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Sensors, Volume 25, Issue 5 (March-1 2025) – 359 articles

Cover Story (view full-size image): Iron plays a crucial role across environmental, health, and food sciences, yet its accurate quantification remains challenging due to its distinct chemical properties, continuous oxidation-state interconversion, the presence of interfering species, and matrix complexities. This review highlights the latest advancements in mercury-free electrochemical sensors, emphasizing novel electrode materials, nanomaterial modifications, and selective ligands to enhance the sensitivity and selectivity of detection. By addressing persistent challenges and knowledge gaps, this work paves the way for next-generation electrochemical strategies with improved detection capabilities for real-world applications. View this paper
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12 pages, 6370 KiB  
Communication
A 24 GHz End-Fire Rod Antenna Based on a Substrate Integrated Waveguide
by Yanfei Mao, Shiju E, Yu Zhang and Wen-cheng Lai
Sensors 2025, 25(5), 1636; https://doi.org/10.3390/s25051636 - 6 Mar 2025
Viewed by 717
Abstract
Most of the traditional rod antennas in the literature are in the shape of a cylinder or are conical, which are not suitable shapes for planar PCB technology or planar integrated CMOS or BiCMOS technology. In this paper, we present a 24 GHz [...] Read more.
Most of the traditional rod antennas in the literature are in the shape of a cylinder or are conical, which are not suitable shapes for planar PCB technology or planar integrated CMOS or BiCMOS technology. In this paper, we present a 24 GHz planar end-fire rod antenna based on an SIW (substrate integrated waveguide) suitable for planar PCB technology or planar integrated circuit technology. The antenna is made of PCB Rogers 4350 and utilizes the SIW to realize the end-fire rod antenna. The measurement results of the antenna are presented: its gain is 8.55 dB and its S11 bandwidth is 6.2 GHz. This kind of planar end-fire rod antenna possesses the characteristics of high gain, wide bandwidth, compactness, and simple design and structure. This type of antenna can also be used as a PCB antenna in other frequency bands, and it could also possibly be utilized in mm-wave and THz integrated antenna design in the future due to its very simple architecture. Full article
(This article belongs to the Special Issue Waveguide-Based Sensors and Applications)
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16 pages, 20081 KiB  
Article
YOLO-ACE: Enhancing YOLO with Augmented Contextual Efficiency for Precision Cotton Weed Detection
by Qi Zhou, Huicheng Li, Zhiling Cai, Yiwen Zhong, Fenglin Zhong, Xiaoyu Lin and Lijin Wang
Sensors 2025, 25(5), 1635; https://doi.org/10.3390/s25051635 - 6 Mar 2025
Cited by 1 | Viewed by 601
Abstract
Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension [...] Read more.
Effective weed management is essential for protecting crop yields in cotton production, yet conventional deep learning approaches often falter in detecting small or occluded weeds and can be restricted by large parameter counts. To tackle these challenges, we propose YOLO-ACE, an advanced extension of YOLOv5s, which was selected for its optimal balance of accuracy and speed, making it well suited for agricultural applications. YOLO-ACE integrates a Context Augmentation Module (CAM) and Selective Kernel Attention (SKAttention) to capture multi-scale features and dynamically adjust the receptive field, while a decoupled detection head separates classification from bounding box regression, enhancing overall efficiency. Experiments on the CottonWeedDet12 (CWD12) dataset show that YOLO-ACE achieves notable mAP@0.5 and mAP@0.5:0.95 scores—95.3% and 89.5%, respectively—surpassing previous benchmarks. Additionally, we tested the model’s transferability and generalization across different crops and environments using the CropWeed dataset, where it achieved a competitive mAP@0.5 of 84.3%, further showcasing its robust ability to adapt to diverse conditions. These results confirm that YOLO-ACE combines precise detection with parameter efficiency, meeting the exacting demands of modern cotton weed management. Full article
(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)
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25 pages, 7323 KiB  
Review
Application of Zeolite-Based Materials for Chemical Sensing of VOCs
by Dusan Stosic and Vladimir Zholobenko
Sensors 2025, 25(5), 1634; https://doi.org/10.3390/s25051634 - 6 Mar 2025
Viewed by 700
Abstract
Considerable levels of pollution produced by urbanization and industrial development have established a need for monitoring the presence of harmful compounds and the assessment of environmental risks to provide a basis for timely reaction and the prevention of disastrous consequences. Chemical sensors offer [...] Read more.
Considerable levels of pollution produced by urbanization and industrial development have established a need for monitoring the presence of harmful compounds and the assessment of environmental risks to provide a basis for timely reaction and the prevention of disastrous consequences. Chemical sensors offer a reasonable solution; however, the desired properties, such as high sensitivity, selectivity, stability and reliability, ease of fabrication, and cost-effectiveness, are not always easily met. To this end, the incorporation of zeolites in sensor materials has attracted considerable attention. Such hybrid sensor materials exhibit excellent performances due to the unique properties of zeolites, which have been successfully utilized in gas-sensing applications. In this review, we discuss recent findings in the area of the application of zeolites as sensor materials, focusing on the detection of volatile organic compounds and highlighting the role of zeolite frameworks and the proposed mechanisms in the sensing process. Finally, we consider possible future directions for the development of zeolite-based sensor technology, including the application of hierarchical materials, nanosized zeolites, and 2D material–zeolite heterostructures that would fulfill industrial and environmental demands. Full article
(This article belongs to the Special Issue Chemical Sensors—Recent Advances and Future Challenges 2023–2024)
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17 pages, 5833 KiB  
Article
Comparison of Guide to Expression of Uncertainty in Measurement and Monte Carlo Method for Evaluating Gauge Factor Calibration Test Uncertainty of High-Temperature Wire Strain Gauge
by Yazhi Zhao, Fengling Zhang, Yanting Ai, Jing Tian and Zhi Wang
Sensors 2025, 25(5), 1633; https://doi.org/10.3390/s25051633 - 6 Mar 2025
Viewed by 503
Abstract
High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor (GF) is the most critical link. Evaluating the uncertainty of GF [...] Read more.
High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor (GF) is the most critical link. Evaluating the uncertainty of GF is of great significance to the accuracy analysis of measurement results. Firstly, the calibration test of the GF of the Pt-W high-temperature strain gauge was carried out in the range of 25 °C to 900 °C. The real test data required for the uncertainty evaluation were obtained. Secondly, the guide to the expression of uncertainty in measurement (GUM) and the Monte Carlo method (MCM) were used to evaluate the uncertainty of GF calibration test. The evaluation results of GUM and MCM were compared. Finally, the concept of the weight coefficient W was proposed to quantitatively analyze the influence of each input on the uncertainty of the output GF. The main uncertainty source was found, which had important engineering practical significance. The results show that the mean value of GF decreases with the increase in temperature nonlinearly. At 25 °C, GF is 3.29, and at 900 °C, GF decreases to 1.6. Through comparison and verification, the uncertainty interval given by MCM is closer to the real situation. MCM is superior to GUM, which only uses prior information for uncertainty assessment. MCM is more suitable for evaluating GF uncertainty. Among multiple uncertain sources, the weight coefficient W can effectively analyze Δε as the main uncertain source. Full article
(This article belongs to the Special Issue Sensors for High Temperature Monitoring)
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18 pages, 24480 KiB  
Article
A Simple Model for Estimating the Kinematics of Tape-like Unstable Bases from Angular Measurements near Anchor Points
by Heinz Hegi and Ralf Kredel
Sensors 2025, 25(5), 1632; https://doi.org/10.3390/s25051632 - 6 Mar 2025
Viewed by 484
Abstract
Sensorimotor training on an unstable base of support is considered to lead to improvements in balance and coordination tasks. Here, we intend to lay the groundwork for generating cost-effective real-time kinematic feedback for coordination training on devices with an unstable base of support, [...] Read more.
Sensorimotor training on an unstable base of support is considered to lead to improvements in balance and coordination tasks. Here, we intend to lay the groundwork for generating cost-effective real-time kinematic feedback for coordination training on devices with an unstable base of support, such as Sensopros or slacklines, by establishing a model for estimating relevant tape kinematic data from angle measurements alone. To assess the accuracy of the model in a real-world setting, we record a convenience sample of three people performing ten exercises on the Sensopro Luna and compare the model predictions to motion capture data of the tape. The measured accuracy is reported for each target measure separately, namely the roll angle and XYZ-position of the tape segment directly below the foot. After the initial assessment of the model in its general form, we also propose how to adjust the model parameters based on preliminary measurements to adapt it to a specific setting and further improve its accuracy. The results show that the proposed method is viable for recording tape kinematic data in real-world settings, and may therefore serve as a performance indicator directly or form the basis for estimating posture and other measures related to human motor control in a more intricate training feedback system. Full article
(This article belongs to the Special Issue Sensors for Human Posture and Movement)
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17 pages, 4719 KiB  
Article
Synergistic Enhancement of Chemiresistive NO2 Gas Sensors Using Nitrogen-Doped Reduced Graphene Oxide (N-rGO) Decorated with Nickel Oxide (NiO) Nanoparticles: Achieving sub-ppb Detection Limit
by Chiheb Walleni, Mounir Ben Ali, Mohamed Faouzi Ncib and Eduard Llobet
Sensors 2025, 25(5), 1631; https://doi.org/10.3390/s25051631 - 6 Mar 2025
Cited by 1 | Viewed by 1660
Abstract
Detecting low nitrogen dioxide concentrations (NO2) is crucial for environmental monitoring. In this paper, we report the synergistic effect of decorating nitrogen-doped reduced graphene oxide (N-rGO) with nickel oxide (NiO) nanoparticles for developing highly selective and sensitive chemiresistive NO2 gas [...] Read more.
Detecting low nitrogen dioxide concentrations (NO2) is crucial for environmental monitoring. In this paper, we report the synergistic effect of decorating nitrogen-doped reduced graphene oxide (N-rGO) with nickel oxide (NiO) nanoparticles for developing highly selective and sensitive chemiresistive NO2 gas sensors. The N-rGO/NiO sensor was synthesized straightforwardly, ensuring uniform decoration of NiO nanoparticles on the N-rGO surface. Comprehensive characterization using SEM, TEM, XRD, and Raman spectroscopy confirmed the successful integration of NiO nanoparticles with N-rGO and revealed key structural and morphological features contributing to its enhanced sensing performance. As a result, the NiO/N-rGO nanohybrids demonstrate a significantly enhanced response five orders of magnitude higher than that of N-rGO toward low NO2 concentrations (<1 ppm) at 100 °C. Moreover, the present device has an outstanding performance, high sensitivity, and low limit of detection (<1 ppb). The findings pave the way for integrating these sensors into advanced applications, including environmental monitoring and IoT-enabled air quality management systems. Full article
(This article belongs to the Special Issue Recent Advances in Sensors for Chemical Detection Applications)
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11 pages, 3163 KiB  
Article
An Enhanced Bimetallic Optical Fiber SPR Biosensor Using Graphene Oxide for the Label-Free and Sensitive Detection of Human IgG
by Qiang Xu, Huiting Yin, Mei Cui, Renliang Huang and Rongxin Su
Sensors 2025, 25(5), 1630; https://doi.org/10.3390/s25051630 - 6 Mar 2025
Viewed by 579
Abstract
A fiber-reinforced SPR sensor based on silver-nucleated gold-shell bimetallic nanoparticles and graphene oxide was developed and applied to human IgG detection. The refractive index (RI) sensitivity of the Ag@Au/GO fiber SPR sensor is as high as 4715.9 nm/RIU in the RI range of [...] Read more.
A fiber-reinforced SPR sensor based on silver-nucleated gold-shell bimetallic nanoparticles and graphene oxide was developed and applied to human IgG detection. The refractive index (RI) sensitivity of the Ag@Au/GO fiber SPR sensor is as high as 4715.9 nm/RIU in the RI range of 1.333–1.365. Staphylococcus aureus protein A (SPA) can specifically recognize and bind to the fragment crystallizable (Fc) of the antibody; it facilitates the highly targeted immobilization of the antibody. SPA and rabbit anti-human IgG were immobilized on the surface of the Ag@Au/GO fiber SPR sensor for the detection of different concentrations of human IgG with a sensitivity of 0.53 nm/μg/mL and detection limits of 0.037 μg/mL. This biosensor based on the mixed structure of GO and Ag@Au combined the common advantages of the two materials. Therefore, our study provides a simple platform for biological analysis and has a good application prospect. Full article
(This article belongs to the Section Optical Sensors)
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20 pages, 1435 KiB  
Article
Hardware Acceleration-Based Privacy-Aware Authentication Scheme for Internet of Vehicles Using Physical Unclonable Function
by Ujunwa Madububa Mbachu, Rabeea Fatima, Ahmed Sherif, Elbert Dockery, Mohamed Mahmoud, Maazen Alsabaan and Kasem Khalil
Sensors 2025, 25(5), 1629; https://doi.org/10.3390/s25051629 - 6 Mar 2025
Viewed by 772
Abstract
Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the [...] Read more.
Due to technological advancement, the advent of smart cities has facilitated the deployment of advanced urban management systems. This integration has been made possible through the Internet of Vehicles (IoV), a foundational technology. By connecting smart cities with vehicles, the IoV enhances the safety and efficiency of transportation. This interconnected system facilitates wireless communication among vehicles, enabling the exchange of crucial traffic information. However, this significant technological advancement also raises concerns regarding privacy for individual users. This paper presents an innovative privacy-preserving authentication scheme focusing on IoV using physical unclonable functions (PUFs). This scheme employs the k-nearest neighbor (KNN) encryption technique, which possesses a multi-multi searching property. The main objective of this scheme is to authenticate autonomous vehicles (AVs) within the IoV framework. An innovative PUF design is applied to generate random keys for our authentication scheme to enhance security. This two-layer security approach protects against various cyber-attacks, including fraudulent identities, man-in-the-middle attacks, and unauthorized access to individual user information. Due to the substantial amount of information that needs to be processed for authentication purposes, our scheme is implemented using hardware acceleration on an Nexys A7-100T FPGA board. Our analysis of privacy and security illustrates the effective accomplishment of specified design goals. Furthermore, the performance analysis reveals that our approach imposes a minimal communication and computational burden and optimally utilizes hardware resources to accomplish design objectives. The results show that the proposed authentication scheme exhibits a non-linear increase in encryption time with a growing AV ID size, starting at 5μs for 100 bits and rising to 39 μs for 800 bits. Also, the result demonstrates a more gradual, linear increase in the search time with a growing AV ID size, starting at less than 1 μs for 100 bits and rising to less than 8 μs for 800 bits. Additionally, for hardware utilization, our scheme uses only 25% from DSP slides and IO pins, 22.2% from BRAM, 5.6% from flip-flops, and 24.3% from LUTs. Full article
(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)
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27 pages, 27384 KiB  
Article
Adaptive Non-Stationary Fuzzy Time Series Forecasting with Bayesian Networks
by Bo Wang and Xiaodong Liu
Sensors 2025, 25(5), 1628; https://doi.org/10.3390/s25051628 - 6 Mar 2025
Viewed by 470
Abstract
Despite its interpretability and excellence in time series forecasting, the fuzzy time series forecasting model (FTSFM) faces significant challenges when handling non-stationary time series. This paper proposes a novel hybrid non-stationary FTSFM that integrates time-variant FTSFM, Bayesian network (BN), and non-stationary fuzzy sets. [...] Read more.
Despite its interpretability and excellence in time series forecasting, the fuzzy time series forecasting model (FTSFM) faces significant challenges when handling non-stationary time series. This paper proposes a novel hybrid non-stationary FTSFM that integrates time-variant FTSFM, Bayesian network (BN), and non-stationary fuzzy sets. We first apply first-order differencing to extract the fluctuation information of the time series while reducing non-stationarity. A novel time-variant FTSFM updating method is proposed to effectively merge historical knowledge with new observations, enhancing model stability while maintaining sensitivity to time series changes. The updating of fuzzy sets is achieved by incorporating non-stationary fuzzy sets and prediction residuals. Based on updated fuzzy sets, the system reconstructs fuzzy logical relationship groups by combining historical and new data. This approach implements dynamic quantitative modeling of fuzzy relationships between historical and predicted moments, integrating valuable historical temporal fuzzy patterns with emerging temporal fuzzy characteristics. This paper further develops an adaptive BN structure learning method with an adaptive scoring function to update temporal dependence relationships between any two moments while building upon existing dependence relationships. Experimental results indicate that the proposed model significantly outperforms benchmark algorithms. Full article
(This article belongs to the Section Intelligent Sensors)
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12 pages, 8634 KiB  
Article
Industrial Potential of Formaldehyde Gas Sensor Based on PdPt Bimetallic Loaded SnO2 Nanoparticles
by Bing Shen, Tongwei Yuan, Wenshuang Zhang, Xian Tan, Yang Chen and Jiaqiang Xu
Sensors 2025, 25(5), 1627; https://doi.org/10.3390/s25051627 - 6 Mar 2025
Viewed by 1496
Abstract
SnO2-based semiconductor gas-sensing materials are regarded as some of the most crucial sensing materials, owing to their extremely high electron mobility, high sensitivity, and excellent stability. To bridge the gap between laboratory-scale SnO2 and its industrial applications, low-cost and high-efficiency [...] Read more.
SnO2-based semiconductor gas-sensing materials are regarded as some of the most crucial sensing materials, owing to their extremely high electron mobility, high sensitivity, and excellent stability. To bridge the gap between laboratory-scale SnO2 and its industrial applications, low-cost and high-efficiency requirements must be met. This implies the need for simple synthesis techniques, reduced energy consumption, and satisfactory gas-sensing performances. In this study, we utilized a surfactant-free simple method to modify SnO2 nanoparticles with PdPt noble metals, ensuring the stable state of the material. Under the synergistic catalytic effect of Pd and Pt, the composite material (1.0 wt%-PdPt-SnO2) significantly enhanced its response to HCHO. This modification decreased the optimal working temperature to as low as 180 °C to achieve a response value (Ra/Rg = 8.2) and showcased lower operating temperatures, higher sensitivity, and better selectivity to detect 10 ppm of HCHO when compared with pristine SnO2 or single noble metal-decorated SnO2 sensors. Stability tests verified that the gas sensor signals based on PdPt-SnO2 nanoparticles exhibit good reliability. Furthermore, a portable HCHO detector was designed for practical applications, such as in newly purchased cushions, indicating its potential for industrialization beyond the laboratory. Full article
(This article belongs to the Special Issue Gas Sensors: Materials, Mechanisms and Applications: 2nd Edition)
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15 pages, 3116 KiB  
Article
Dielectric Properties of Transformer Resin Under Varying Conditions: Impact on Instrument Transformer Stability and Accuracy
by Simone Vincenzo Suraci, Jizhu Jin, Roberto Tinarelli, Lorenzo Peretto, Davide Fabiani and Alessandro Mingotti
Sensors 2025, 25(5), 1626; https://doi.org/10.3390/s25051626 - 6 Mar 2025
Viewed by 421
Abstract
The accuracy of instrument transformers (ITs) is vital for the accurate measurement of electrical quantities. However, their performance is influenced by various factors during operation, including environmental conditions such as temperature, pressure, and humidity, as well as other factors like positioning, electromagnetic fields, [...] Read more.
The accuracy of instrument transformers (ITs) is vital for the accurate measurement of electrical quantities. However, their performance is influenced by various factors during operation, including environmental conditions such as temperature, pressure, and humidity, as well as other factors like positioning, electromagnetic fields, and geometry. Given that IT accuracy is challenging to verify once installed in the field, it is essential to thoroughly understand its performance beforehand. This paper investigates how variations in resin properties affect IT accuracy. Samples prepared with different curing temperatures were subjected to aging tests, which included exposure to temperature and combined temperature–humidity conditions. Throughout the aging process, the dielectric properties of the samples were measured, and their impact on IT accuracy was evaluated. The results clearly demonstrate that the choice of resin properties is critical to ensure reliable IT performance, as improper selection can lead to significant accuracy deviations. Full article
(This article belongs to the Section Physical Sensors)
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14 pages, 6547 KiB  
Article
Angle of Arrival for the Beam Detection Method of Spatially Distributed Sensor Array
by Shan Zhao, Lei Zhu, Shiyang Shen, Heng Du, Xiangyu Wang, Lei Chen and Xiaodong Wang
Sensors 2025, 25(5), 1625; https://doi.org/10.3390/s25051625 - 6 Mar 2025
Viewed by 497
Abstract
Laser space networks are an important development direction for inter-satellite communication. Detecting the angle of arrival (AOA) of multiple satellites in a wide field of view (FOV) is the key to realize inter-satellite laser communication networking. The traditional AOA detection method based on [...] Read more.
Laser space networks are an important development direction for inter-satellite communication. Detecting the angle of arrival (AOA) of multiple satellites in a wide field of view (FOV) is the key to realize inter-satellite laser communication networking. The traditional AOA detection method based on the lens system has a limited FOV. In this paper, we demonstrate a system that uses a spatially distributed sensor array to detect the AOA in a wide FOV. The basic concept is to detect AOA using the signal strength of each sensor at different spatial angles. An AOA detection model was developed, and the relationship of key structural parameters of the spatially distributed sensor array on the FOV and angular resolution was analyzed. Furthermore, a spatially distributed sensor array prototype consisting of 5 InGaAs PIN photodiodes distributed on a 3D-printed structure with an inclination angle of 30° was developed. In order to improve the angle calculation accuracy, a multi-sensor data fusion algorithm is proposed. The experimental results show that the prototype’s maximum FOV is 110°. The root mean square error (RMSE) for azimuth is 0.6° within a 60° FOV, whereas the RMSE for elevation is 0.67°. The RMSE increases to 1.1° for azimuth and 1.7° for elevation when the FOV expands to 110°. The designed spatially distributed sensor array has the advantages of a wide FOV and low size, weight, and power (SWaP), presenting great potential for multi-satellite laser communication applications. Full article
(This article belongs to the Section Optical Sensors)
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21 pages, 6301 KiB  
Article
A Novel Fault-Tolerant Information Fusion Method for Integrated Navigation Systems Based on Fuzzy Inference
by Yixian Zhu, Minmin Zhang, Ling Zhou and Ting Cai
Sensors 2025, 25(5), 1624; https://doi.org/10.3390/s25051624 - 6 Mar 2025
Viewed by 387
Abstract
To enhance the precision and reliability of integrated navigation systems, a novel fault-tolerant information fusion algorithm based on a federated filter is proposed. Decentralized filtering architecture is employed to fuse information from different navigation subsystems. The chi-square detection function and the filter innovation [...] Read more.
To enhance the precision and reliability of integrated navigation systems, a novel fault-tolerant information fusion algorithm based on a federated filter is proposed. Decentralized filtering architecture is employed to fuse information from different navigation subsystems. The chi-square detection function and the filter innovation correlation are used as inputs to the fuzzy system, which then outputs the observation quality factor. The observation quality factor directly reflects the reliability of the measurement data and is utilized to adjust the local filter gain matrix online. Additionally, the information sharing coefficients, determined by the observation quality factors, ensure dependable fault isolation while improving the sensitivity of fault detection to gradual faults. Comparative experimental results demonstrate that the proposed method effectively detects various faults and significantly enhances the performance of the integrated navigation system during malfunctions. Full article
(This article belongs to the Section Navigation and Positioning)
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14 pages, 4643 KiB  
Article
An Evaluation of the Effect of Dimple Insoles on Foot Temperature in Diabetic Patients
by Asma Aferhane, Hassan Douzi, Rachid Harba, Luis Vilcahuaman, Alejandro J. Almenar-Arasanz, Javier Alfaro-Santafé, Hugo Arbañil, María Teresa Arista and Roozbeh Naemi
Sensors 2025, 25(5), 1623; https://doi.org/10.3390/s25051623 - 6 Mar 2025
Viewed by 504
Abstract
Objective: Insoles play a crucial role in foot comfort, with their effect on foot temperature being a key factor. This study aims to evaluate and compare the effect of walking with two different insole types—dimple insoles versus a conventional insole—on foot temperature changes [...] Read more.
Objective: Insoles play a crucial role in foot comfort, with their effect on foot temperature being a key factor. This study aims to evaluate and compare the effect of walking with two different insole types—dimple insoles versus a conventional insole—on foot temperature changes in patients with diabetic neuropathy. Methods: Thermal imaging was used to measure the foot temperature of nine participants immediately before and after walking 250 m in each insole. Temperature variations were analyzed for the whole foot across four specific regions to assess and compare the effect of each insole on foot temperature. Results: The Wilcoxon Signed-Rank Test revealed that contralateral temperature differences between the left and right feet after walking (∆TAfter) were significantly (p<0.05) lower in dimple insoles compared to the conventional insoles. This effect was particularly strong in the midfoot and toe regions. Conclusions: The results indicate that insole type can influence foot contralateral temperature differences after walking. These findings provide valuable insights for selecting insoles based on thermal data and can have implications in improving patient outcomes. Full article
(This article belongs to the Collection Sensors for Globalized Healthy Living and Wellbeing)
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21 pages, 30213 KiB  
Article
Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region
by Masoud Babadi Ataabadi, Darren Pouliot, Dongmei Chen and Temitope Seun Oluwadare
Sensors 2025, 25(5), 1622; https://doi.org/10.3390/s25051622 - 6 Mar 2025
Viewed by 450
Abstract
The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics [...] Read more.
The Landsat archive stands as one of the most critical datasets for studying landscape change, offering over 50 years of imagery. This invaluable historical record facilitates the monitoring of land cover and land use changes, helping to detect trends in and the dynamics of the Earth’s system. However, the relatively low temporal frequency and irregular clear-sky observations of Landsat data pose significant challenges for multi-temporal analysis. To address these challenges, this research explores the application of a closed-form continuous-depth neural network (CFC) integrated within a recurrent neural network (RNN) called CFC-mmRNN for reconstructing historical Landsat time series in the Canadian Prairies region from 1985 to present. The CFC method was evaluated against the continuous change detection (CCD) method, widely used for Landsat time series reconstruction and change detection. The findings indicate that the CFC method significantly outperforms CCD across all spectral bands, achieving higher accuracy with improvements ranging from 33% to 42% and providing more accurate dense time series reconstructions. The CFC approach excels in handling the irregular and sparse time series characteristic of Landsat data, offering improvements in capturing complex temporal patterns. This study underscores the potential of leveraging advanced deep learning techniques like CFC to enhance the quality of reconstructed satellite imagery, thus supporting a wide range of remote sensing (RS) applications. Furthermore, this work opens up avenues for further optimization and application of CFC in higher-density time series datasets such as MODIS and Sentinel-2, paving the way for improved environmental monitoring and forecasting. Full article
(This article belongs to the Special Issue Application of Satellite Remote Sensing in Geospatial Monitoring)
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11 pages, 4983 KiB  
Article
High-Sensitivity Magnetic Field Sensor Based on an Optoelectronic Oscillator with a Mach–Zehnder Interferometer
by Mingjian Zhu, Pufeng Gao, Shiyi Cai, Naihan Zhang, Beilei Wu, Yan Liu, Bin Yin and Muguang Wang
Sensors 2025, 25(5), 1621; https://doi.org/10.3390/s25051621 - 6 Mar 2025
Viewed by 502
Abstract
A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. [...] Read more.
A high-sensitivity magnetic field sensor based on an optoelectronic oscillator (OEO) with a Mach–Zehnder interferometer (MZI) is proposed and experimentally demonstrated. The magnetic field sensor consists of a fiber Mach–Zehnder interferometer, with the lower arm of the interferometer wound around a magnetostrictive transducer. Due to the magnetostrictive effect, an optical phase shift induced by magnetic field variation is generated between two orthogonal light waves transmitted in the upper and lower arms of the MZI. The polarization-dependent property of a Mach–Zehnder modulator (MZM) is utilized to transform the magnetostrictive phase shift into the phase difference between the sidebands and optical carrier, which is mapped to the oscillating frequency upon the completion of an OEO loop. High-sensitivity magnetic field sensing is achieved by observing the frequency shift of the radio frequency (RF) signal. Temperature-induced cross-sensitivity is mitigated through precise length matching of the MZI arms. In the experiment, the high magnetic field sensitivity of 6.824 MHz/mT with a range of 25 mT to 25.3 mT is achieved and the sensing accuracy measured by an electrical spectrum analyzer (ESA) at “maxhold” mode is 0.002 mT. The proposed sensing structure has excellent magnetic field detection performance and provides a solution for temperature-insensitive magnetic field detection, which would have broad application prospects. Full article
(This article belongs to the Special Issue Advances in Microwave Photonics)
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12 pages, 2699 KiB  
Article
Molecular Shape-Preserving Au Electrode for Progesterone Detection
by Fukuto Soyama, Taisei Motomura and Kenshin Takemura
Sensors 2025, 25(5), 1620; https://doi.org/10.3390/s25051620 - 6 Mar 2025
Viewed by 469
Abstract
Quantifying progesterone levels in the body is an important indicator of early pregnancy and health. Molecular shape-preserving electrodes have garnered attention in electrochemical biosensors because they can detect targets without the need for expensive enzymes or antibodies. However, some of the currently used [...] Read more.
Quantifying progesterone levels in the body is an important indicator of early pregnancy and health. Molecular shape-preserving electrodes have garnered attention in electrochemical biosensors because they can detect targets without the need for expensive enzymes or antibodies. However, some of the currently used methods typically have low electrode durability. Here, progesterone, for which antibodies are typically expensive, was used to develop a molecular shape-preserving electrode using Au to enhance its long-term stability. The physical properties of the electrodes were characterized using scanning electron microscopy (SEM), the electrochemical surface area (ECSA), and cyclic voltammetry (CV). The specific structure of the electrode demonstrated an electrochemical double layer comparable to that of a smooth Au electrode, confirming its high durability. The detection performance was assessed using CV, square wave voltammetry (SWV), and electrochemical impedance spectroscopy (EIS). The current response to progesterone increased in a concentration-dependent manner, but decreased from the saturated state owing to electrodeposition on the surface. Additionally, electrochemical impedance measurements showed high selectivity compared with hormones with similar structures. The fabricated molecular shape-preserving electrode exhibits an excellent durability, stability, and detection performance, confirming its suitability for long-term use. These findings pave the way to new possibilities for electrode fabrication. Full article
(This article belongs to the Special Issue Nanostructures and Nanocrystals for Sensing Studies)
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17 pages, 3625 KiB  
Article
Automated Assessment of Upper Extremity Function with the Modified Mallet Score Using Single-Plane Smartphone Videos
by Cancan Su, Lianne Brandt, Guangwen Sun, Kaitlynn Sampel, Edward D. Lemaire, Kevin Cheung, Albert Tu and Natalie Baddour
Sensors 2025, 25(5), 1619; https://doi.org/10.3390/s25051619 - 6 Mar 2025
Viewed by 507
Abstract
The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were [...] Read more.
The Modified Mallet Score (MMS) is widely used to assess upper limb function but requires evaluation by experienced clinicians. This study automated MMS assessments using smartphone videos, artificial intelligence (AI), and new algorithms. A total of 125 videos covering all MMS grades were recorded from four neurotypical participants. For all recordings, an expert physician provided manual scores as the ground truth. The OpenPose BODY25 model extracted body keypoint data, which were used to calculate joint angles for an automated scoring algorithm. The algorithm’s scores were compared to the ground truth and expert manual scoring. High accuracy was achieved for the global abduction, hand-to-neck, hand-on-spine, and hand-to-mouth movements, with Pearson correlation coefficients (PCCs) > 0.9 and a low root mean square error (RMSE). Although slightly less accurate for global external rotation, the algorithm still showed strong agreement. This study demonstrates the potential of using AI and smartphone videos for reliable, remote upper limb assessments. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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17 pages, 4555 KiB  
Article
Preliminary Study on Wearable Smart Socks with Hydrogel Electrodes for Surface Electromyography-Based Muscle Activity Assessment
by Gabriele Rescio, Elisa Sciurti, Lucia Giampetruzzi, Anna Maria Carluccio, Luca Francioso and Alessandro Leone
Sensors 2025, 25(5), 1618; https://doi.org/10.3390/s25051618 - 6 Mar 2025
Viewed by 621
Abstract
Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require [...] Read more.
Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin–electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin–electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset. Full article
(This article belongs to the Special Issue Wearable Sensors for Gait, Human Motion Analysis and Health Monitoring: 2nd Edition)
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11 pages, 2851 KiB  
Communication
A Method for Enhancing Inventory Efficiency of Densely Stacked Tags in RFID Cabinets
by Chengzhen Ma, Jia Chai, Kaiqi Ren, Tingting Xie, Zhicheng Ruan, Yuzhu Liu, Dan Zhang and Suiping Jiang
Sensors 2025, 25(5), 1617; https://doi.org/10.3390/s25051617 - 6 Mar 2025
Viewed by 390
Abstract
This paper explicitly proposes a novel algorithm to enhance the inventory efficiency of densely stacked tags in a radio frequency identification (RFID) cabinet. By flexibly setting the inventoried flags, tags are not repeatedly inventoried by different interrogator antennas in the RFID cabinet. Comprehensive [...] Read more.
This paper explicitly proposes a novel algorithm to enhance the inventory efficiency of densely stacked tags in a radio frequency identification (RFID) cabinet. By flexibly setting the inventoried flags, tags are not repeatedly inventoried by different interrogator antennas in the RFID cabinet. Comprehensive experiments are conducted to validate the proposed algorithm’s feasibility. The experimental results show that for 560 stacked tags, the proposed algorithm achieves 100% inventory accuracy while reducing inventory time by 40%, thereby significantly enhancing the efficiency of tag inventory management. Full article
(This article belongs to the Section Intelligent Sensors)
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27 pages, 3967 KiB  
Article
Adaptive Super-Twisting Tracking for Uncertain Robot Manipulators Based on the Event-Triggered Algorithm
by Yajun Ma, Hui Zhao and Tao Li
Sensors 2025, 25(5), 1616; https://doi.org/10.3390/s25051616 - 6 Mar 2025
Viewed by 375
Abstract
In this study, the authors present an event-triggered control scheme for uncertain robot manipulators combined with an adaptive super-twisting algorithm to handle uncertain robot manipulator systems with unknown external uncertainties and disturbances. The proposed controller can ensure the system-tracking performance while also guaranteeing [...] Read more.
In this study, the authors present an event-triggered control scheme for uncertain robot manipulators combined with an adaptive super-twisting algorithm to handle uncertain robot manipulator systems with unknown external uncertainties and disturbances. The proposed controller can ensure the system-tracking performance while also guaranteeing the robust stability of the system. First, an event-triggered adaptive super-twisting control (ETASTC) method for multivariable second-order nonlinear systems is proposed. In addition, unlike the implementation of periodic control, in the event-triggered method, the control signal is updated by the requirement of system stability, thus avoiding the frequent periodic execution of control tasks. Furthermore, through rigorous proof, the Zeno free execution of the triggering sequence is also ensured. Lastly, the proposed method is illustrated through numerical simulation and experimental study, and the results show that the computational cost is saved while also ensuring the desired performance of the robot system. Full article
(This article belongs to the Special Issue Dynamics and Control System Design for Robot Manipulation)
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33 pages, 11652 KiB  
Review
Deep-Learning-Based Analysis of Electronic Skin Sensing Data
by Yuchen Guo, Xidi Sun, Lulu Li, Yi Shi, Wen Cheng and Lijia Pan
Sensors 2025, 25(5), 1615; https://doi.org/10.3390/s25051615 - 6 Mar 2025
Viewed by 902
Abstract
E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning [...] Read more.
E-skin is an integrated electronic system that can mimic the perceptual ability of human skin. Traditional analysis methods struggle to handle complex e-skin data, which include time series and multiple patterns, especially when dealing with intricate signals and real-time responses. Recently, deep learning techniques, such as the convolutional neural network, recurrent neural network, and transformer methods, provide effective solutions that can automatically extract data features and recognize patterns, significantly improving the analysis of e-skin data. Deep learning is not only capable of handling multimodal data but can also provide real-time response and personalized predictions in dynamic environments. Nevertheless, problems such as insufficient data annotation and high demand for computational resources still limit the application of e-skin. Optimizing deep learning algorithms, improving computational efficiency, and exploring hardware–algorithm co-designing will be the key to future development. This review aims to present the deep learning techniques applied in e-skin and provide inspiration for subsequent researchers. We first summarize the sources and characteristics of e-skin data and review the deep learning models applicable to e-skin data and their applications in data analysis. Additionally, we discuss the use of deep learning in e-skin, particularly in health monitoring and human–machine interactions, and we explore the current challenges and future development directions. Full article
(This article belongs to the Special Issue Analyzation of Sensor Data with the Aid of Deep Learning)
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24 pages, 4014 KiB  
Article
Calibration of Low-Cost LoRaWAN-Based IoT Air Quality Monitors Using the Super Learner Ensemble: A Case Study for Accurate Particulate Matter Measurement
by Gokul Balagopal, Lakitha Wijeratne, John Waczak, Prabuddha Hathurusinghe, Mazhar Iqbal, Daniel Kiv, Adam Aker, Seth Lee, Vardhan Agnihotri, Christopher Simmons and David J. Lary
Sensors 2025, 25(5), 1614; https://doi.org/10.3390/s25051614 - 6 Mar 2025
Viewed by 642
Abstract
This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective [...] Read more.
This study calibrates an affordable, solar-powered LoRaWAN air quality monitoring prototype using the research-grade Palas Fidas Frog sensor. Motivated by the need for sustainable air quality monitoring in smart city initiatives, this work integrates low-cost, self-sustaining sensors with research-grade instruments, creating a cost-effective hybrid network that enhances both spatial coverage and measurement accuracy. To improve calibration precision, the study leverages the Super Learner machine learning technique, which optimally combines multiple models to achieve robust PM (Particulate Matter) monitoring in low-resource settings. Data was collected by co-locating the Palas sensor and LoRaWAN devices under various climatic conditions to ensure reliability. The LoRaWAN monitor measures PM concentrations alongside meteorological parameters such as temperature, pressure, and humidity. The collected data were calibrated against precise PM concentrations and particle count densities from the Palas sensor. Various regression models were evaluated, with the stacking-based Super Learner model outperforming traditional approaches, achieving an average test R2 value of 0.96 across all target variables, including 0.99 for PM2.5 and 0.91 for PM10.0. This study presents a novel approach by integrating Super Learner-based calibration with LoRaWAN technology, offering a scalable solution for low-cost, high-accuracy air quality monitoring. The findings demonstrate the feasibility of deploying these sensors in urban areas such as the Dallas-Fort Worth metroplex, providing a valuable tool for researchers and policymakers to address air pollution challenges effectively. Full article
(This article belongs to the Section Sensor Networks)
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17 pages, 3529 KiB  
Article
Meta-Transfer-Learning-Based Multimodal Human Pose Estimation for Lower Limbs
by Guoming Du, Haiqi Zhu, Zhen Ding, Hong Huang, Xiaofeng Bie and Feng Jiang
Sensors 2025, 25(5), 1613; https://doi.org/10.3390/s25051613 - 6 Mar 2025
Viewed by 608
Abstract
Accurate and reliable human pose estimation (HPE) is essential in interactive systems, particularly for applications requiring personalized adaptation, such as controlling cooperative robots and wearable exoskeletons, especially for healthcare monitoring equipment. However, continuously maintaining diverse datasets and frequently updating models for individual adaptation [...] Read more.
Accurate and reliable human pose estimation (HPE) is essential in interactive systems, particularly for applications requiring personalized adaptation, such as controlling cooperative robots and wearable exoskeletons, especially for healthcare monitoring equipment. However, continuously maintaining diverse datasets and frequently updating models for individual adaptation are both resource intensive and time-consuming. To address these challenges, we propose a meta-transfer learning framework that integrates multimodal inputs, including high-frequency surface electromyography (sEMG), visual-inertial odometry (VIO), and high-precision image data. This framework improves both accuracy and stability through a knowledge fusion strategy, resolving the data alignment issue, ensuring seamless integration of different modalities. To further enhance adaptability, we introduce a training and adaptation framework with few-shot learning, facilitating efficient updating of encoders and decoders for dynamic feature adjustment in real-time applications. Experimental results demonstrate that our framework provides accurate, high-frequency pose estimations, particularly for intra-subject adaptation. Our approach enables efficient adaptation to new individuals with only a few new samples, providing an effective solution for personalized motion analysis with minimal data. Full article
(This article belongs to the Special Issue Wearable and Unobtrusive Technologies for Healthcare Monitoring—2nd Edition)
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18 pages, 1734 KiB  
Article
How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?
by Ophélie Pila and Christophe Duret
Sensors 2025, 25(5), 1612; https://doi.org/10.3390/s25051612 - 6 Mar 2025
Viewed by 455
Abstract
Upper limb training dose after stroke is usually quantified by time and repetitions. This study analyzed upper limb motor training dose in stroke participants (N = 36) using a more comprehensive approach. Participants, classified by initial motor severity (severe/moderate/mild) and recovery trajectory (good/poor), [...] Read more.
Upper limb training dose after stroke is usually quantified by time and repetitions. This study analyzed upper limb motor training dose in stroke participants (N = 36) using a more comprehensive approach. Participants, classified by initial motor severity (severe/moderate/mild) and recovery trajectory (good/poor), received daily robotic and occupational therapy. Treatment dose was reported using a multidimensional framework. Fugl-Meyer Assessment (FMA) score and robot-derived kinematic parameters (reach distance (cm), velocity (cm/s), accuracy (cm) and smoothness (number of velocity peaks)) were analyzed pre- and post-intervention. FMA scores (mean (SD)) improved significantly post-intervention in severe (+11 (12) pts; p < 0.001) and moderate (+13 (6) pts; p ≤ 0.01) impairment groups. In the severe group, good recoverers showed greater improvement (+18 (12) pts) than poor recoverers (+4 (4) pts). Despite similar robotic therapy duration (34 min/session) and number of movements (600–900/session) between good and poor recoverers, both groups experienced very different therapeutic plans in the use of physical modalities: good recoverers gradually moved from assisted to the unassisted then resisted modality. Kinematic analysis showed distinct patterns of motor improvement across severity levels, ranging from quantitative (reach distance/velocity) to qualitative (accuracy/smoothness) changes. This approach provides a more accurate description of the therapeutic dose by characterizing the movements actually performed and can help personalize rehabilitation strategies. Full article
(This article belongs to the Special Issue Assistive and Rehabilitation Technologies Based on Intelligent Sensors)
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32 pages, 6211 KiB  
Article
Mechanical Structure Design and Motion Simulation Analysis of a Lower Limb Exoskeleton Rehabilitation Robot Based on Human–Machine Integration
by Chenglong Zhao, Zhen Liu, Yuefa Ou and Liucun Zhu
Sensors 2025, 25(5), 1611; https://doi.org/10.3390/s25051611 - 6 Mar 2025
Viewed by 713
Abstract
Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing [...] Read more.
Population aging is an inevitable trend in contemporary society, and the application of technologies such as human–machine interaction, assistive healthcare, and robotics in daily service sectors continues to increase. The lower limb exoskeleton rehabilitation robot has great potential in areas such as enhancing human physical functions, rehabilitation training, and assisting the elderly and disabled. This paper integrates the structural characteristics of the human lower limb, motion mechanics, and gait features to design a biomimetic exoskeleton structure and proposes a human–machine integrated lower limb exoskeleton rehabilitation robot. Human gait data are collected using the Optitrack optical 3D motion capture system. SolidWorks 3D modeling software Version 2021 is used to create a virtual prototype of the exoskeleton, and kinematic analysis is performed using the standard Denavit–Hartenberg (D-H) parameter method. Kinematic simulations are carried out using the Matlab Robotic Toolbox Version R2018a with the derived D-H parameters. A physical prototype was fabricated and tested to verify the validity of the structural design and gait parameters. A controller based on BP fuzzy neural network PID control is designed to ensure the stability of human walking. By comparing two sets of simulation results, it is shown that the BP fuzzy neural network PID control outperforms the other two control methods in terms of overshoot and settling time. The specific conclusions are as follows: after multiple walking gait tests, the robot’s walking process proved to be relatively safe and stable; when using BP fuzzy neural network PID control, there is no significant oscillation, with an overshoot of 5.5% and a settling time of 0.49 s, but the speed was slow, with a walking speed of approximately 0.18 m/s, a stride length of about 32 cm, and a gait cycle duration of approximately 1.8 s. The model proposed in this paper can effectively assist patients in recovering their ability to walk. However, the lower limb exoskeleton rehabilitation robot still faces challenges, such as a slow speed, large size, and heavy weight, which need to be optimized and improved in future research. Full article
(This article belongs to the Section Sensors and Robotics)
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27 pages, 2191 KiB  
Article
Detection of Anomalies in Data Streams Using the LSTM-CNN Model
by Agnieszka Duraj, Piotr S. Szczepaniak and Artur Sadok
Sensors 2025, 25(5), 1610; https://doi.org/10.3390/s25051610 - 6 Mar 2025
Viewed by 785
Abstract
This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models [...] Read more.
This paper presents a comparative analysis of selected deep learning methods applied to anomaly detection in data streams. The anomaly detection results obtained on the popular Yahoo! Webscope S5 dataset are used for the computational experiments. The two commonly used and recommended models in the literature, which are the basis for this analysis, are the following: the LSTM and its more complicated variant, the LSTM autoencoder. Additionally, the usefulness of an innovative LSTM-CNN approach is evaluated. The results indicate that the LSTM-CNN approach can successfully be applied for anomaly detection in data streams as its performance compares favorably with that of the two mentioned standard models. For the performance evaluation, the F1score is used. Full article
(This article belongs to the Section Intelligent Sensors)
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13 pages, 4221 KiB  
Article
A Method to Address the Impact of Incident Conditions on the Spectral Reconstruction of the Talbot Wavemeter
by Yiming Wang, Yu Huang, Xiaohu Yang, Zhanfeng Li and Yue Li
Sensors 2025, 25(5), 1609; https://doi.org/10.3390/s25051609 - 6 Mar 2025
Viewed by 405
Abstract
The Talbot wavemeter has attracted widespread attention from researchers in recent years due to its advantages of miniaturization and low cost. However, the impact of varying incident conditions caused by factors such as alignment has remained a challenge for spectral retrieval. This paper [...] Read more.
The Talbot wavemeter has attracted widespread attention from researchers in recent years due to its advantages of miniaturization and low cost. However, the impact of varying incident conditions caused by factors such as alignment has remained a challenge for spectral retrieval. This paper first derives the influence of different incident conditions on the interference pattern based on Fresnel diffraction and verifies the derivation through simulations. We propose a method to address the impact of incident conditions on the interference pattern. By adding a grating with a different periodicity in front of the detector, Moiré fringes are generated in the periodicity dimension, increasing the fringe period and thus enlarging the tolerance for angular misalignment. Finally, we constructed a Talbot wavemeter based on a double-grating structure, achieving a spectral resolution of 9 nm at 360 nm. This method provides a reference for the future development of a high-precision, high-resolution Talbot wavemeter. Full article
(This article belongs to the Section Optical Sensors)
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21 pages, 6656 KiB  
Article
A Flexible PVDF Sensor for Forcecardiography
by Salvatore Parlato, Jessica Centracchio, Eliana Cinotti, Gaetano D. Gargiulo, Daniele Esposito, Paolo Bifulco and Emilio Andreozzi
Sensors 2025, 25(5), 1608; https://doi.org/10.3390/s25051608 - 6 Mar 2025
Viewed by 660
Abstract
Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow [...] Read more.
Forcecardiography (FCG) uses force sensors to record the mechanical vibrations induced on the chest wall by cardiac and respiratory activities. FCG is usually performed via piezoelectric lead-zirconate titanate (PZT) sensors, which simultaneously record the very slow respiratory movements of the chest, the slow infrasonic vibrations due to emptying and filling of heart chambers, the faster infrasonic vibrations due to movements of heart valves, which are usually recorded via Seismocardiography (SCG), and the audible vibrations corresponding to heart sounds, commonly recorded via Phonocardiography (PCG). However, PZT sensors are not flexible and do not adapt very well to the deformations of soft tissues on the chest. This study presents a flexible FCG sensor based on a piezoelectric polyvinylidene fluoride (PVDF) transducer. The PVDF FCG sensor was compared with a well-assessed PZT FCG sensor, as well as with an electro-resistive respiratory band (ERB), an accelerometric SCG sensor, and an electronic stethoscope for PCG. Simultaneous recordings were acquired with these sensors and an electrocardiography (ECG) monitor from a cohort of 35 healthy subjects (16 males and 19 females). The PVDF sensor signals were compared in terms of morphology with those acquired simultaneously via the PZT sensor, the SCG sensor and the electronic stethoscope. Moreover, the estimation accuracies of PVDF and PZT sensors for inter-beat intervals (IBIs) and inter-breath intervals (IBrIs) were assessed against reference ECG and ERB measurements. The results of statistical analyses confirmed that the PVDF sensor provides FCG signals with very high similarity to those acquired via PZT sensors (median cross-correlation index of 0.96 across all subjects) as well as with SCG and PCG signals (median cross-correlation indices of 0.85 and 0.80, respectively). Moreover, the PVDF sensor provides very accurate estimates of IBIs, with R2 > 0.99 and Bland–Altman limits of agreement (LoA) of [−5.30; 5.00] ms, and of IBrIs, with R2 > 0.96 and LoA of [−0.510; 0.513] s. The flexibility of the PVDF sensor makes it more comfortable and ideal for wearable applications. Unlike PZT, PVDF is lead-free, which increases safety and biocompatibility for prolonged skin contact. Full article
(This article belongs to the Special Issue Sensors for Heart Rate Monitoring and Cardiovascular Disease)
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21 pages, 14388 KiB  
Article
Adaptive Matching of High-Frequency Infrared Sea Surface Images Using a Phase-Consistency Model
by Xiangyu Li, Jie Chen, Jianwei Li, Zhentao Yu and Yaxun Zhang
Sensors 2025, 25(5), 1607; https://doi.org/10.3390/s25051607 - 6 Mar 2025
Viewed by 422
Abstract
The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due [...] Read more.
The sea surface displays dynamic characteristics, such as waves and various formations. As a result, images of the sea surface usually have few stable feature points, with a background that is often complex and variable. Moreover, the sea surface undergoes significant changes due to variations in wind speed, lighting conditions, weather, and other environmental factors, resulting in considerable discrepancies between images. These variations present challenges for identification using traditional methods. This paper introduces an algorithm based on the phase-consistency model. We utilize image data collected from a specific maritime area with a high-frame-rate surface array infrared camera. By accurately detecting images with identical names, we focus on the subtle texture information of the sea surface and its rotational invariance, enhancing the accuracy and robustness of the matching algorithm. We begin by constructing a nonlinear scale space using a nonlinear diffusion method. Maximum and minimum moments are generated using an odd symmetric Log–Gabor filter within the two-dimensional phase-consistency model. Next, we identify extremum points in the anisotropic weighted moment space. We use the phase-consistency feature values as image gradient features and develop feature descriptors based on the Log–Gabor filter that are insensitive to scale and rotation. Finally, we employ Euclidean distance as the similarity measure for initial matching, align the feature descriptors, and remove false matches using the fast sample consensus (FSC) algorithm. Our findings indicate that the proposed algorithm significantly improves upon traditional feature-matching methods in overall efficacy. Specifically, the average number of matching points for long-wave infrared images is 1147, while for mid-wave infrared images, it increases to 8241. Additionally, the root mean square error (RMSE) fluctuations for both image types remain stable, averaging 1.5. The proposed algorithm also enhances the rotation invariance of image matching, achieving satisfactory results even at significant rotation angles. Full article
(This article belongs to the Section Remote Sensors)
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