XGBoost (XR) model performed best, achieving an R-squared of 0.73, an RMSE of 11.9 mg/dL, an NRMSE of 0.52 mg/dL, a MARD of 7.1%, and 99.4% of predictions falling within Zones A and B of the Clarke Error Grid. This study demonstrates the potential of combining feature engineering and tree-based ML regression techniques for continuous glucose monitoring using wearable sensors. Full article

(This article belongs to the Special Issue Wearable Sensors for Continuous Health Monitoring and Analysis)

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55 pages, 931 KB

Open AccessEditor’s ChoiceReview

Virtual Electroencephalogram Acquisition: A Review on Electroencephalogram Generative Methods

by Zhishui You, Yuzhu Guo, Xiulei Zhang and Yifan Zhao

Sensors 2025, 25(10), 3178; https://doi.org/10.3390/s25103178 - 18 May 2025

Cited by 4 | Viewed by 3902

Abstract

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to [...] Read more.

Driven by the remarkable capabilities of machine learning, brain–computer interfaces (BCIs) are carving out an ever-expanding range of applications across a multitude of diverse fields. Notably, electroencephalogram (EEG) signals have risen to prominence as the most prevalently utilized signals within BCIs, owing to their non-invasive essence, exceptional portability, cost-effectiveness, and high temporal resolution. However, despite the significant strides made, the paucity of EEG data has emerged as the main bottleneck, preventing generalization of decoding algorithms. Taking inspiration from the resounding success of generative models in computer vision and natural language processing arenas, the generation of synthetic EEG data from limited recorded samples has recently garnered burgeoning attention. This paper undertakes a comprehensive and thorough review of the techniques and methodologies underpinning the generative models of the general EEG, namely the variational autoencoder (VAE), the generative adversarial network (GAN), and the diffusion model. Special emphasis is placed on their practical utility in augmenting EEG data. The structural designs and performance metrics of the different generative approaches in various application domains have been meticulously dissected and discussed. A comparative analysis of the strengths and weaknesses of each existing model has been carried out, and prospective avenues for future enhancement and refinement have been put forward. Full article

(This article belongs to the Special Issue EEG Signal Processing Techniques and Applications—3rd Edition)

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19 pages, 4959 KB

Open AccessEditor’s ChoiceArticle

Performance Optimization of a High-Speed Permanent Magnet Synchronous Motor Drive System for Formula Electric Vehicle Application

by Mahmoud Ibrahim, Oskar Järg, Raigo Seppago and Anton Rassõlkin

Sensors 2025, 25(10), 3156; https://doi.org/10.3390/s25103156 - 16 May 2025

Cited by 3 | Viewed by 3311

Abstract

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay [...] Read more.

The proliferation of electric vehicle (EV) racing competitions, such as Formula electric vehicle (FEV) competitions, has intensified the quest for high-performance electric propulsion systems. High-speed permanent magnet synchronous motors (PMSMs) for FEVs necessitate an optimized control strategy that adeptly manages the complex interplay between electromagnetic torque production and minimal power loss, ensuring peak operational efficiency and performance stability across the full speed range. This paper delves into the optimization of high-speed PMSM, pivotal for its application in FEVs. It begins with a thorough overview of the FEV motor’s basic principles, followed by the derivation of a detailed mathematical model that lays the groundwork for subsequent analyses. Utilizing MATLAB/Simulink, a simulation model of the motor drive system was constructed. The proposed strategy synergizes the principles of maximum torque per ampere (MTPA) with the flux weakening control technique instead of conventional zero direct axis current (ZDAC), aiming to push the boundaries of motor performance while navigating the inherent limitations of high-speed operation. Covariance matrix adaptation evolution strategy (CMA-ES) was deployed to determine the optimal d-q axis current ratio achieving maximum operating torque without overdesign problems. The implementation of the optimized control strategy was rigorously tested on the simulation model, with subsequent validation conducted on a real test bench setup. The outcomes of the proposed technique reveal that the tailored control strategy significantly elevates motor torque performance by almost 22%, marking a pivotal advancement in the domain of high-speed PMSM. Full article

(This article belongs to the Special Issue Cooperative Perception and Control for Autonomous Vehicles)

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22 pages, 21322 KB

Open AccessEditor’s ChoiceArticle

Detecting Burn Severity and Vegetation Recovery After Fire Using dNBR and dNDVI Indices: Insight from the Bosco Difesa Grande, Gravina in Southern Italy

by Somayeh Zahabnazouri, Patrick Belmont, Scott David, Peter E. Wigand, Mario Elia and Domenico Capolongo

Sensors 2025, 25(10), 3097; https://doi.org/10.3390/s25103097 - 14 May 2025

Cited by 7 | Viewed by 3450

Abstract

Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in [...] Read more.

Wildfires serve a paradoxical role in landscapes—supporting biodiversity and nutrient cycling while also threatening ecosystems and economies, especially as climate change intensifies their frequency and severity. This study investigates the impact of wildfires and vegetation recovery in the Bosco Difesa Grande forest in southern Italy, focusing on the 2017 and 2021 fire events. Using Google Earth Engine (GEE) accessed in January 2025, we applied remote sensing techniques to assess burn severity and post-fire regrowth. Sentinel-2 imagery was used to compute the Normalized Burn Ratio (NBR) and Normalized Difference Vegetation Index (NDVI); burn severity was derived from differenced NBR (dNBR), and vegetation recovery was monitored via differenced NDVI (dNDVI) and multi-year NDVI time series. We uniquely compare recovery across four zones with different fire histories—unburned, single-burn (2017 or 2021), and repeated-burn (2017 and 2021)—providing a novel perspective on post-fire dynamics in Mediterranean ecosystems. Results show that low-severity zones recovered more quickly than high-severity areas. Repeated-burn zones experienced the slowest and least complete recovery, while unburned areas remained stable. These findings suggest that repeated fires may shift vegetation from forest to shrubland. This study highlights the importance of remote sensing for post-fire assessment and supports adaptive land management to enhance long-term ecological resilience. Full article

(This article belongs to the Special Issue Remote Sensing Image Processing, Analysis and Application)

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20 pages, 1914 KB

Open AccessEditor’s ChoiceArticle

A Predictive Approach for Enhancing Accuracy in Remote Robotic Surgery Using Informer Model

by Muhammad Hanif Lashari, Shakil Ahmed, Wafa Batayneh and Ashfaq Khokhar

Sensors 2025, 25(10), 3067; https://doi.org/10.3390/s25103067 - 13 May 2025

Cited by 2 | Viewed by 1583

Abstract

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient [...] Read more.

Precise and real-time estimation of the robotic arm’s position on the patient’s side is essential for the success of remote robotic surgery in Tactile Internet (TI) environments. This paper presents a prediction model based on the Transformer-based Informer framework for accurate and efficient position estimation, combined with a Four-State Hidden Markov Model (4-State HMM) to simulate realistic packet loss scenarios. The proposed approach addresses challenges such as network delays, jitter, and packet loss to ensure reliable and precise operation in remote surgical applications. The method integrates the optimization problem into the Informer model by embedding constraints such as energy efficiency, smoothness, and robustness into its training process using a differentiable optimization layer. The Informer framework uses features such as ProbSparse attention, attention distilling, and a generative-style decoder to focus on position-critical features while maintaining a low computational complexity of

O (L log L)

. The method is evaluated using the JIGSAWS dataset, achieving a prediction accuracy of over 90% under various network scenarios. A comparison with models such as TCN, RNN, and LSTM demonstrates the Informer framework’s superior performance in handling position prediction and meeting real-time requirements, making it suitable for Tactile Internet-enabled robotic surgery. Full article

(This article belongs to the Section Sensors and Robotics)

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18 pages, 3955 KB

Open AccessEditor’s ChoiceArticle

Field Testing Multi-Parametric Wearable Technologies for Wildfire Firefighting Applications

by Mariangela Pinnelli, Stefano Marsella, Fabio Tossut, Emiliano Schena, Roberto Setola and Carlo Massaroni

Sensors 2025, 25(10), 3066; https://doi.org/10.3390/s25103066 - 13 May 2025

Cited by 6 | Viewed by 2741

Abstract

In response to the escalating complexity and frequency of wildland fires, this study investigates the feasibility of using wearable devices for real-time monitoring of cardiac, respiratory, physical, and environmental parameters during live wildfire suppression tasks. Data were collected from twelve male firefighters (FFs) [...] Read more.

In response to the escalating complexity and frequency of wildland fires, this study investigates the feasibility of using wearable devices for real-time monitoring of cardiac, respiratory, physical, and environmental parameters during live wildfire suppression tasks. Data were collected from twelve male firefighters (FFs) from the Italian National Fire Corp during a simulated protocol, including rest, running, and active fire suppression phases. Physiological and physical metrics such as heart rate (HR), heart rate variability (HRV), respiratory frequency (f_R) and physical activity levels were extracted using chest straps. The protocol designed to mimic real-world firefighting scenarios revealed significant cardiovascular and respiratory strain, with HR often exceeding 85% of age-predicted maxima and sustained elevations in high-stress roles. Recovery phases highlighted variability in physiological responses, with reduced HRV indicating heightened autonomic stress. Additionally, physical activity analysis showed task-dependent intensity variations, with debris management roles exhibiting consistently high exertion levels. These findings demonstrate the relevance of wearable technology for real-time monitoring, providing an accurate analysis of key metrics to offer a comprehensive overview of work-rest cycles, informing role-specific training and operational strategies. Full article

(This article belongs to the Special Issue Development of Flexible and Wearable Sensors and Their Applications)

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20 pages, 7568 KB

Open AccessEditor’s ChoiceArticle

Carbon Nano-Onions–Polyvinyl Alcohol Nanocomposite for Resistive Monitoring of Relative Humidity

by Bogdan-Catalin Serban, Niculae Dumbravescu, Octavian Buiu, Marius Bumbac, Carmen Dumbravescu, Mihai Brezeanu, Cristina Pachiu, Cristina-Mihaela Nicolescu, Cosmin Romanitan and Oana Brincoveanu

Sensors 2025, 25(10), 3047; https://doi.org/10.3390/s25103047 - 12 May 2025

Cited by 4 | Viewed by 1335

Abstract

This paper reports several preliminary investigations concerning the relative humidity (RH) detection response of a chemiresistive sensor that uses a novel sensing layer based on pristine carbon nano-onions (CNOs) and polyvinyl alcohol (PVA) at a 1/1 and 2/1 w/w ratio. The [...] Read more.

This paper reports several preliminary investigations concerning the relative humidity (RH) detection response of a chemiresistive sensor that uses a novel sensing layer based on pristine carbon nano-onions (CNOs) and polyvinyl alcohol (PVA) at a 1/1 and 2/1 w/w ratio. The sensing device, including a Si/SiO₂ substrate and gold electrodes, is obtained by depositing the CNOs–PVA aqueous suspension on the sensing structure by drop casting. The composition and morphology of the sensing film are explored by means of scanning electron microscopy, Raman spectroscopy, atomic force microscopy, and X-ray diffraction. The manufactured sensor’s room temperature RH detection performance is examined by applying a continuous flow of the electric current between the interdigitated electrodes and measuring the voltage as the RH varies from 5% to 95%. For RH below 82% (sensing layer based on CNOs–PVA at 1/1 w/w ratio) or below 50.5% (sensing layer based on CNOs–PVA at 2/1 w/w ratio), the resistance varies linearly with RH, with a moderate slope. The newly developed sensor, using CNOs–PVA at a 1:1 ratio (w/w), responded as well as or better than the reference sensor. At the same time, the recorded recovery time was about 30 s, which is half the recovery time of the reference sensor. Additionally, the changes in resistance (ΔR/ΔRH) for different humidity levels showed that the CNOs–PVA layer at 1:1 was more sensitive at humidity levels above 80%. The main RH sensing mechanisms considered and discussed are the decrease in the hole concentration in the CNOs during the interaction with an electron donor molecule, such as water, and the swelling of the hydrophilic PVA. The experimental RH detection data are analyzed and compared with the RH sensing results reported in previously published work on RH detectors employing sensing layers based on oxidized carbon nanohorns–polyvinylpirrolidone (PVP), oxidized carbon nanohorns–PVA and CNOs–polyvinylpyrrolidone. Full article

(This article belongs to the Special Issue New Polymeric Nanomaterials: Optimization and Application in the Field of Environmental Sensors)

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23 pages, 7532 KB

Open AccessEditor’s ChoiceArticle

Real-Time Aerial Multispectral Object Detection with Dynamic Modality-Balanced Pixel-Level Fusion

by Zhe Wang and Qingling Zhang

Sensors 2025, 25(10), 3039; https://doi.org/10.3390/s25103039 - 12 May 2025

Cited by 1 | Viewed by 2061

Abstract

Aerial object detection plays a critical role in numerous fields, utilizing the flexibility of airborne platforms to achieve real-time tasks. Combining visible and infrared sensors can overcome limitations under low-light conditions, enabling full-time tasks. While feature-level fusion methods exhibit comparable performances in visible–infrared [...] Read more.

Aerial object detection plays a critical role in numerous fields, utilizing the flexibility of airborne platforms to achieve real-time tasks. Combining visible and infrared sensors can overcome limitations under low-light conditions, enabling full-time tasks. While feature-level fusion methods exhibit comparable performances in visible–infrared multispectral object detection, they suffer from heavy model size, inadequate inference speed and visible light preferences caused by inherent modality imbalance, limiting their applications in airborne platform deployment. To address these challenges, this paper proposes a YOLO-based real-time multispectral fusion framework combining pixel-level fusion with dynamic modality-balanced augmentation called Full-time Multispectral Pixel-wise Fusion Network (FMPFNet). Firstly, we introduce the Multispectral Luminance Weighted Fusion (MLWF) module consisting of attention-based modality reconstruction and feature fusion. By leveraging YUV color space transformation, this module efficiently fuses RGB and IR modalities while minimizing computational overhead. We also propose the Dynamic Modality Dropout and Threshold Masking (DMDTM) strategy, which balances modality attention and improves detection performance in low-light scenarios. Additionally, we refine our model to enhance the detection of small rotated objects, a requirement commonly encountered in aerial detection applications. Experimental results on the DroneVehicle dataset demonstrate that our FMPFNet achieves 76.80% mAP50 and 132 FPS, outperforming state-of-the-art feature-level fusion methods in both accuracy and inference speed. Full article

(This article belongs to the Section Remote Sensors)

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23 pages, 11070 KB

Open AccessEditor’s ChoiceReview

Gap Measurements in Aerospace Engineering

by Xinyuan Zhao, Chao Zhang, Long Xu, Tao Wang, Pei Li, Heng Zhang and Jun Yang

Sensors 2025, 25(10), 3059; https://doi.org/10.3390/s25103059 - 12 May 2025

Cited by 2 | Viewed by 1852

Abstract

Advanced precision gap measurement technologies play a pivotal role in ensuring the design and operational efficiency of aerospace systems. Gaps between aircraft components directly influence assembly accuracy, performance, and safety. This review comprehensively explores the state-of-the-art in precision gap measurement technologies used in [...] Read more.

Advanced precision gap measurement technologies play a pivotal role in ensuring the design and operational efficiency of aerospace systems. Gaps between aircraft components directly influence assembly accuracy, performance, and safety. This review comprehensively explores the state-of-the-art in precision gap measurement technologies used in the aerospace sector. It categorizes and analyzes various sensors based on their operating principles, including optical, electrical, and other emerging technologies. Each sensor’s principle of operation, key advantages, and limitations are detailed. Furthermore, the paper identifies the significant challenges faced in aerospace gap measurement and discusses future development directions, emphasizing the need for enhanced accuracy, adaptability, and resilience to environmental factors. This study provides valuable insights for researchers and engineers in the field, guiding future innovations in precision gap measurement technologies to meet the evolving demands of aerospace manufacturing and maintenance. Full article

(This article belongs to the Section Sensors Development)

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24 pages, 9146 KB

Open AccessEditor’s ChoiceArticle

AI-Driven Dynamic Covariance for ROS 2 Mobile Robot Localization

by Bogdan Felician Abaza

Sensors 2025, 25(10), 3026; https://doi.org/10.3390/s25103026 - 11 May 2025

Cited by 3 | Viewed by 4777

Abstract

In the evolving field of mobile robotics, enhancing localization robustness in dynamic environments remains a critical challenge, particularly for ROS 2-based systems where sensor fusion plays a pivotal role. This study evaluates an AI-driven approach to dynamically adjust covariance parameters for improved pose [...] Read more.

In the evolving field of mobile robotics, enhancing localization robustness in dynamic environments remains a critical challenge, particularly for ROS 2-based systems where sensor fusion plays a pivotal role. This study evaluates an AI-driven approach to dynamically adjust covariance parameters for improved pose estimation in a differential-drive mobile robot. A regression model was integrated into the robot_localization package to adapt the Extended Kalman Filter (EKF) covariance in real time, with experiments conducted in a controlled indoor setting over runs comparing AI-enabled dynamic covariance prediction against a static covariance baseline across Static, Moderate, and Aggressive motion dynamics. The AI-enabled system achieved a Mean Absolute Error (MAE) of 0.0061 for pose estimation and reduced median yaw prediction errors to 0.0362 rad (static) and 0.0381 rad (moderate) with tighter interquartile ranges (0.0489 rad, 0.1069 rad) compared to the baseline (0.0222 rad, 0.1399 rad). Aggressive dynamics posed challenges, with errors up to 0.9491 rad due to data distribution bias and Random Forest model constraints. Enhanced dataset augmentation, LSTM modeling, and online learning are proposed to address these limitations. Datalogging enabled iterative re-training, supporting scalable state estimation with future focus on online learning. Full article

(This article belongs to the Section Sensors and Robotics)

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18 pages, 22040 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 1 | Viewed by 3763

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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13 pages, 5645 KB

Open AccessEditor’s ChoiceArticle

Morphology-Dependent Behavior of PVDF/ZnO Composites: Their Fabrication and Application in Pressure Sensors

by Binbin Zhang, Wenhui Zhang, Wei Luo, Zhijie Liang, Yan Hong, Jianhui Li, Guoyun Zhou and Wei He

Sensors 2025, 25(9), 2936; https://doi.org/10.3390/s25092936 - 7 May 2025

Cited by 2 | Viewed by 1843

Abstract

This study investigated the impact of zinc oxide’s (ZnO’s) morphology on the piezoelectric performance of polyvinylidene fluoride (PVDF) composites for flexible sensors. Rod-like (NR) and sheet-like (NS) ZnO nanoparticles were synthesized via hydrothermal methods and incorporated into PVDF through direct ink writing (DIW). [...] Read more.

This study investigated the impact of zinc oxide’s (ZnO’s) morphology on the piezoelectric performance of polyvinylidene fluoride (PVDF) composites for flexible sensors. Rod-like (NR) and sheet-like (NS) ZnO nanoparticles were synthesized via hydrothermal methods and incorporated into PVDF through direct ink writing (DIW). The structural analyses confirmed the successful formation of wurtzite ZnO and enhanced β-phase content in the PVDF/ZnO composites. At a degree of 15 wt% loading, the ZnO-NS nanoparticles achieved the highest β-phase fraction (81.3%) in PVDF due to their high specific surface area, facilitating dipole alignment and strain-induced crystallization. The optimized PVDF/ZnO-NS-15 sensor demonstrated superior piezoelectric outputs (4.75 V, 140 mV/N sensitivity) under a 27 N force, outperforming its ZnO-NR counterparts (3.84 V, 100 mV/N). The cyclic tests revealed exceptional durability (<5% signal attenuation after 1000 impacts) and a rapid response (<100 ms). The application trials validated their real-time motion-monitoring capabilities, including finger joint flexion detection. This work highlights the morphology-dependent interfacial polarization as a critical factor for high-performance flexible sensors, offering a scalable DIW-based strategy for wearable electronics. Full article

(This article belongs to the Special Issue Functional Nanomaterials in Sensing)

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17 pages, 4243 KB

Open AccessEditor’s ChoiceArticle

Estimation of Respiratory States Based on a Measurement Model of Airflow Characteristics in Powered Air-Purifying Respirators Using Differential Pressure and Pulse Width Modulation Control Signals—In the Development of a Public-Oriented Powered Air-Purifying Respirator as an Alternative to Lockdown Measures

by Yusaku Fujii, Akihiro Takita, Seiji Hashimoto and Kenji Amagai

Sensors 2025, 25(9), 2939; https://doi.org/10.3390/s25092939 - 7 May 2025

Cited by 3 | Viewed by 2626

Abstract

Fluid dynamics modeling was conducted for the supply unit of a Powered Air-Purifying Respirator (PAPR) consisting of a nonwoven fabric filter and a pump, as well as for the exhaust filter (nonwoven fabric). The supply flow rate Q₁ was modeled as a [...] Read more.

Fluid dynamics modeling was conducted for the supply unit of a Powered Air-Purifying Respirator (PAPR) consisting of a nonwoven fabric filter and a pump, as well as for the exhaust filter (nonwoven fabric). The supply flow rate Q₁ was modeled as a function of the differential pressure ΔP and the duty value d of the PWM control under a constant pump voltage of V = 12.0 [V]. In contrast, the exhaust flow rate Q₂ was modeled solely as a function of ΔP. To simulate the pressurized hood compartment of the PAPR, a pressure buffer and a connected “respiratory airflow simulator” (a piston–cylinder mechanism) were developed. The supply unit and exhaust filter were connected to this pressure buffer, and simulated respiratory flow was introduced as an external disturbance flow. Under these conditions, it was demonstrated that the respiratory state—i.e., the expiratory state (flow from the simulator to the pressure buffer) and the inspiratory state (flow from the pressure buffer to the simulator)—can be estimated from the differential pressure ΔP, the pump voltage V, and the PWM duty value d, with respect to the disturbance flow generated by the respiratory airflow simulator. It was also confirmed that such respiratory state estimation remains valid even when the duty value d of the pump is being actively modulated to control the internal pressure of the PAPR hood. Furthermore, based on the estimated respiratory states, a theoretical investigation was conducted on constant pressure control inside the PAPR and on the inverse pressure control aimed at supporting respiratory activity—namely, pressure control that assists breathing by depressurizing when expiratory motion is detected and pressurizing when inspiratory motion is detected. This study was conducted as part of a research and development project on public-oriented PAPR systems, which are being explored as alternatives to lockdown measures in response to airborne infectious diseases such as COVID-19. The present work specifically focused on improving the wearing comfort of the PAPR. Full article

(This article belongs to the Special Issue Sensors and Analog Front-End Circuits for Sensing Systems and High Sensitivity Measurements: 2nd Edition)

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14 pages, 2429 KB

Open AccessEditor’s ChoiceArticle

End-to-End Architecture for Real-Time IoT Analytics and Predictive Maintenance Using Stream Processing and ML Pipelines

by Ouiam Khattach, Omar Moussaoui and Mohammed Hassine

Sensors 2025, 25(9), 2945; https://doi.org/10.3390/s25092945 - 7 May 2025

Cited by 10 | Viewed by 7981

Abstract

The rapid proliferation of Internet of Things (IoT) devices across industries has created a need for robust, scalable, and real-time data processing architectures capable of supporting intelligent analytics and predictive maintenance. This paper presents a novel comprehensive architecture that enables end-to-end processing of [...] Read more.

The rapid proliferation of Internet of Things (IoT) devices across industries has created a need for robust, scalable, and real-time data processing architectures capable of supporting intelligent analytics and predictive maintenance. This paper presents a novel comprehensive architecture that enables end-to-end processing of IoT data streams, from acquisition to actionable insights. The system integrates Kafka-based message brokering for the high-throughput ingestion of real-time sensor data, with Apache Spark facilitating batch and stream extraction, transformation, and loading (ETL) processes. A modular machine-learning pipeline handles automated data preprocessing, training, and evaluation across various models. The architecture incorporates continuous monitoring and optimization components to track system performance and model accuracy, feeding insights to users via a dedicated Application Programming Interface (API). The design ensures scalability, flexibility, and real-time responsiveness, making it well suited for industrial IoT applications requiring continuous monitoring and intelligent decision-making. Full article

(This article belongs to the Special Issue IoT Sensor Systems: Design, Interfaces, Signals, Processing, and Applications)

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24 pages, 3848 KB

Open AccessEditor’s ChoiceArticle

Efficient Deep Learning Model Compression for Sensor-Based Vision Systems via Outlier-Aware Quantization

by Joonhyuk Yoo and Guenwoo Ban

Sensors 2025, 25(9), 2918; https://doi.org/10.3390/s25092918 - 5 May 2025

Cited by 4 | Viewed by 2864

Abstract

With the rapid growth of sensor technology and computer vision, efficient deep learning models are essential for real-time image feature extraction in resource-constrained environments. However, most existing quantized deep neural networks (DNNs) are highly sensitive to outliers, leading to severe performance degradation in [...] Read more.

With the rapid growth of sensor technology and computer vision, efficient deep learning models are essential for real-time image feature extraction in resource-constrained environments. However, most existing quantized deep neural networks (DNNs) are highly sensitive to outliers, leading to severe performance degradation in low-precision settings. Our study reveals that outliers extending beyond the nominal weight distribution significantly increase the dynamic range, thereby reducing quantization resolution and affecting sensor-based image analysis tasks. To address this, we propose an outlier-aware quantization (OAQ) method that effectively reshapes weight distributions to enhance quantization accuracy. By analyzing previous outlier-handling techniques using structural similarity (SSIM) measurement results, we demonstrated that OAQ significantly reduced the negative impact of outliers while maintaining computational efficiency. Notably, OAQ was orthogonal to existing quantization schemes, making it compatible with various quantization methods without additional computational overhead. Experimental results on multiple CNN architectures and quantization approaches showed that OAQ effectively mitigated quantization errors. In post-training quantization (PTQ), our 4-bit OAQ ResNet20 model achieved improved accuracy compared with full-precision counterparts, while in quantization-aware training (QAT), OAQ enhanced 2-bit quantization performance by 43.55% over baseline methods. These results confirmed the potential of OAQ for optimizing deep learning models in sensor-based vision applications. Full article

(This article belongs to the Special Issue Image Feature Extraction for Computer Vision Tasks in Sensor Systems and Applications)

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23 pages, 1271 KB

Open AccessEditor’s ChoiceArticle

The Impact of Digital Storytelling on Presence, Immersion, Enjoyment, and Continued Usage Intention in VR-Based Museum Exhibitions

by Sungbok Chang and Jungho Suh

Sensors 2025, 25(9), 2914; https://doi.org/10.3390/s25092914 - 5 May 2025

Cited by 21 | Viewed by 9989

Abstract

Recent advancements in virtual reality (VR) technology have introduced a new paradigm in exhibition culture, with digital storytelling emerging as a crucial component supporting this transformation. Particularly in virtual exhibitions, digital storytelling serves as a key medium for enhancing user experience and maximizing [...] Read more.

Recent advancements in virtual reality (VR) technology have introduced a new paradigm in exhibition culture, with digital storytelling emerging as a crucial component supporting this transformation. Particularly in virtual exhibitions, digital storytelling serves as a key medium for enhancing user experience and maximizing immersion, thereby fostering continuous usage intention. However, systematic research on the structural influence of VR-based digital storytelling on user experience remains insufficient. To address this research gap, this study examines the impact of key components of digital storytelling in VR—namely, interest, emotion, and educational value—on presence, immersion, enjoyment, and continuous usage intention through path analysis. The results indicate that interest, emotion, and educational value all have a significant positive effect on presence. Furthermore, while interest and emotion positively influence immersion, educational value does not show a statistically significant effect. Presence, in turn, has a positive effect on immersion, enjoyment, and continuous usage intention, while immersion also positively influences enjoyment and continuous usage intention. Finally, enjoyment was found to have a significant positive effect on continuous usage intention. This study empirically validates the effectiveness of digital storytelling in virtual exhibition environments, offering valuable academic and practical insights. Theoretically, it contributes to the field by elucidating the complex and hierarchical relationships among three core factors—interest, emotion, and educational value—and their impact on user experience. Practically, the findings provide strategic guidelines for designing virtual exhibitions that maximize user immersion and satisfaction, reaffirming the importance of storytelling content that emphasizes interest and emotion. Full article

(This article belongs to the Section Sensing and Imaging)

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27 pages, 4634 KB

Open AccessEditor’s ChoiceArticle

A Blockchain Framework for Scalable, High-Density IoT Networks of the Future

by Alexandru A. Maftei, Adrian I. Petrariu, Valentin Popa and Alexandru Lavric

Sensors 2025, 25(9), 2886; https://doi.org/10.3390/s25092886 - 3 May 2025

Cited by 2 | Viewed by 3730

Abstract

The Internet of Things has transformed industries, cities, and homes through a vast network of interconnected devices. As the IoT expands, the number of devices is projected to reach tens of billions, generating massive amounts of data. This growth presents significant data storage, [...] Read more.

The Internet of Things has transformed industries, cities, and homes through a vast network of interconnected devices. As the IoT expands, the number of devices is projected to reach tens of billions, generating massive amounts of data. This growth presents significant data storage, management, and security challenges, especially in large-scale deployments such as smart cities and industrial operations. Traditional centralized solutions struggle to handle the high data volume and heterogeneity of IoT data, while ensuring real-time processing and interoperability. This paper presents the design, development, and evaluation of a blockchain framework tailored for the secure storage and management of data generated by IoT devices. Our framework introduces efficient methods for managing, transmitting, and securing data packets within a blockchain-enabled IoT network. The proposed framework uses a gateway node to aggregate multiple data packets into single transactions, increasing throughput, optimizing network bandwidth, reducing latency, simplifying data retrieval, and improving scalability. The results obtained from rigorous analysis and testing of the evaluated scenarios show that the proposed blockchain framework achieves a high level of performance, scalability, and efficiency while ensuring robust security being able to integrate a large number of IoT devices in a flexible manner. Full article

(This article belongs to the Special Issue Decentralized Architectures and Blockchain for Resilient and Trustworthy Infrastructures: From Theory to Applications)

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26 pages, 8139 KB

Open AccessEditor’s ChoiceArticle

Design and Construction of UAV-Based Measurement System for Water Hyperspectral Remote-Sensing Reflectance

by Haohui Zeng, Xianqiang He, Yan Bai, Fang Gong, Difeng Wang and Xuan Zhang

Sensors 2025, 25(9), 2879; https://doi.org/10.3390/s25092879 - 2 May 2025

Cited by 3 | Viewed by 1661

Abstract

Acquiring a large number of in situ water spectral measurements is fundamental for constructing water color remote-sensing retrieval models and validating the accuracy of water color remote-sensing products. However, traditional manual site-based water spectral measurements are time-consuming and labor-intensive, resulting in an insufficient [...] Read more.

Acquiring a large number of in situ water spectral measurements is fundamental for constructing water color remote-sensing retrieval models and validating the accuracy of water color remote-sensing products. However, traditional manual site-based water spectral measurements are time-consuming and labor-intensive, resulting in an insufficient number of in situ water spectral samples to date. To resolve this issue, this study develops an unmanned aerial vehicle-based hyperspectral remote-sensing reflectance measurement system (UAV-RRS) capable of continuous on-the-move water spectral measurements. This paper provides a detailed introduction to the system components and conducts precise experiments on the correction and calibration of the spectral sensors. Using this system, an in situ–UAV–satellite multi-source remote-sensing reflectance comparison experiment was conducted in the middle reaches of the Qiantang River, East China, to evaluate the accuracy and reliability of UAV-RRS and extend the analysis to satellite data across different spatial scales. The results demonstrate that, in small-scale water bodies, UAV-RRS achieves higher spatial precision and spectral accuracy, offering a valuable solution for high-precision, low-altitude continuous water body observations. Full article

(This article belongs to the Section Remote Sensors)

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30 pages, 2043 KB

Open AccessEditor’s ChoiceReview

Wearable Devices for Arrhythmia Detection: Advancements and Clinical Implications

by Ahmed Abdelrazik, Mahmoud Eldesouky, Ibrahim Antoun, Edward Y. M. Lau, Abdulmalik Koya, Zakariyya Vali, Safiyyah A. Suleman, James Donaldson and G. André Ng

Sensors 2025, 25(9), 2848; https://doi.org/10.3390/s25092848 - 30 Apr 2025

Cited by 15 | Viewed by 13928

Abstract

Cardiac arrhythmias are a growing global health concern, and the need for accessible, continuous monitoring has driven rapid advancements in wearable technologies. This review explores the evolution, capabilities, and clinical impact of modern wearables for arrhythmia detection, including smartwatches, smart rings, ECG patches, [...] Read more.

Cardiac arrhythmias are a growing global health concern, and the need for accessible, continuous monitoring has driven rapid advancements in wearable technologies. This review explores the evolution, capabilities, and clinical impact of modern wearables for arrhythmia detection, including smartwatches, smart rings, ECG patches, and smart textiles. In light of the recent surge in commercially available wearables across all categories, this review offers a detailed comparative analysis of leading devices, evaluating cost, regulatory approval, model specifications, and system compatibility. Smartwatches and patches, in particular, show a strong performance in atrial fibrillation detection, with patches outperforming Holter monitors in long-term monitoring and diagnostic yield. This review highlights a paradigm shift toward patient-initiated diagnostics but also discusses challenges such as false positives, regulatory gaps, and healthcare integration. Overall, wearable devices hold significant promise for reshaping arrhythmia management through early detection and remote monitoring. Full article

(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)

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33 pages, 678 KB

Open AccessEditor’s ChoiceReview

Internet of Medical Things Systems Review: Insights into Non-Functional Factors

by Giovanni Donato Gallo and Daniela Micucci

Sensors 2025, 25(9), 2795; https://doi.org/10.3390/s25092795 - 29 Apr 2025

Cited by 11 | Viewed by 5021

Abstract

Internet of Medical Things (IoMT) is a rapidly evolving field with the potential to bring significant changes to healthcare. While several surveys have examined the structure and operation of these systems, critical aspects such as interoperability, sustainability, security, runtime self-adaptation [...] Read more.

Internet of Medical Things (IoMT) is a rapidly evolving field with the potential to bring significant changes to healthcare. While several surveys have examined the structure and operation of these systems, critical aspects such as interoperability, sustainability, security, runtime self-adaptation, and configurability are sometimes overlooked. Interoperability is essential for integrating data from various devices and platforms to provide a comprehensive view of a patient’s health. Sustainability addresses the environmental impact of IoMT technologies, crucial in the context of green computing. Security ensures the protection of sensitive patient data from breaches and manipulation. Runtime self-adaptation allows systems to adjust to changing patient conditions and environments. Configurability enables IoMT frameworks to monitor diverse patient conditions and manage different treatment paths. This article reviews current techniques addressing these aspects and highlights areas requiring further research. Full article

(This article belongs to the Section Biomedical Sensors)

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18 pages, 3089 KB

Open AccessEditor’s ChoiceArticle

Quantitative Estimation of Organic Pollution in Inland Water Using Sentinel-2 Multispectral Imager

by Jiayi Li, Ruru Deng, Yu Guo, Cong Lei, Zhenqun Hua and Junying Yang

Sensors 2025, 25(9), 2737; https://doi.org/10.3390/s25092737 - 26 Apr 2025

Cited by 2 | Viewed by 1450

Abstract

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, [...] Read more.

Organic pollution poses a significant threat to water security, making the monitoring of organic pollutants in water environments essential for the protection of water resources. Remote sensing technology, with its wide coverage, continuous monitoring capability, and cost-efficiency, overcomes the limitations of traditional methods, which are often time-consuming, labor-intensive, and spatially restricted. As a result, it has become an effective tool for monitoring organic pollution in water environments. In this study, we propose a physically constrained remote sensing algorithm for the quantitative estimation of organic pollution in inland waters based on radiative transfer theory. The algorithm was applied to the Feilaixia Basin using Sentinel-2 data. Accuracy assessment results demonstrate good performance in the quantitative assessment of organic pollution, with a coefficient of determination (R²) of 0.79, a mean absolute percentage error (MAPE) of 13.03%, and a root mean square error (RMSE) of 0.39 mg/L. Additionally, a seasonal variation map of organic pollutant concentrations in the Feilaixia Basin was generated, providing valuable scientific support for regional water quality monitoring and management. Full article

(This article belongs to the Section Environmental Sensing)

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14 pages, 9227 KB

Open AccessEditor’s ChoiceArticle

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

Cited by 1 | Viewed by 2932

Abstract

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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18 pages, 3766 KB

Open AccessEditor’s ChoiceArticle

Self-Supervised Multiscale Contrastive and Attention-Guided Gradient Projection Network for Pansharpening

by Qingping Li, Xiaomin Yang, Bingru Li and Jin Wang

Sensors 2025, 25(8), 2560; https://doi.org/10.3390/s25082560 - 18 Apr 2025

Cited by 2 | Viewed by 1609

Abstract

Pansharpening techniques are crucial in remote sensing image processing, with deep learning emerging as the mainstream solution. In this paper, the pansharpening problem is formulated as two optimization subproblems with a solution proposed based on multiscale contrastive learning combined with attention-guided gradient projection [...] Read more.

Pansharpening techniques are crucial in remote sensing image processing, with deep learning emerging as the mainstream solution. In this paper, the pansharpening problem is formulated as two optimization subproblems with a solution proposed based on multiscale contrastive learning combined with attention-guided gradient projection networks. First, an efficient and generalized Spectral–Spatial Universal Module (SSUM) is designed and applied to spectral and spatial enhancement modules (SpeEB and SpaEB). Then, the multiscale high-frequency features of PAN and MS images are extracted using discrete wavelet transform (DWT). These features are combined with contrastive learning and residual connection to progressively balance spectral and spatial information. Finally, high-resolution multispectral images are generated through multiple iterations. Experimental results verify that the proposed method outperforms existing approaches in both visual quality and quantitative evaluation metrics. Full article

(This article belongs to the Section Sensor Networks)

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36 pages, 10690 KB

Open AccessEditor’s ChoiceArticle

Novel Amperometric Sensor Based on Glassy Graphene for Flow Injection Analysis

by Ramtin Eghbal Shabgahi, Alexander Minkow, Michael Wild, Dietmar Kissinger and Alberto Pasquarelli

Sensors 2025, 25(8), 2454; https://doi.org/10.3390/s25082454 - 13 Apr 2025

Cited by 4 | Viewed by 1472

Abstract

Flow injection analysis (FIA) is widely used in drug screening, neurotransmitter detection, and water analysis. In this study, we investigated the electrochemical sensing performance of glassy graphene electrodes derived from pyrolyzed positive photoresist films (PPFs) via rapid thermal annealing (RTA) on SiO₂ [...] Read more.

Flow injection analysis (FIA) is widely used in drug screening, neurotransmitter detection, and water analysis. In this study, we investigated the electrochemical sensing performance of glassy graphene electrodes derived from pyrolyzed positive photoresist films (PPFs) via rapid thermal annealing (RTA) on SiO₂/Si and polycrystalline diamond (PCD). Glassy graphene films fabricated at 800, 900, and 950 °C were characterized using Raman spectroscopy, scanning electron microscopy (SEM), and atomic force microscopy (AFM) to assess their structural and morphological properties. Electrochemical characterization in phosphate-buffered saline (PBS, pH 7.4) revealed that annealing temperature and substrate type influence the potential window and double-layer capacitance. The voltammetric response of glassy graphene electrodes was further evaluated using the surface-insensitive [Ru(NH₃)₆]^3+/2+ redox marker, the surface-sensitive [Fe(CN)₆]^3−/4− redox couple, and adrenaline, demonstrating that electron transfer efficiency is governed by annealing temperature and substrate-induced microstructural changes. FIA with amperometric detection showed a linear electrochemical response to adrenaline in the 3–300 µM range, achieving a low detection limit of 1.05 µM and a high sensitivity of 1.02 µA cm⁻²/µM. These findings highlight the potential of glassy graphene as a cost-effective alternative for advanced electrochemical sensors, particularly in biomolecule detection and analytical applications. Full article

(This article belongs to the Special Issue Electrochemical Sensors and Biosensors: Materials, Methods and Applications)

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27 pages, 5073 KB

Open AccessEditor’s ChoiceReview

A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production

by Meng Lv, Yi-Xiao Xu, Yu-Hang Miao and Wen-Hao Su

Sensors 2025, 25(8), 2433; https://doi.org/10.3390/s25082433 - 12 Apr 2025

Cited by 4 | Viewed by 5508

Abstract

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for [...] Read more.

The high nutritional and medicinal value of apples has contributed to their widespread cultivation worldwide. Unfavorable factors in the healthy growth of trees and extensive orchard work are threatening the profitability of apples. This study reviewed deep learning combined with computer vision for monitoring apple tree growth and fruit production processes in the past seven years. Three types of deep learning models were used for real-time target recognition tasks: detection models including You Only Look Once (YOLO) and faster region-based convolutional network (Faster R-CNN); classification models including Alex network (AlexNet) and residual network (ResNet); segmentation models including segmentation network (SegNet), and mask regional convolutional neural network (Mask R-CNN). These models have been successfully applied to detect pests and diseases (located on leaves, fruits, and trunks), organ growth (including fruits, apple blossoms, and branches), yield, and post-harvest fruit defects. This study introduced deep learning and computer vision methods, outlined in the current research on these methods for apple tree growth and fruit production. The advantages and disadvantages of deep learning were discussed, and the difficulties faced and future trends were summarized. It is believed that this research is important for the construction of smart apple orchards. Full article

(This article belongs to the Section Smart Agriculture)

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25 pages, 12941 KB

Open AccessEditor’s ChoiceArticle

Dynamic Multibody Modeling of Spherical Roller Bearings with Localized Defects for Large-Scale Rotating Machinery

by Luca Giraudo, Luigi Gianpio Di Maggio, Lorenzo Giorio and Cristiana Delprete

Sensors 2025, 25(8), 2419; https://doi.org/10.3390/s25082419 - 11 Apr 2025

Cited by 5 | Viewed by 1769

Abstract

Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink^® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The [...] Read more.

Early fault detection in rotating machinery is crucial for optimizing maintenance and minimizing downtime costs, especially in medium-to-large-scale industrial applications. This study presents a multibody model developed in the Simulink^® Simscape environment to simulate the dynamic behavior of medium-sized spherical bearings. The model includes descriptions of the six degrees of freedoms of each subcomponent, and was validated by comparison with experimental measurements acquired on a test rig capable of applying heavy radial loads. The results show a good fit between experimental and simulated signals in terms of identifying characteristic fault frequencies, which highlights the model’s ability to reproduce vibrations induced by localized defects on the inner and outer races. Amplitude differences can be attributed to simplifications such as neglected housing compliancies and lubrication effects, and do not alter the model’s effectiveness in detecting fault signatures. In conclusion, the developed model represents a promising tool for generating useful datasets for training diagnostic and prognostic algorithms, thereby contributing to the improvement of predictive maintenance strategies in industrial settings. Despite some amplitude discrepancies, the model proves useful for generating fault data and supporting condition monitoring strategies for industrial machinery. Full article

(This article belongs to the Special Issue Feature Papers in Fault Diagnosis & Sensors 2025)

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19 pages, 1385 KB

Open AccessEditor’s ChoiceArticle

Optimizing Sensor Placement for Event Detection: A Case Study in Gaseous Chemical Detection

by Priscile Fogou Suawa and Christian Herglotz

Sensors 2025, 25(8), 2397; https://doi.org/10.3390/s25082397 - 10 Apr 2025

Cited by 1 | Viewed by 2343

Abstract

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor [...] Read more.

In dynamic industrial environments, strategic sensor placement is key to accurately monitoring equipment and detecting critical events. Despite progress in Industry 4.0 and the Internet of Things, research on optimal sensor placement remains limited. This study addresses this gap by analyzing how sensor placement impacts event detection, using chemical detection as a case study with an open dataset. Detecting gases is challenging due to their dispersion. Effective algorithms and well-planned sensor locations are required for reliable results. Using deep convolutional neural networks (DCNNs) and decision tree (DT) methods, we implemented and tested detection models on a public dataset of chemical substances collected at five locations. In addition, we also implemented a multi-objective optimization approach based on the non-dominated sorting genetic algorithm II (NSGA-II) to identify optimal sensor configurations that balance high detection accuracy with cost efficiency in sensor deployment. Using the refined sensor placement, the DCNN model achieved 100% accuracy using only 30% of the available sensors. Full article

(This article belongs to the Special Issue Innovative Technologies and Applications in Engineering Sensing Through Deep and Machine Learning)

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17 pages, 11621 KB

Open AccessEditor’s ChoiceArticle

An Automated Algorithm for Obstructive Sleep Apnea Detection Using a Wireless Abdomen-Worn Sensor

by Thi Hang Dang, Seong-mun Kim, Min-seong Choi, Sung-nam Hwan, Hyung-ki Min and Franklin Bien

Sensors 2025, 25(8), 2412; https://doi.org/10.3390/s25082412 - 10 Apr 2025

Cited by 2 | Viewed by 4546

Abstract

Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This [...] Read more.

Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). Thirty-seven subjects underwent overnight monitoring using an HSAT device and the Soomirang system simultaneously. Normal and apnea events were classified using an MLP-Mixer deep learning model based on Soomirang data, which was also used to estimate total sleep time (ST). Pearson correlation and Bland–Altman analyses were conducted to evaluate the agreement of ST and the apnea–hypopnea index (AHI) calculated by the HSAT device and Soomirang. ST demonstrated a correlation of 0.9 with an average time difference of 7.5 min, while AHI showed a correlation of 0.95 with an average AHI difference of 3. The accuracy, sensitivity, and specificity of the Soomirang for detecting OSA were 97.14%, 100%, and 95.45% at AHI ≥ 15, respectively. The proposed algorithm, utilizing data from a wireless abdomen-worn device exhibited excellent performance in detecting moderate to severe OSA. The findings underscored the potential of a simple device as an accessible and effective tool for OSA screening and follow-up. Full article

(This article belongs to the Section Wearables)

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22 pages, 12622 KB

Open AccessEditor’s ChoiceArticle

Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons

by Giorgos Marinou, Ibrahima Kourouma and Katja Mombaur

Sensors 2025, 25(8), 2379; https://doi.org/10.3390/s25082379 - 9 Apr 2025

Cited by 3 | Viewed by 3803

Abstract

With rapid advancements in lower-limb exoskeleton hardware, two key challenges persist: the accurate assessment of user biomechanics and the reliable control of device behavior in real-world settings. This study presents a modular, sensor-based system designed to enhance both biomechanical evaluation and control of [...] Read more.

With rapid advancements in lower-limb exoskeleton hardware, two key challenges persist: the accurate assessment of user biomechanics and the reliable control of device behavior in real-world settings. This study presents a modular, sensor-based system designed to enhance both biomechanical evaluation and control of lower-limb exoskeletons, leveraging advanced sensor technologies and fuzzy logic. The system addresses the limitations of traditional lab-bound, high-cost methods by integrating inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components work independently or in unison to capture critical biomechanical metrics, including the anteroposterior center of pressure and crutch ground reaction forces. Data are processed in real time by a central unit using fuzzy logic algorithms to estimate gait phases and support exoskeleton control. Validation experiments with three participants, benchmarked against motion capture and force plate systems, demonstrate the system’s ability to reliably detect gait phases and accurately measure biomechanical parameters. By offering an open-source, cost-effective design, this work contributes to the advancement of wearable robotics and promotes broader innovation and accessibility in exoskeleton research. Full article

(This article belongs to the Special Issue Wearable Robotics and Assistive Devices)

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24 pages, 5550 KB

Open AccessEditor’s ChoiceArticle

Energy-Aware Edge Infrastructure Traffic Management Using Programmable Data Planes in 5G and Beyond

by Jorge Andrés Brito, José Ignacio Moreno and Luis M. Contreras

Sensors 2025, 25(8), 2375; https://doi.org/10.3390/s25082375 - 9 Apr 2025

Cited by 2 | Viewed by 2463

Abstract

Next-generation networks, particularly 5G and beyond, face rising energy demands that pose both economic and environmental challenges. In this work, we present a traffic management scheme leveraging programmable data planes and an SDN controller to achieve energy proportionality, matching network resource usage to [...] Read more.

Next-generation networks, particularly 5G and beyond, face rising energy demands that pose both economic and environmental challenges. In this work, we present a traffic management scheme leveraging programmable data planes and an SDN controller to achieve energy proportionality, matching network resource usage to fluctuating traffic loads. This approach integrates flow monitoring on programmable switches with a dynamic power manager in the controller, which selectively powers off inactive switches. We evaluate this scheme in an emulated edge environment across multiple urban traffic profiles. Our results show that disabling switches not handling traffic can significantly reduce energy consumption, even under relatively subtle load variations, while maintaining normal network operations and minimizing overhead on the control plane. We further include a projected savings analysis illustrating the potential benefits if the solution is deployed on hardware devices such as Tofino-based switches. Overall, these findings highlight how data plane-centric, energy-aware traffic management can make 5G-and-beyond edge infrastructures both sustainable and adaptable for future networking needs. Full article

(This article belongs to the Special Issue Energy-Efficient Communication Networks and Systems: 2nd Edition)

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24 pages, 23606 KB

Open AccessEditor’s ChoiceArticle

Improved RRT*-Connect Manipulator Path Planning in a Multi-Obstacle Narrow Environment

by Xueyi He, Yimin Zhou, Haonan Liu and Wanfeng Shang

Sensors 2025, 25(8), 2364; https://doi.org/10.3390/s25082364 - 8 Apr 2025

Cited by 8 | Viewed by 4149

Abstract

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling [...] Read more.

This paper proposes an improved RRT*-Connect algorithm (IRRT*-Connect) for robotic arm path planning in narrow environments with multiple obstacles. A heuristic sampling strategy is adopted with the integration of the ellipsoidal subset sampling and goal-biased sampling strategies, which can continuously compress the sampling space to enhance the sampling efficiency. During the node expansion process, an adaptive step-size method is introduced to dynamically adjust the step size based on the obstacle information, while a node rejection strategy is used to accelerate the search process so as to generate a near-optimal collision-free path. A pruning optimization strategy is also proposed to eliminate the redundant nodes from the path. Furthermore, a cubic non-uniform B-spline interpolation algorithm is applied to smooth the generated path. Finally, simulation experiments of the IRRT*-Connect algorithm are conducted in Python and ROS, and physical experiments are performed on a UR5 robotic arm. By comparing with the existing algorithms, it is demonstrated that the proposed method can achieve shorter planning times and lower path costs of the manipulator operation. Full article

(This article belongs to the Special Issue Advances in Bionic Tactile Systems for Sensor Application and Flexible Operation)

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16 pages, 4785 KB

Open AccessEditor’s ChoiceArticle

Fabrication and Characterization of a Flexible Non-Enzymatic Electrochemical Glucose Sensor Using a Cu Nanoparticle/Laser-Induced Graphene Fiber/Porous Laser-Induced Graphene Network Electrode

by Taeheon Kim and James Jungho Pak

Sensors 2025, 25(7), 2341; https://doi.org/10.3390/s25072341 - 7 Apr 2025

Cited by 5 | Viewed by 2712

Abstract

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using [...] Read more.

We demonstrate a flexible electrochemical biosensor for non-enzymatic glucose detection under different bending conditions. The novel flexible glucose sensor consists of a Cu nanoparticle (NP)/laser-induced graphene fiber (LIGF)/porous laser-induced graphene (LIG) network structure on a polyimide film. The bare LIGF/LIG electrode fabricated using an 8.9 W laser power shows a measured sheet resistance and thickness of 6.8 Ω/□ and ~420 μm, respectively. In addition, a conventional Cu NP electroplating method is used to fabricate a Cu/LIGF/LIG electrode-based glucose sensor that shows excellent glucose detection characteristics, including a sensitivity of 1438.8 µA/mM∙cm², a limit of detection (LOD) of 124 nM, and a broad linear range at an applied potential of +600 mV. Significantly, the Cu/LIGF/LIG electrode-based glucose sensor exhibits a relatively high sensitivity, low LOD, good linear detection range, and long-term stability at bending angles of 0°, 45°, 90°, 135°, and 180°. Full article

(This article belongs to the Section Chemical Sensors)

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50 pages, 7835 KB

Open AccessEditor’s ChoiceArticle

Enhancing Connected Health Ecosystems Through IoT-Enabled Monitoring Technologies: A Case Study of the Monit4Healthy System

by Marilena Ianculescu, Victor-Ștefan Constantin, Andreea-Maria Gușatu, Mihail-Cristian Petrache, Alina-Georgiana Mihăescu, Ovidiu Bica and Adriana Alexandru

Sensors 2025, 25(7), 2292; https://doi.org/10.3390/s25072292 - 4 Apr 2025

Cited by 15 | Viewed by 3399

Abstract

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, [...] Read more.

The Monit4Healthy system is an IoT-enabled health monitoring solution designed to address critical challenges in real-time biomedical signal processing, energy efficiency, and data transmission. The system’s modular design merges wireless communication components alongside a number of physiological sensors, including galvanic skin response, electromyography, photoplethysmography, and EKG, to allow for the remote gathering and evaluation of health information. In order to decrease network load and enable the quick identification of abnormalities, edge computing is used for real-time signal filtering and feature extraction. Flexible data transmission based on context and available bandwidth is provided through a hybrid communication approach that includes Bluetooth Low Energy and Wi-Fi. Under typical monitoring scenarios, laboratory testing shows reliable wireless connectivity and ongoing battery-powered operation. The Monit4Healthy system is appropriate for scalable deployment in connected health ecosystems and portable health monitoring due to its responsive power management approaches and structured data transmission, which improve the resiliency of the system. The system ensures the reliability of signals whilst lowering latency and data volume in comparison to conventional cloud-only systems. Limitations include the requirement for energy profiling, distinctive hardware miniaturizing, and sustained real-world validation. By integrating context-aware processing, flexible design, and effective communication, the Monit4Healthy system complements existing IoT health solutions and promotes better integration in clinical and smart city healthcare environments. Full article

(This article belongs to the Special Issue Advancements and Challenges in IoT Communication Technologies for a Connected World)

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46 pages, 2791 KB

Open AccessEditor’s ChoiceReview

YOLO Object Detection for Real-Time Fabric Defect Inspection in the Textile Industry: A Review of YOLOv1 to YOLOv11

by Makara Mao and Min Hong

Sensors 2025, 25(7), 2270; https://doi.org/10.3390/s25072270 - 3 Apr 2025

Cited by 42 | Viewed by 9675

Abstract

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review [...] Read more.

Automated fabric defect detection is crucial for improving quality control, reducing manual labor, and optimizing efficiency in the textile industry. Traditional inspection methods rely heavily on human oversight, which makes them prone to subjectivity, inefficiency, and inconsistency in high-speed manufacturing environments. This review systematically examines the evolution of the You Only Look Once (YOLO) object detection framework from YOLO-v1 to YOLO-v11, emphasizing architectural advancements such as attention-based feature refinement and Transformer integration and their impact on fabric defect detection. Unlike prior studies focusing on specific YOLO variants, this work comprehensively compares the entire YOLO family, highlighting key innovations and their practical implications. We also discuss the challenges, including dataset limitations, domain generalization, and computational constraints, proposing future solutions such as synthetic data generation, federated learning, and edge AI deployment. By bridging the gap between academic advancements and industrial applications, this review is a practical guide for selecting and optimizing YOLO models for fabric inspection, paving the way for intelligent quality control systems. Full article

(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems)

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42 pages, 7190 KB

Open AccessEditor’s ChoiceReview

Recent Advances in Nanomaterial-Based Self-Healing Electrodes Towards Sensing and Energy Storage Applications

by Oresegun Olakunle Ibrahim, Chen Liu, Shulan Zhou, Bo Jin, Zhaotao He, Wenjie Zhao, Qianqian Wang and Sheng Zhang

Sensors 2025, 25(7), 2248; https://doi.org/10.3390/s25072248 - 2 Apr 2025

Cited by 11 | Viewed by 4354

Abstract

Nanomaterial-based self-healing electrodes have demonstrated significant potential in sensing and energy storage applications due to their ability to withstand electrical breakdowns at high electric fields. However, such electrodes often face mechanical challenges, such as cracking under stress, compromising stability and reliability. This review [...] Read more.

Nanomaterial-based self-healing electrodes have demonstrated significant potential in sensing and energy storage applications due to their ability to withstand electrical breakdowns at high electric fields. However, such electrodes often face mechanical challenges, such as cracking under stress, compromising stability and reliability. This review critically examines nanomaterial-based self-healing mechanisms, focusing on properties and applications in health monitoring, motion sensing, environmental monitoring, and energy storage. By comprehensively reviewing research conducted on dimension-based nanomaterials (OD, 1D, 2D, and 3D) for self-healing electrode applications, this paper aims to provide essential insights into design strategies and performance enhancements afforded by nanoscale dimensions. This review paper highlights the tremendous potential of harnessing dimensional nanomaterials to develop autonomously restoring electrodes for next-generation sensing and energy devices. Full article

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

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13 pages, 3659 KB

Open AccessEditor’s ChoiceArticle

A Non-Contact Privacy Protection Bed Angle Estimation Method Based on LiDAR

by Yezhao Ju, Yuanji Li, Haiyang Zhang, Le Xin, Changming Zhao and Ziyi Xu

Sensors 2025, 25(7), 2226; https://doi.org/10.3390/s25072226 - 2 Apr 2025

Viewed by 3210

Abstract

Accurate bed angle monitoring is crucial in healthcare settings, particularly in Intensive Care Units (ICUs), where improper bed positioning can lead to severe complications such as ventilator-associated pneumonia. Traditional camera-based solutions, while effective, often raise significant privacy concerns. This study proposes a non-intrusive [...] Read more.

Accurate bed angle monitoring is crucial in healthcare settings, particularly in Intensive Care Units (ICUs), where improper bed positioning can lead to severe complications such as ventilator-associated pneumonia. Traditional camera-based solutions, while effective, often raise significant privacy concerns. This study proposes a non-intrusive bed angle detection system based on LiDAR technology, utilizing the Intel RealSense L515 sensor. By leveraging time-of-flight principles, the system enables real-time, privacy-preserving monitoring of head-of-bed elevation angles without direct visual surveillance. Our methodology integrates advanced techniques, including coordinate system transformation, plane fitting, and a deep learning framework combining YOLO-X with an enhanced A2J algorithm. Customized loss functions further improve angle estimation accuracy. Experimental results in ICU environments demonstrate the system’s effectiveness, with an average angle detection error of less than 3 degrees. Full article

(This article belongs to the Section Radar Sensors)

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18 pages, 6709 KB

Open AccessEditor’s ChoiceArticle

Effects of Dust and Moisture Surface Contaminants on Automotive Radar Sensor Frequencies

by Jeongmin Kang, Oskar Hamidi, Karl Vanäs, Tobias Eidevåg, Emil Nilsson and Ross Friel

Sensors 2025, 25(7), 2192; https://doi.org/10.3390/s25072192 - 30 Mar 2025

Cited by 4 | Viewed by 2416

Abstract

Perception and sensing of the surrounding environment are crucial for ensuring the safety of autonomous driving systems. A key issue is securing sensor reliability from sensors mounted on the vehicle and obtaining accurate raw data. Surface contamination in front of a sensor typically [...] Read more.

Perception and sensing of the surrounding environment are crucial for ensuring the safety of autonomous driving systems. A key issue is securing sensor reliability from sensors mounted on the vehicle and obtaining accurate raw data. Surface contamination in front of a sensor typically occurs due to adverse weather conditions or particulate matter on the road, which can degrade system reliability depending on sensor placement and surrounding bodywork geometry. Moreover, the moisture content of dust contaminants can cause surface adherence, making it more likely to persist on a vertical sensor surface compared to moisture only. In this work, a 76–81 GHz radar sensor, a 72–82 GHz automotive radome tester, a 60–90 GHz vector network analyzer system, and a 76–81 GHz radar target simulator setup were used in combination with a representative polypropylene plate that was purposefully contaminated with a varying range of water and ISO standard dust combinations; this was used to determine any signal attenuation and subsequent impact on target detection. The results show that the water content in dust contaminants significantly affects radar signal transmission and object detection performance, with higher water content levels causing increased signal attenuation, impacting detection capability across all tested scenarios. Full article

(This article belongs to the Section Radar Sensors)

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24 pages, 6826 KB

Open AccessEditor’s ChoiceArticle

Preparation of NiO NWs by Thermal Oxidation for Highly Selective Gas-Sensing Applications

by Marwa Ben Arbia, Sung-Ho Kim, Jun-Bo Yoon and Elisabetta Comini

Sensors 2025, 25(7), 2075; https://doi.org/10.3390/s25072075 - 26 Mar 2025

Cited by 5 | Viewed by 2450

Abstract

This paper presents a novel approach for fabricating porous NiO films decorated with nanowires, achieved through sputtering followed by thermal oxidation of a metallic layer. Notably, we successfully fabricate NiO nanowires using this simple and cost-effective method, demonstrating its potential applicability in the [...] Read more.

This paper presents a novel approach for fabricating porous NiO films decorated with nanowires, achieved through sputtering followed by thermal oxidation of a metallic layer. Notably, we successfully fabricate NiO nanowires using this simple and cost-effective method, demonstrating its potential applicability in the gas-sensing field. Furthermore, by using the film of our nanowires, we are able to easily prepare NiO sensors and deposit the required Pt electrodes directly on the film. This is a key advantage, as it simplifies the fabrication process and makes it easier to integrate the sensors into practical gas-sensing devices without the need for nanostructure transfer or intricate setups. Scanning electron microscopy (SEM) reveals the porous structure and nanowire formation, while X-ray diffraction (XRD) confirms the presence of the NiO phase. As a preliminary investigation, the gas-sensing properties of NiO films with varying thicknesses were evaluated at different operating temperatures. The results indicate that thinner layers exhibit superior performances. Gas measurements confirm the p-type nature of the NiO samples, with sensors showing high responsiveness and selectivity toward NO₂ at an optimal temperature of 200 °C. However, incomplete recovery is observed due to the high binding energy of NO₂ molecules. At higher temperatures, sufficient activation energy enables a full sensor recovery but with reduced response. The paper discusses the adsorption–desorption reaction mechanisms on the NiO surface, examines how moisture impacts the enhanced responsiveness of Pt-NiO (2700%) and Au-NiO (400%) sensors, and highlights the successful fabrication of NiO nanowires through a simple and cost-effective method, presenting a promising alternative to more complex approaches. Full article

(This article belongs to the Special Issue Nanomaterials for Chemical Sensors 2023)

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25 pages, 40263 KB

Open AccessEditor’s ChoiceArticle

Autonomous Navigation of Mobile Robots: A Hierarchical Planning–Control Framework with Integrated DWA and MPC

by Zhongrui Wang, Shuting Wang, Yuanlong Xie, Tifan Xiong and Chao Wang

Sensors 2025, 25(7), 2014; https://doi.org/10.3390/s25072014 - 23 Mar 2025

Cited by 5 | Viewed by 2249

Abstract

In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework [...] Read more.

In human–robot collaborative environments, the inherent complexity of shared operational spaces imposes dual requirements on process safety and task execution efficiency. To address the limitations of conventional approaches that decouple planning and control modules, we propose a hierarchical planning–control framework. The proposed framework explicitly incorporates path tracking constraints during path generation while simultaneously considering path characteristics in the control process. The framework comprises two principal components: (1) an enhanced Dynamic Window Approach (DWA) for the local path planning module, introducing adaptive sub-goal selection method and improved path evaluation functions; and (2) a modified Model Predictive Control (MPC) for the path tracking module, with a curvature-based reference state online changing strategy. Comprehensive simulation and real-world experiments demonstrate the framework’s operational advantages over conventional methods. Full article

(This article belongs to the Special Issue Toward Embodied Intelligence: State-of-the-Art in Sensing, Decision-Making and Control Technologies for Autonomous Robots (2nd Edition))

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22 pages, 4867 KB

Open AccessEditor’s ChoiceArticle

Integrating Proximal and Remote Sensing with Machine Learning for Pasture Biomass Estimation

by Bernardo Cândido, Ushasree Mindala, Hamid Ebrahimy, Zhou Zhang and Robert Kallenbach

Sensors 2025, 25(7), 1987; https://doi.org/10.3390/s25071987 - 22 Mar 2025

Cited by 3 | Viewed by 2900

Abstract

This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were combined with vegetation indices (e.g., NDVI, MSAVI2) derived from Landsat 7 [...] Read more.

This study tackles the challenge of accurately estimating pasture biomass by integrating proximal sensing, remote sensing, and machine learning techniques. Field measurements of vegetation height collected using the PaddockTrac ultrasonic sensor were combined with vegetation indices (e.g., NDVI, MSAVI2) derived from Landsat 7 and Sentinel-2 satellite data. We applied the Boruta algorithm for feature selection to identify influential biophysical predictors and evaluated four machine learning models—Linear Regression, Decision Tree, Random Forest, and XGBoost—for biomass prediction. XGBoost consistently performed the best, achieving an R² of 0.86, an MAE of 414 kg ha⁻¹, and an RMSE of 538 kg ha⁻¹ using Landsat 7 data across multiple years. Sentinel-2’s red-edge indices did not substantially improve predictions, suggesting a limited benefit from finer spectral resolutions in this homogenous pasture context. Nonetheless, these indices may offer value in more complex vegetation scenarios. The findings emphasize the effectiveness of combining detailed ground-based measurements with advanced machine learning and remote sensing data, providing a scalable and accurate approach to biomass estimation. This integrated framework provides practical insights for precision agriculture and optimized pasture management, significantly advancing efficient and sustainable rangeland monitoring. Full article

(This article belongs to the Special Issue Remote Sensing of Plants, Soil, and Atmosphere for Agricultural Monitoring)

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29 pages, 4979 KB

Open AccessEditor’s ChoiceArticle

Land Cover Classification Model Using Multispectral Satellite Images Based on a Deep Learning Synergistic Semantic Segmentation Network

by Abdorreza Alavi Gharahbagh, Vahid Hajihashemi, José J. M. Machado and João Manuel R. S. Tavares

Sensors 2025, 25(7), 1988; https://doi.org/10.3390/s25071988 - 22 Mar 2025

Cited by 3 | Viewed by 5249

Abstract

Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and [...] Read more.

Land cover classification (LCC) using satellite images is one of the rapidly expanding fields in mapping, highlighting the need for updating existing computational classification methods. Advances in technology and the increasing variety of applications have introduced challenges, such as more complex classes and a demand for greater detail. In recent years, deep learning and Convolutional Neural Networks (CNNs) have significantly enhanced the segmentation of satellite images. Since the training of CNNs requires sophisticated and expensive hardware and significant time, using pre-trained networks has become widespread in the segmentation of satellite image. This study proposes a hybrid synergistic semantic segmentation method based on the Deeplab v3+ network and a clustering-based post-processing scheme. The proposed method accurately classifies various land cover (LC) types in multispectral satellite images, including Pastures, Other Built-Up Areas, Water Bodies, Urban Areas, Grasslands, Forest, Farmland, and Others. The post-processing scheme includes a spectral bag-of-words model and K-medoids clustering to refine the Deeplab v3+ outputs and correct possible errors. The simulation results indicate that combining the post-processing scheme with deep learning improves the Matthews correlation coefficient (MCC) by approximately 5.7% compared to the baseline method. Additionally, the proposed approach is robust to data imbalance cases and can dynamically update its codewords over different seasons. Finally, the proposed synergistic semantic segmentation method was compared with several state-of-the-art segmentation methods in satellite images of Italy’s Lake Garda (Lago di Garda) region. The results showed that the proposed method outperformed the best existing techniques by at least 6% in terms of MCC. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

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30 pages, 6268 KB

Open AccessEditor’s ChoiceArticle

Cooperative Hybrid Modelling and Dimensionality Reduction for a Failure Monitoring Application in Industrial Systems

by Morgane Suhas, Emmanuelle Abisset-Chavanne and Pierre-André Rey

Sensors 2025, 25(6), 1952; https://doi.org/10.3390/s25061952 - 20 Mar 2025

Cited by 6 | Viewed by 1441

Abstract

Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based [...] Read more.

Failure monitoring of industrial systems is imperative in order to ensure their reliability and competitiveness. This paper presents an innovative hybrid modelling approach applied to DC electric motors, specifically the Kollmorgen AKM42 servomotor. The proposed Cooperative Hybrid Model for Classification (CHMC) combines physics-based and data-driven models to improve fault detection and extrapolation to new usage profiles. The integration of physical knowledge of the healthy behaviour of the motor into a recurrent neural network enhances the accuracy of bearing fault detection by identifying three health states: healthy, progressive fault and stabilised fault. Additionally, Singular Value Decomposition (SVD) is employed for the purposes of feature extraction and dimensionality reduction, thereby enhancing the model’s capacity to generalise with limited training data. The findings of this study demonstrate that a reduction in the input data of 90% preserves the essential information, with an analysis of the first harmonics revealing a narrow frequency range. This elucidates the reason why the first 20 components are sufficient to explain the data variability. The findings reveal that, for usage profiles analogous to the training data, both the CHMC and NHMC models demonstrate comparable performance without reduction. However, the CHMC model exhibits superior performance in detecting true negatives (90% vs. 89%) and differentiating between healthy and failure states. The NHMC model encounters greater difficulty in distinguishing failure states (83.92% vs. 86.56% for progressive failure). When exposed to new usage profiles with increased frequency and amplitude, the CHMC model adapts better, showing superior performance in detecting true positives and handling new data, highlighting its superior extrapolation capabilities. The integration of SVD further reduces input data complexity, and the CHMC model consistently outperforms the NHMC model in these reduced data scenarios, demonstrating the efficacy of combining physical models and dimensionality reduction in enhancing the model’s generalisation, fault detection, and adaptability. This approach has the advantage of reducing the need for retraining, which makes the CHMC model a cost-effective solution for motor fault classification in industrial settings. In conclusion, the CHMC model offers a generalisable method with significant advantages in fault detection, model adaptation, and predictive maintenance performance across varying usage profiles and on unseen operational scenarios. Full article

(This article belongs to the Section Industrial Sensors)

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42 pages, 14097 KB

Open AccessEditor’s ChoiceReview

Microfluidic Biosensors: Enabling Advanced Disease Detection

by Siyue Wang, Xiaotian Guan and Shuqing Sun

Sensors 2025, 25(6), 1936; https://doi.org/10.3390/s25061936 - 20 Mar 2025

Cited by 22 | Viewed by 11873

Abstract

Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to [...] Read more.

Microfluidic biosensors integrate microfluidic and biosensing technologies to achieve the miniaturization, integration, and automation of disease diagnosis, and show great potential for application in the fields of cancer liquid biopsy, pathogenic bacteria detection, and POCT. This paper reviews the recent advances related to microfluidic biosensors in the field of laboratory medicine, focusing on their applications in the above three areas. In cancer liquid biopsy, microfluidic biosensors facilitate the isolation, enrichment, and detection of tumor markers such as CTCs, ctDNA, miRNA, exosomes, and so on, providing support for early diagnosis, precise treatment, and prognostic assessment. In terms of pathogenic bacteria detection, microfluidic biosensors can achieve the rapid, highly sensitive, and highly specific detection of a variety of pathogenic bacteria, helping disease prevention and control as well as public health safety. Pertaining to the realm of POCT, microfluidic biosensors bring the convenient detection of a variety of diseases, such as tumors, infectious diseases, and chronic diseases, to primary health care. Future microfluidic biosensor research will focus on enhancing detection throughput, lowering costs, innovating new recognition elements and signal transduction methods, integrating artificial intelligence, and broadening applications to include home health care, drug discovery, food safety, and so on. Full article

(This article belongs to the Special Issue Recent Advances in Microfluidic Sensing Devices)

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17 pages, 2682 KB

Open AccessEditor’s ChoiceArticle

Ankle Sensor-Based Detection of Freezing of Gait in Parkinson’s Disease in Semi-Free Living Environments

by Juan Daniel Delgado-Terán, Kjell Hilbrants, Dzeneta Mahmutović, Ana Lígia Silva de Lima, Richard J. A. van Wezel and Tjitske Heida

Sensors 2025, 25(6), 1895; https://doi.org/10.3390/s25061895 - 18 Mar 2025

Cited by 6 | Viewed by 2607

Abstract

Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson’s Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural [...] Read more.

Freezing of gait (FOG) is a motor symptom experienced by people with Parkinson’s Disease (PD) where they feel like they are glued to the floor. Accurate and continuous detection is needed for effective cueing to prevent or shorten FOG episodes. A convolutional neural network (CNN) was developed to detect FOG episodes in data recorded from an inertial measurement unit (IMU) on a PD patient’s ankle under semi-free living conditions. Data were split into two sets: one with all movements and another with walking and turning activities relevant to FOG detection. The CNN model was evaluated using five-fold cross-validation (5Fold-CV), leave-one-subject-out cross-validation (LOSO-CV), and performance metrics such as accuracy, sensitivity, precision, F1-score, and AUROC; Data from 24 PD participants were collected, excluding three with no FOG episodes. For walking and turning activities, the CNN model achieved AUROC = 0.9596 for 5Fold-CV and AUROC = 0.9275 for LOSO-CV. When all activities were included, AUROC dropped to 0.8888 for 5Fold-CV and 0.9017 for LOSO-CV; the model effectively detected FOG in relevant movement scenarios but struggled with distinguishing FOG from other inactive states like sitting and standing in semi-free-living environments. Full article

(This article belongs to the Section Wearables)

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17 pages, 2010 KB

Open AccessEditor’s ChoiceArticle

Gaze Estimation Network Based on Multi-Head Attention, Fusion, and Interaction

by Changli Li, Fangfang Li, Kao Zhang, Nenglun Chen and Zhigeng Pan

Sensors 2025, 25(6), 1893; https://doi.org/10.3390/s25061893 - 18 Mar 2025

Cited by 2 | Viewed by 2934

Abstract

Gaze is an externally observable indicator of human visual attention, and thus, recording the gaze position can help to solve many problems. Existing gaze estimation models typically utilize separate neural network branches to process data streams from both eyes and the face, failing [...] Read more.

Gaze is an externally observable indicator of human visual attention, and thus, recording the gaze position can help to solve many problems. Existing gaze estimation models typically utilize separate neural network branches to process data streams from both eyes and the face, failing to fully exploit their feature correlations. This study presents a gaze estimation network that integrates multi-head attention mechanisms, fusion, and interaction strategies to fuse facial features with eye features, as well as features from both eyes, separately. Specifically, multi-head attention and channel attention are used to fuse features from both eyes, and a face and eye interaction module is designed to highlight the most important facial features guided by the eye features; in addition, the channel attention in the Convolutional Block Attention Module (CBAM) is replaced with minimum pooling instead of maximum pooling, and a shortcut connection is added to enhance the network’s attention to eye region details. Comparative experiments on three public datasets—Gaze360, MPIIFaceGaze, and EYEDIAP—validate the superiority of the proposed method. Full article

(This article belongs to the Section Intelligent Sensors)

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21 pages, 6799 KB

Open AccessEditor’s ChoiceArticle

Spatial–Temporal Dynamics of Vegetation Indices in Response to Drought Across Two Traditional Olive Orchard Regions in the Iberian Peninsula

by Nazaret Crespo, Luís Pádua, Paula Paredes, Francisco J. Rebollo, Francisco J. Moral, João A. Santos and Helder Fraga

Sensors 2025, 25(6), 1894; https://doi.org/10.3390/s25061894 - 18 Mar 2025

Cited by 4 | Viewed by 2633

Abstract

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived [...] Read more.

This study investigates the spatial–temporal dynamics of vegetation indices in olive orchards across two traditionally rainfed regions of the Iberian Peninsula, namely the “Trás-os-Montes” (TM) agrarian region in Portugal and the Badajoz (BA) province in Spain, in response to drought conditions. Using satellite-derived vegetation indices, derived from the Harmonized Landsat Sentinel-2 project (HLSL30), such as the Normalized Difference Moisture Index (NDMI) and Soil-Adjusted Vegetation Index (SAVI), this study evaluates the impact of drought periods on olive tree growing conditions. The Mediterranean Palmer Drought Severity Index (MedPDSI), specifically developed for olive trees, was selected to quantify drought severity, and impacts on vegetation dynamics were assessed throughout the study period (2015–2023). The analysis reveals significant differences between the regions, with BA experiencing more intense drought conditions, particularly during the warm season, compared to TM. Seasonal variability in vegetation dynamics is clearly linked to MedPDSI, with lagged responses stronger in the previous two-months. Both the SAVI and the NDMI show vegetation vigour declines during dry seasons, particularly in the years of 2017 and 2022. The findings reported in this study highlight the vulnerability of rainfed olive orchards in BA to long-term drought-induced stress, while TM appears to have slightly higher resilience. The study underscores the value of combining satellite-derived vegetation indices with drought indicators for the effective monitoring of olive groves and to improve water use management practices in response to climate change. These insights are crucial for developing adaptation measures that ensure the sustainability, resiliency, and productivity of rainfed olive orchards in the Iberian Peninsula, particularly under climate change scenarios. Full article

(This article belongs to the Special Issue Advancing Land Monitoring through Synergistic Harmonization of Optical, Radar and Lidar Satellite Technologies)

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