Sensors

15 pages, 1227 KiB

Open AccessArticle

Cellular Signal Detection by Hydrogenated Amorphous Silicon Photosensitive Chip with Electroexcitation

by Fengyan Hou, Jianjun Dong, Xia Wang, Qiuyang Deng, M. James C. Crabbe and Zuobin Wang

Sensors 2025, 25(17), 5255; https://doi.org/10.3390/s25175255 (registering DOI) - 23 Aug 2025

Based on the photoconductive effect of photosensitive films, a designed light pattern was projected onto a hydrogenated amorphous silicon (a-Si:H) photosensitive chip to generate virtual light-induced electrodes for cellular electrical detection. To obtain high-quality cellular signals, this study aims to explore the effect [...] Read more.

Based on the photoconductive effect of photosensitive films, a designed light pattern was projected onto a hydrogenated amorphous silicon (a-Si:H) photosensitive chip to generate virtual light-induced electrodes for cellular electrical detection. To obtain high-quality cellular signals, this study aims to explore the effect of electrical excitation on a-Si:H photosensitive chip. Firstly, the electrochemical impedance spectroscopy (EIS) and volt-ampere characteristics of the a-Si:H photosensitive chip were characterized. EIS data were fitted to extract equivalent circuit models (ECMs) for both the chip and system. Then analog experiments were performed to verify the ECMs, and the results were consistent with the circuit simulation. Finally, applied alternating current (AC) or direct current (DC) signals to the chip and recorded the electrical signals of the cultured cardiomyocytes on the a-Si:H photosensitive chip. The results demonstrated that applying a high-frequency small AC signal to the chip reduced the background noise of the system by approximately 85.1%, and applying a DC bias increased the amplitude of the detection signal by approximately 142.7%. Consequently, the detection performance of the a-Si:H photosensitive chip for weak bioelectrical signals was significantly enhanced, advancing its applicability in cellular electrophysiological studies. Full article

(This article belongs to the Section Biosensors)

► Show Figures

Figure 1

20 pages, 1846 KiB

Open AccessArticle

Unsupervised Tablet Defect Detection Method Based on Diffusion Model

by Mengfan Zhang, Weifeng Liu, Linqing He and Di Wang

Sensors 2025, 25(17), 5254; https://doi.org/10.3390/s25175254 (registering DOI) - 23 Aug 2025

Abstract

Reconstruction-based unsupervised detection methods have demonstrated strong generalization capabilities in the field of tablet anomaly detection, but there are still problems such as poor reconstruction effect and inaccurate positioning of abnormal areas. To address these problems, this paper proposes an unsupervised Diffusion-based [...] Read more.

Reconstruction-based unsupervised detection methods have demonstrated strong generalization capabilities in the field of tablet anomaly detection, but there are still problems such as poor reconstruction effect and inaccurate positioning of abnormal areas. To address these problems, this paper proposes an unsupervised Diffusion-based Tablet Defect Detection (DTDD) method. This method uses an Assisted Reconstruction (AR) network to introduce original image information to assist in the reconstruction of abnormal areas, thereby improving the reconstruction effect of the diffusion model. It also uses a Scale Fusion (SF) network and an improved anomaly measurement method to improve the accuracy of abnormal area positioning. Finally, the effectiveness of the algorithm is verified on the tablet dataset. The experimental results show that the algorithm in this paper is superior to the algorithms in the same field, effectively improving the detection accuracy and abnormal positioning accuracy, and performing well in the tablet defect detection task. Full article

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

► Show Figures

Figure 1

26 pages, 7644 KiB

Open AccessArticle

Improved PPO Optimization for Robotic Arm Grasping Trajectory Planning and Real-Robot Migration

by Chunlei Li, Zhe Liu, Liang Li, Zeyu Ji, Chenbo Li, Jiaxing Liang and Yafeng Li

Sensors 2025, 25(17), 5253; https://doi.org/10.3390/s25175253 (registering DOI) - 23 Aug 2025

Abstract

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the [...] Read more.

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. Full article

(This article belongs to the Special Issue Design and Integration of Sensors for Control, Planning and Deployment in Robotic Systems)

41 pages, 1863 KiB

Open AccessSystematic Review

Stage-Wise IoT Solutions for Alzheimer’s Disease: A Systematic Review of Detection, Monitoring, and Assistive Technologies

by Sanket Salvi, Lalit Garg and Varadraj Gurupur

Sensors 2025, 25(17), 5252; https://doi.org/10.3390/s25175252 (registering DOI) - 23 Aug 2025

Abstract

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of iot-enabled systems tailored to ad care, focusing on [...] Read more.

The Internet of Things (IoT) has emerged as a transformative technology in managing Alzheimer’s Disease (AD), offering novel solutions for early diagnosis, continuous patient monitoring, and assistive care. This review presents a comprehensive analysis of iot-enabled systems tailored to ad care, focusing on wearable biosensors, cognitive monitoring tools, smart home automation, and Artificial Intelligence (AI)-driven analytics. A systematic literature survey was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to identify, screen, and synthesize 236 relevant studies primarily published between 2020 and 2025 across IEEE Xplore, PubMed, Scopus and Web of Science. The inclusion criteria targeted peer-reviewed articles that proposed or evaluated iot-based solutions for ad detection, progression monitoring, or patient assistance. Key findings highlight the effectiveness of the iot in detecting behavioral and cognitive changes, enhancing safety through real-time alerts, and improving patient autonomy. The review also explores integration challenges such as data privacy, system interoperability, and clinical adoption. The study reveals critical gaps in real-world deployment, clinical validation, and ethical integration of iot-based systems for Alzheimer’s care. This study aims to serve as a definitive reference for researchers, clinicians, and developers working at the intersection of the iot and neurodegenerative healthcare. Full article

(This article belongs to the Special Issue Advances in IoT, AI and Sensor-Based Technologies for Disease Treatment, Health Promotion and Ageing Well)

25 pages, 4631 KiB

Open AccessArticle

Pressure-Guided LSTM Modeling for Fermentation Quantification Prediction

by Jooho Lee, Jieun Jeong and Sangoh Kim

Sensors 2025, 25(17), 5251; https://doi.org/10.3390/s25175251 (registering DOI) - 23 Aug 2025

Abstract

Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability [...] Read more.

Despite significant advancements in sensor technologies, real-time monitoring and prediction of fermentation dynamics remain challenging due to the complexity and nonlinearity of environmental variables. This study presents an integrated framework that combines deep learning techniques with blockchain-enabled data logging to enhance the reliability and transparency of fermentation monitoring. A Long Short-Term Memory (LSTM)-based Fermentation Process Prediction Model (FPPM) was developed to predict Fermentation Percent (FP) and cumulative Fermentation Quantification (FQ) using multivariate time-series data obtained from modular sensor units (PBSU, GBSU, and FQSU). Fermentation conditions were systematically varied under controlled environments, and all data were securely transmitted to a Fermentation–Blockchain–Cloud System (FBCS) to ensure data integrity and traceability. The LSTM models trained on AAG1–3 datasets demonstrated high predictive accuracy, with coefficients of determination (R²) between 0.8547 and 0.9437, and the estimated FQ values showed strong concordance with actual measurements. These results underscore the feasibility of integrating AI-driven prediction models with decentralized data infrastructure for robust and scalable bioprocess control. Full article

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

► Show Figures

Figure 1

27 pages, 1969 KiB

Open AccessArticle

EEL-GA: An Evolutionary Clustering Framework for Energy-Efficient 3D Wireless Sensor Networks in Smart Forestry

by Faryal Batool, Kamran Ali, Aboubaker Lasebae, David Windridge and Anum Kiyani

Sensors 2025, 25(17), 5250; https://doi.org/10.3390/s25175250 (registering DOI) - 23 Aug 2025

Abstract

Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided [...] Read more.

Wireless Sensor Networks (WSNs) are very important for monitoring complex 3D environments like forests, where energy efficiency and reliable communication are critical. This paper presents EEL-GA, an Energy Efficient LEACH-based clustering protocol optimized using a Genetic Algorithm. Cluster head (CH) selection is guided by a dual-metric fitness function combining residual energy and intra-cluster distance. EEL-GA is evaluated against EEL variants using Particle Swarm Optimization (PSO), Differential Evolution (DE), and the Artificial Bee Colony (ABC) across key performance metrics, including energy efficiency, packet delivery, and cluster lifetime. Simulations using real environmental data confirm EEL-GA’s superiority in sustaining energy, minimizing delay, and improving network stability, making it ideal for smart forestry and mission-critical WSN deployments. The model also incorporates environmental dynamics, such as temperature and humidity, enhancing its robustness in real-world applications. These findings support EEL-GA as a scalable, adaptive solution for future energy-aware 3D WSN frameworks. Full article

(This article belongs to the Special Issue Sensor Enabled Smart Energy Solutions)

20 pages, 1760 KiB

Open AccessArticle

Enhancing Real-World Fall Detection Using Commodity Devices: A Systematic Study

by Awatif Yasmin, Tarek Mahmud, Syed Tousiful Haque, Sana Alamgeer and Anne H. H. Ngu

Sensors 2025, 25(17), 5249; https://doi.org/10.3390/s25175249 (registering DOI) - 23 Aug 2025

Abstract

The widespread adoption of smartphones and smartwatches has enabled non-intrusive fall detection through built-in sensors and on-device computation. While these devices are widely used by older adults, existing systems still struggle to accurately detect soft falls in real-world settings. There is a notable [...] Read more.

The widespread adoption of smartphones and smartwatches has enabled non-intrusive fall detection through built-in sensors and on-device computation. While these devices are widely used by older adults, existing systems still struggle to accurately detect soft falls in real-world settings. There is a notable drop in performance when fall-detection models trained offline on labeled accelerometer data are deployed and tested in real-world conditions using streaming, real-time data. To address this, our experimental study investigates whether incorporating additional sensor modalities, specifically gyroscope data with accelerometer data from wrist and hip locations, can help bridge this performance gap. Through systematic experimentation, we demonstrated that combining accelerometer data from the hip and the wrist yields a model capable of achieving an F1-score of 88% using a Transformer-based neural network in offline evaluation, which is an improvement of 8% over a model trained solely on wrist accelerometer data. However, when it is deployed in an uncontrolled home environment with streaming real-time data, this model produced a high number of false positives. To address this, we retrained the model using feedback data that comprised both false positives and true positives and was collected from ten participants during real-time testing. This refinement yielded an F1-sore of 92% and significantly reduced false positives while maintaining comparable accuracy in detecting true falls in real-world settings. Furthermore, we demonstrated that the improved model generalizes well to older adults’ movement patterns, with minimal false-positive detections. Full article

(This article belongs to the Special Issue Smart Sensor Technologies for Accurate Movement Monitoring and Connectivity)

► Show Figures

Figure 1

15 pages, 3542 KiB

Open AccessArticle

mm-Wave Substrate-Integrated Fabry–Perot/Leaky-Wave Antennas in E-Band

by Rana Muhammad Hasan Bilal, Stefano Moscato, Simone Genovesi, Giuliano Manara and Filippo Costa

Sensors 2025, 25(17), 5248; https://doi.org/10.3390/s25175248 (registering DOI) - 23 Aug 2025

Abstract

This article introduces a substrate-integrated, low-cost, and low-profile E-band high-gain Fabry–Perot (FP)/leaky-wave (LW) antenna. This design enables the full integration of a high-gain antenna within a single-layer substrate for millimeter-wave (mm-wave) applications. The antenna design layout comprises a partially reflective surface (PRS) mounted [...] Read more.

This article introduces a substrate-integrated, low-cost, and low-profile E-band high-gain Fabry–Perot (FP)/leaky-wave (LW) antenna. This design enables the full integration of a high-gain antenna within a single-layer substrate for millimeter-wave (mm-wave) applications. The antenna design layout comprises a partially reflective surface (PRS) mounted on a thin, metal-coated, low-cost I-Tera MT40 dielectric substrate. The proposed antenna differs from conventional air-cavity-based FP/LW antennas, as it is fabricated on a low-cost dielectric substrate, eliminating the need for an air cavity, which restricts integration with printed circuit boards (PCBs) and planar circuits. The antenna is excited using a rectangular WR12 waveguide located beneath the ground plane. Impedance matching is achieved by employing a rectangular iris. The formulation for analyzing leaky waves within a cavity is thoroughly discussed using the Transverse Resonance Method (TRM). The proposed FP antenna achieves a maximum realized gain of 14.6 dBi with good impedance matching (

| S_{11} |

= –14 dB). Finally, the proposed antenna is fabricated, and its performance is validated through experimental measurements. Full article

(This article belongs to the Section Electronic Sensors)

► Show Figures

Figure 1

17 pages, 2656 KiB

Open AccessArticle

Chip-Sized Lensless Holographic Microscope for Real-Time On-Chip Biological Sensing

by Sofía Moncada-Madrazo, Sergio Moreno, Oriol Caravaca, Joan Canals, Natalia Castro, Manel López, Javier Ramón-Azcón, Anna Vilà and Ángel Diéguez

Sensors 2025, 25(17), 5247; https://doi.org/10.3390/s25175247 (registering DOI) - 23 Aug 2025

Abstract

Microscopy is a fundamental tool in biological research. However, conventional microscopes require manual operation and depend on user and equipment availability, limiting their suitability for continuous observation. Moreover, their size and complexity make them impractical for in situ experimentation. In this work, we [...] Read more.

Microscopy is a fundamental tool in biological research. However, conventional microscopes require manual operation and depend on user and equipment availability, limiting their suitability for continuous observation. Moreover, their size and complexity make them impractical for in situ experimentation. In this work, we present a novel, compact, affordable, and portable microscope that enables continuous in situ monitoring by being placed directly on biological samples. This chip-sized lensless holographic microscope (CLHM) is specifically designed to overcome the limitations of traditional microscopy. The device consists solely of an ultra-compact, state-of-the-art micro-LED display and a CMOS sensor, all enclosed within a 3D-printed housing. This unique light source enables a size that is markedly smaller than any comparable technology, allowing a resolution of 2.19 μm within a 7 mm distance between the light source and the camera. This paper demonstrates the CLHM’s versatility by monitoring in vitro models and performing whole-organism morphological analyses of small specimens. These experiments underscore its potential as an on-platform sensing device for continuous, in situ biological monitoring across diverse models. Full article

(This article belongs to the Special Issue Advanced Sensors and Instrumentation for In-Vivo and In-Situ Biological Monitoring)

► Show Figures

Figure 1

21 pages, 11779 KiB

Open AccessArticle

Dynamic Responses of a Single-Axle Trailer When Driving Over a Road Obstacle

by Dalibor Barta, Miroslav Blatnický, Alyona Lovska, Sławomir Kowalski, Aleš Slíva and Ján Dižo

Sensors 2025, 25(17), 5246; https://doi.org/10.3390/s25175246 (registering DOI) - 23 Aug 2025

Abstract

Trailers for passenger cars are often used for the transportation of goods. There are various trailer designs. Most trailers are equipped with axles, which include swinging arms and are suspended by rubber segments. Observations have revealed that empty trailers have unfavorable driving properties [...] Read more.

Trailers for passenger cars are often used for the transportation of goods. There are various trailer designs. Most trailers are equipped with axles, which include swinging arms and are suspended by rubber segments. Observations have revealed that empty trailers have unfavorable driving properties when they are driven on uneven roads, for example, the wheels could jump off the road. Such a situation is dangerous because it is not possible to transmit any contact forces (longitudinal, lateral, or vertical) between the wheel and the road. The goal of the present research was to measure acceleration generated in a single-axle trailer when driving over a road obstacle. Measurements were conducted in a non-public area to avoid the risk of accidents. Acceleration was recorded using two accelerometers placed on the single-axle trailer frame above the wheels’ axle of rotation. Tests were performed using a vehicle–trailer combination at the chosen driving speeds, and the results for driving speeds of 20 and 30 km/h are presented. Wood plates with a height of 25 and 50 mm were used as an artificial road obstacle. The single-axle trailer was loaded with gravel bags weighing 0 to 300 kg. The measurements revealed that heavier trailer loads and lower driving speeds are safer for trailer operation. Furthermore, the measurements also demonstrated that the wheels were significantly more likely to jump off the road with a 0 kg load and low driving speed. Full article

(This article belongs to the Special Issue Advanced Sensing and Analysis Technology in Transportation Safety)

► Show Figures

Figure 1

13 pages, 629 KiB

Open AccessArticle

Estrus Detection and Optimal Insemination Timing in Holstein Cattle Using a Neck-Mounted Accelerometer Sensor System

by Jacobo Álvarez, Antía Acción, Elio López, Carlota Antelo, Renato Barrionuevo, Juan José Becerra, Ana Isabel Peña, Pedro García Herradón, Luis Ángel Quintela and Uxía Yáñez

Sensors 2025, 25(17), 5245; https://doi.org/10.3390/s25175245 (registering DOI) - 23 Aug 2025

Abstract

This study aimed to evaluate the accuracy of the accelerometer-equipped collar RUMI to detect estrus in dairy cows, establish a recommendation for the optimal timing for artificial insemination (AI) when using this device, and characterize the blood flow of the dominant follicle (F) [...] Read more.

This study aimed to evaluate the accuracy of the accelerometer-equipped collar RUMI to detect estrus in dairy cows, establish a recommendation for the optimal timing for artificial insemination (AI) when using this device, and characterize the blood flow of the dominant follicle (F) and the corpus luteum (CL) as ovulation approaches. Forty-seven cycling cows were monitored following synchronization with a modified G6G protocol, allowing for spontaneous ovulation. Ultrasound examinations were conducted every 12 h, starting 48 h after the second PGF2α dose, to monitor uterine and ovarian changes. Blood samples were also collected to determine serum progesterone (P4) levels. Each cow was fitted with a RUMI collar, which continuously monitored behavioral changes to identify the onset, offset, and peak of activity of estrus. One-way ANOVA assessed the relationship between physiological parameters and time before ovulation. Results showed that the RUMI collar demonstrated high specificity (100%), sensitivity (90.90%), and accuracy (93.62%) for estrus detection. The optimal AI window was identified as between 11.4 and 15.5 h after heat onset. Increased blood flow to the F and reduced luteal activity were observed in the 48 h prior to ovulation. Further research is needed to assess the influence of this AI window on conception rates, and if it should be modified considering external factors. Full article

(This article belongs to the Section Intelligent Sensors)

► Show Figures

Figure 1

21 pages, 1633 KiB

Open AccessArticle

Efficient Deep Learning-Based Arrhythmia Detection Using Smartwatch ECG Electrocardiograms

by Herwin Alayn Huillcen Baca and Flor de Luz Palomino Valdivia

Sensors 2025, 25(17), 5244; https://doi.org/10.3390/s25175244 (registering DOI) - 23 Aug 2025

Abstract

According to the World Health Organization, cardiovascular diseases, including cardiac arrhythmias, are the leading cause of death worldwide due to their silent, asymptomatic nature. To address this problem, early and accurate diagnosis is crucial. Although this task is typically performed by a cardiologist, [...] Read more.

According to the World Health Organization, cardiovascular diseases, including cardiac arrhythmias, are the leading cause of death worldwide due to their silent, asymptomatic nature. To address this problem, early and accurate diagnosis is crucial. Although this task is typically performed by a cardiologist, diagnosing arrhythmias can be imprecise due to the subjectivity of reading and interpreting electrocardiograms (ECGs), and electrocardiograms are often subject to noise and interference. Deep learning-based approaches present methods for automatically detecting arrhythmias and are positioned as an alternative to support cardiologists’ diagnoses. However, these methods are trained and tested only on open datasets of electrocardiograms from Holter devices, whose results aim to improve the accuracy of the state of the art, neglecting the efficiency of the model and its application in a practical clinical context. In this work, we propose an efficient model based on a 1D CNN architecture to detect arrhythmias from smartwatch ECGs, for subsequent deployment in a practical scenario for the monitoring and early detection of arrhythmias. Two datasets were used: UMass Medical School Simband for a binary arrhythmia detection model to evaluate its efficiency and effectiveness, and the MIT-BIH arrhythmia database to validate the multiclass model and compare it with state-of-the-art models. The results of the binary model achieved an accuracy of 64.81%, a sensitivity of 89.47%, and a specificity of 6.25%, demonstrating the model’s reliability, especially in specificity. Furthermore, the computational complexity was 1.2 million parameters and 68.48 MFlops, demonstrating the efficiency of the model. Finally, the results of the multiclass model achieved an accuracy of 99.57%, a sensitivity of 99.57%, and a specificity of 99.47%, making it one of the best state-of-the-art proposals and also reconfirming the reliability of the model. Full article

(This article belongs to the Special Issue Advances in Wearable Sensors for Continuous Health Monitoring)

► Show Figures

Figure 1

15 pages, 4392 KiB

Open AccessArticle

InfraRed Thermographic Measurements in Parkinson’s Disease Subjects: Preliminary Results

by Antonio Cannuli, Fabrizio Freni, Antonino Quattrocchi, Carmen Terranova, Andrea Venuto and Roberto Montanini

Sensors 2025, 25(17), 5243; https://doi.org/10.3390/s25175243 (registering DOI) - 23 Aug 2025

Abstract

In this preliminary study, the thermoregulatory response in individuals diagnosed with Parkinson’s disease was investigated by infrared thermography. Parkinson’s disease is a complex neurodegenerative disorder primarily known for motor impairments, significantly reducing the quality of life of affected people. However, in most cases, [...] Read more.

In this preliminary study, the thermoregulatory response in individuals diagnosed with Parkinson’s disease was investigated by infrared thermography. Parkinson’s disease is a complex neurodegenerative disorder primarily known for motor impairments, significantly reducing the quality of life of affected people. However, in most cases, such disease is accompanied or preceded by non-motor symptoms, including autonomic dysfunction. As in the case of neurovegetative dysautonomia, this dysfunction involves a malfunction of the autonomic nervous system, which also plays a key role in thermoregulation. In general, such conditions are not always easy to detect; a valid method could be represented by the vasomotor response of the skin to cold stimuli. In this context, infrared thermography can provide insights into the thermoregulatory patterns associated with autonomic dysfunction, representing a valuable tool for non-invasive assessment of Parkinson’s research. Early biomarkers of the disease can be obtained through changes in skin temperature, allowing for timely intervention and management. The study was conducted on a cohort of 16 subjects (8 patients with Parkinson’s disease and 8 healthy controls), who were monitored with infrared images captured from their hands, following a specific protocol established by a preliminary analysis. Experimental results revealed that thermography can detect focal points and regions exhibiting either hyper- or hypothermia across the skin surface and muscular regions. This capability allows for extracting and categorizing precise medical data, which could inform future research aimed at identifying early markers of the disease. However, as this is a preliminary observational study, no diagnostic claims are made, and further investigations on larger cohorts with controlled comorbidities are needed. Full article

(This article belongs to the Special Issue Advanced Wearable Sensors and Other Sensing Technologies for Diagnosis and Treatment of Parkinson's Disease and Movement Disorders)

► Show Figures

Figure 1

16 pages, 6695 KiB

Open AccessArticle

Optimizing the Egli Model for Vehicular Ultra-Shortwave Communication Using High-Resolution Remote Sensing Satellite Imagery

by Guangshuo Zhang, Peng Chen, Fulin Wu, Yangzhen Qin, Qi Xu, Tianao Li, Shiwei Zhang and Hongmin Lu

Sensors 2025, 25(17), 5242; https://doi.org/10.3390/s25175242 (registering DOI) - 23 Aug 2025

Abstract

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed [...] Read more.

The traditional radio wave propagation models exhibit several limitations when they are employed to predict the path loss for vehicular ultra-shortwave wireless communication. To addresses these challenges, an optimized approach for Egli model based on the high-resolution remote sensing satellite image is proposed in this study. The optimization process includes three components. First, a method for calculating the actual equivalent antenna height is introduced, utilizing high-precision remote sensing satellite imagery to obtain communication path profiles. This method accounts for the antenna’s physical length, vehicular height, and local terrain characteristics, thereby providing an accurate representation of the antenna’s effective height within its operational environment. Second, an equivalent substitution method for ground loss is developed, utilizing surface information derived from high-precision remote sensing satellite images. This method integrates ground loss directly into the Egli model’s calculation process, eliminating the need for separate computations and simplifying the model. Third, leveraging the Egli model as a foundation, the least squares method (LSM) is employed to fit the relief height, ensuring the model meets the requirements for ultra-short wave communication distances under line-of-sight (LOS) conditions and enhances suitability for real-world vehicular communication systems. Finally, the validity and accuracy of the optimization model are verified by comparing the measured data with the theoretical calculated values. Compared with the Egli model, the Egli model with additional correction factors, and the measured data, the average error of the optimized model is reduced by 8.98%, 2.09%, and the average error is 0.45%. Full article

(This article belongs to the Section Remote Sensors)

► Show Figures

Figure 1

18 pages, 15231 KiB

Open AccessArticle

Stereo Vision-Based Underground Muck Pile Detection for Autonomous LHD Bucket Loading

by Emilia Hennen, Adam Pekarski, Violetta Storoschewich and Elisabeth Clausen

Sensors 2025, 25(17), 5241; https://doi.org/10.3390/s25175241 (registering DOI) - 23 Aug 2025

Abstract

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it [...] Read more.

To increase the safety and efficiency of underground mining processes, it is important to advance automation. An important part of that is to achieve autonomous material loading using load–haul–dump (LHD) machines. To be able to autonomously load material from a muck pile, it is crucial to first detect and characterize it in terms of spatial configuration and geometry. Currently, the technologies available on the market that do not require an operator at the stope are only applicable in specific mine layouts or use 2D camera images of the surroundings that can be observed from a control room for teleoperation. However, due to missing depth information, estimating distances is difficult. This work presents a novel approach to muck pile detection developed as part of the EU-funded Next Generation Carbon Neutral Pilots for Smart Intelligent Mining Systems (NEXGEN SIMS) project. It uses a stereo camera mounted on an LHD to gather three-dimensional data of the surroundings. By applying a topological algorithm, a muck pile can be located and its overall shape determined. This system can detect and segment muck piles while driving towards them at full speed. The detected position and shape of the muck pile can then be used to determine an optimal attack point for the machine. This sensor solution was then integrated into a complete system for autonomous loading with an LHD. In two different underground mines, it was tested and demonstrated that the machines were able to reliably load material without human intervention. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

14 pages, 1418 KiB

Open AccessArticle

Evolution of Physiological Responses and Fatigue Analysis in Padel Matches According to Match Outcome and Playing Position

by Bingen Marcos-Rivero, Javier Yanci, Cristina Granados, Jon Mikel Picabea and Josu Ascondo

Sensors 2025, 25(17), 5240; https://doi.org/10.3390/s25175240 (registering DOI) - 23 Aug 2025

Abstract

Padel is a doubles racket sport played on an enclosed court, characterised by intermittent high-intensity efforts, frequent directional changes, and short recovery periods. This study aimed to analyse the evolution of physiological responses and neuromuscular fatigue in amateur padel players according to playing [...] Read more.

Padel is a doubles racket sport played on an enclosed court, characterised by intermittent high-intensity efforts, frequent directional changes, and short recovery periods. This study aimed to analyse the evolution of physiological responses and neuromuscular fatigue in amateur padel players according to playing position (Right Side [RS] vs. Left Side [LS]) and match outcome (Win or Lose). A total of 52 padel players (35.6 ± 11.6 years) participated, competing in 13 matches. The mean match duration was 57.2 ± 15.7 min, with an average of 152.0 ± 40.4 points per match. Physiological variables were recorded during each set, and neuromuscular variables (countermovement jump [CMJ] and handgrip strength) were assessed before the match and after each set. No significant differences in physiological load were found between winners and losers or between RS and LS positions. However, differences in handgrip strength were observed at T1 (p < 0.05, d = −0.72) and T2 (p < 0.05, d = −0.59) (post-set testing), with LS players showing higher grip strength. Regarding the progression of physiological responses across the different sets, a progressive increase in cardiovascular load was observed within each subgroup, with significant differences across sets (set 1, set 2, and set 3) in several variables, including HR_peak, HR_avg, zone 1, zone 2, zone 3, and TRIMP_Edwards. No performance decline was observed in CMJ or handgrip strength in any of the groups analysed. These findings suggest that physiological responses increase throughout a match, particularly in the final sets, but no signs of neuromuscular fatigue (CMJ and handgrip) were observed, regardless of match outcome or playing position. These results highlight the need to include high-intensity scenarios and role-specific strategies in training to address the progressive physiological demands and positional differences in match play. Full article

(This article belongs to the Section Wearables)

► Show Figures

Figure 1

26 pages, 2363 KiB

Open AccessArticle

An Analysis and Simulation of Security Risks in Radar Networks from the Perspective of Cybersecurity

by Runyang Chen, Yi Zhang, Xiuhe Li and Jinhe Ran

Sensors 2025, 25(17), 5239; https://doi.org/10.3390/s25175239 (registering DOI) - 23 Aug 2025

Abstract

Radar networks, composed of multiple radar stations and a fusion center interconnected via communication technologies, are widely used in civil aviation and maritime operations. Ensuring the security of radar networks is crucial. While their strong anti-jamming capabilities make traditional electronic countermeasures less effective, [...] Read more.

Radar networks, composed of multiple radar stations and a fusion center interconnected via communication technologies, are widely used in civil aviation and maritime operations. Ensuring the security of radar networks is crucial. While their strong anti-jamming capabilities make traditional electronic countermeasures less effective, the openness and vulnerability of their network architecture expose them to cybersecurity risks. Current research on radar network security risk analysis from a cybersecurity perspective remains insufficient, necessitating further study to provide theoretical support for defense strategies. Taking centralized radar networks as an example, this paper first analyzes their architecture and potential cybersecurity risks, identifying a threat where attackers could potentially execute false data injection attacks (FDIAs) against the fusion center via man-in-the-middle attacks (MITMAs). A threat model is then established, outlining possible attack procedures and methods, along with defensive recommendations and evaluation metrics. Furthermore, for scenarios involving single-link control without traffic increase, the impact of different false data construction methods is examined. Simulation experiments validate the findings, showing that the average position offset increases from 8.38 m to 78.35 m after false data injection. This result confirms significant security risks under such threats, providing a reference for future countermeasure research. Full article

(This article belongs to the Section Sensors Development)

► Show Figures

Figure 1

30 pages, 1887 KiB

Open AccessArticle

Laser-Induced Graphene on Biocompatible PDMS/PEG Composites for Limb Motion Sensing

by Anđela Gavran, Marija V. Pergal, Teodora Vićentić, Milena Rašljić Rafajilović, Igor A. Pašti, Marko V. Bošković and Marko Spasenović

Sensors 2025, 25(17), 5238; https://doi.org/10.3390/s25175238 - 22 Aug 2025

Abstract

The advancement of laser-induced graphene (LIG) has significantly enhanced the development of wearable and flexible electronic devices. Due to its exceptional physical, chemical, and electronic properties, LIG has emerged as a highly effective active material for wearable sensors. However, despite the wide range [...] Read more.

The advancement of laser-induced graphene (LIG) has significantly enhanced the development of wearable and flexible electronic devices. Due to its exceptional physical, chemical, and electronic properties, LIG has emerged as a highly effective active material for wearable sensors. However, despite the wide range of materials suitable as precursors for LIG, the scarcity of stretchable and biocompatible polymers amenable to laser graphenization has remained a persistent challenge. In this study, laser-induced graphene (LIG) was fabricated directly on biocompatible and flexible cross-linked PDMS/PEG (with M_n (PEG) = 400 g/mol) composites for the first time, enabling their application in wearable sensors. The addition of PEG compensates for the low carbon content in PDMS, enabling efficient laser graphenization. Laser parameters were systematically optimized to achieve high-quality graphene, and a comprehensive characterization with varying PEG content (10–40 wt.%) was conducted using multiple analytical techniques. Tensile tests revealed that incorporating PEG significantly enhanced elongation at break, reaching 237% for PDMS/40 wt.% PEG while reducing Young’s modulus to 0.25 MPa, highlighting the excellent flexibility of the substrate material. Surface analysis using X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and Raman spectroscopy demonstrated the formation of high-quality few-layer graphene with the fewest defects in PDMS/40 wt.% PEG composites. Nevertheless, the adhesion of electrical contacts to LIG that was directly induced on PDMS/PEG proved to be challenging. To overcome this challenge, we produced devices by means of laser induction on polyimide and transfer to PDMS/PEG. We demonstrate the practical utility of such devices by applying them to monitor limb motion in real time. The sensor showed a stable and repeatable piezoresistive response under multiple bending cycles. These results provide valuable insights into the fabrication of biocompatible LIG-based flexible sensors, paving the way for their broader implementation in medical and sports technologies. Full article

(This article belongs to the Special Issue Materials and Devices for Flexible Electronics in Sensor Applications)

31 pages, 7810 KiB

Open AccessArticle

Time-Frequency Feature Extraction and Analysis of Inland Waterway Buoy Motion Based on Massive Monitoring Data

by Xin Li, Yimei Chen, Lilei Mao and Nini Zhang

Sensors 2025, 25(17), 5237; https://doi.org/10.3390/s25175237 - 22 Aug 2025

Abstract

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid [...] Read more.

Sensors are widely used in inland waterway buoys to monitor their position, but the collected data are often affected by noise, outliers, and irregular sampling intervals. To address these challenges, a standardized data processing framework is proposed. Outliers are identified using a hybrid approach combining interquartile range filtering and Isolation Forest algorithm. Interpolation methods are adaptively selected based on time intervals. For short-term gaps, cubic spline interpolation is applied, otherwise, a method that combines dominant periodicity estimation with physical constraints based on power spectral density (PSD) is proposed. An adaptive unscented Kalman filter (AUKF), integrated with the Singer motion model, are applied for denoising, dynamically adjusting to local noise statistics and capturing acceleration dynamics. Afterwards, a set of time-frequency features are extracted, including centrality, directional dispersion, and wavelet transform-based features. Taking the lower Yangtze River as a case study, representative buoys are selected based on dynamic time warping similarity. The features analysis result show that the movement of buoys is closely related to the dynamics dominated by the semi-diurnal tide, and is also affected by runoff and accidents. The method improves the quality and interpretability of buoy motion data, facilitating more robust monitoring and hydrodynamic analysis. Full article

(This article belongs to the Section Remote Sensors)

Journal Description

Sensors

Latest Articles

Journal Menu

Journal Browser

Highly Accessed Articles

Latest Books

E-Mail Alert

News

Topics

Conferences

Special Issues

Topical Collections

Further Information

Guidelines

MDPI Initiatives

Follow MDPI