Sensors

18 pages, 1724 KiB

Open AccessArticle

Driver Steering Intention Prediction for Human-Machine Shared Systems of Intelligent Vehicles Based on CNN-GRU Network

by Chen Zhou, Fan Zhang, Edric John Cruz Nacpil, Zheng Wang and Fei-Xiang Xu

Sensors 2025, 25(10), 3224; https://doi.org/10.3390/s25103224 - 20 May 2025

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning [...] Read more.

In order to mitigate human-machine conflicts and optimize shared control strategy in advance, it is essential for the shared control system to understand and predict driver behavior. This paper proposes a method for predicting driver steering intention with a CNN-GRU hybrid machine learning model. The convolutional neural network (CNN) layer extracts features from the stochastic driver behavior, which is input to the gated-recurrent-unit (GRU) layer. And the driver’s steering intention is forecasted based on the GRU model. Our study was conducted using a driving simulator to observe the lateral control behaviors of 18 participants in four different driving circumstances. Finally, the efficiency of the suggested prediction approach was evaluated employing long-short-term-memory, GRU, CNN, Transformer, and back propagation networks. Experimental results demonstrated that the proposed CNN-GRU model performs significantly better than baseline models. Compared with the GRU network, the CNN-GRU network reduced the RMSE, MAE, and MAPE of the driver’s input torque by 33.22%, 32.33%, and 35.86%, respectively. The proposed prediction method also possesses adaptability to different driver behaviors. Full article

(This article belongs to the Special Issue Connected and Autonomous Vehicles: Trends, Applications and Security Challenges)

28 pages, 4759 KiB

Open AccessArticle

Soil Porosity Detection Method Based on Ultrasound and Multi-Scale Feature Extraction

by Hang Xing, Zeyang Zhong, Wenhao Zhang, Yu Jiang, Xinyu Jiang, Xiuli Yang, Weizi Cai, Shuanglong Wu and Long Qi

Sensors 2025, 25(10), 3223; https://doi.org/10.3390/s25103223 - 20 May 2025

Abstract

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a [...] Read more.

Soil porosity, as an essential indicator for assessing soil quality, plays a key role in guiding agricultural production, so it is beneficial to detect soil porosity. However, the currently available methods do not apply to high-precision and rapid detection of soil with a black-box nature in the field, so this paper proposes a soil porosity detection method based on ultrasound and multi-scale CNN-LSTM. Firstly, a series of ring cutter soil samples with different porosities were prepared manually to simulate soil collected in the field using a ring cutter, followed by ultrasonic signal acquisition of the soil samples. The acquired signals were subjected to three kinds of data augmentation processes to enrich the dataset: adding Gaussian white noise, time shift transformation, and random perturbation. Since the collected ultrasonic signals belong to long-time series data and there are different frequency and sequence features, this study constructs a multi-scale CNN-LSTM deep neural network model using large convolution kernels based on the idea of multi-scale feature extraction, which uses multiple large convolution kernels of different sizes to downsize the collected ultra-long time series data and extract local features in the sequences, and combining the ability of LSTM to capture global and long-term dependent features enhances the feature expression ability of the model. The multi-head self-attention mechanism is added at the end of the model to infer the before-and-after relationship of the sequence data to improve the degradation of the model performance caused by waveform distortion. Finally, the model was trained, validated, and tested using ultrasonic signal data collected from soil samples to demonstrate the accuracy of the detection method. The model has a coefficient of determination of 0.9990 for detecting soil porosity, with a percentage root mean square error of only 0.66%. It outperforms other advanced comparative models, making it very promising for application. Full article

(This article belongs to the Section Smart Agriculture)

21 pages, 2436 KiB

Open AccessArticle

Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training

by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras and Janine E. Hinton

Sensors 2025, 25(10), 3222; https://doi.org/10.3390/s25103222 - 20 May 2025

Abstract

This study explores nursing students’ stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal activity, and skin temperature, the study assesses the level [...] Read more.

This study explores nursing students’ stress responses while they are being trained in a mixed reality (MR) setting that replicates highly stressful clinical scenarios. Using measurements of physiological indices such as heart rate, electrodermal activity, and skin temperature, the study assesses the level of stress when the students interact with digital patients whose vital signs and symptoms interact dynamically to respond to student inputs. The simulation consists of six segments, during which critical events like hypotension and hypoxia occur, and the patient’s condition changes based on the nurse’s clinical decisions. Machine learning algorithms were then used to analyze the nurse’s physiological data and to classify different levels of stress. Among the models tested, the Stacking Classifier demonstrated the highest classification accuracy of 96.4%, outperforming both Random Forest (96.18%) and Gradient Boosting (95.35%). The results showed clear patterns of stress during the simulation segments. Statistical analysis also found significant differences in stress responses and identified key physiological markers linked to each stress level. This pioneering study demonstrates the effectiveness of MR as a training tool for healthcare professionals in high-pressured scenarios and lays the groundwork for further studies on stress management, adaptive training procedures, and real-time detection and intervention in MR-based nursing training. Full article

(This article belongs to the Special Issue Sensors for Physiological Monitoring and Digital Health)

18 pages, 2153 KiB

Open AccessArticle

Super-Resolution Image Optimisation Based on Gradient Iterative Fast Diffraction-Free Spot Algorithm

by Chen Yu, Ying Liu, Linhan Li, Guangpeng Zhou, Boshi Dang, Jie Du, Junlin Ma and Site Zhang

Sensors 2025, 25(10), 3221; https://doi.org/10.3390/s25103221 - 20 May 2025

Abstract

Diffraction significantly deteriorates the quality of the laser image, causing severe degradation that undermines the theoretical performance parameters of the autofocus system. In this paper, we conduct a comprehensive analysis of the non-uniform features of the images. To enhance the imaging quality of [...] Read more.

Diffraction significantly deteriorates the quality of the laser image, causing severe degradation that undermines the theoretical performance parameters of the autofocus system. In this paper, we conduct a comprehensive analysis of the non-uniform features of the images. To enhance the imaging quality of each individual image, we propose a de-diffraction algorithm based on gradient iteration. This algorithm is capable of rapidly removing the interference spots resulting from diffraction and restoring the distorted laser spots. By doing so, it effectively eliminates the inevitable reduction in the autofocus resolution and focusing accuracy caused by diffraction. Furthermore, the proposed calculation model for the intra-localisation interval significantly improves the convergence of the iterative calculation process. Through experiments, it has been verified that, under the same conditions, the interlayer resolution between the reflective surfaces of the samples processed using this algorithm is increased to a quarter of the original value. This remarkable improvement in resolution, which far exceeds the microscope’s inherent resolution, demonstrates that the algorithm successfully achieves super-resolution for the microscope. Full article

(This article belongs to the Section Optical Sensors)

11 pages, 921 KiB

Open AccessArticle

An Aquatic Treadmill Alters Lower Limb Walking Dynamics in Typically Developing Children and Children with Cerebral Palsy

by Oluwaseye Odanye, Joseph Harrington, Aaron Likens, David Kingston and Brian Knarr

Sensors 2025, 25(10), 3220; https://doi.org/10.3390/s25103220 - 20 May 2025

Abstract

This block-randomized crossover study investigated how a speed-modulated aquatic treadmill (AT) impacts the walking biomechanics of pediatric gait. Eight cerebral palsy (CP) and fifteen typically developing (TD) children walked at normal, slow, and fast treadmill speeds in AT and dry treadmill (DT) conditions. [...] Read more.

This block-randomized crossover study investigated how a speed-modulated aquatic treadmill (AT) impacts the walking biomechanics of pediatric gait. Eight cerebral palsy (CP) and fifteen typically developing (TD) children walked at normal, slow, and fast treadmill speeds in AT and dry treadmill (DT) conditions. The joint angles of participants were calculated from inertial measurement units to derive sample entropy (SE) measures that quantified the regularity or complexity of motion. A hierarchical statistical model revealed that the CP group had lower SE values for the hip, knee, and ankle joints in the AT and at slower than faster treadmill speeds. Only the SE values of the knee and ankle joints were impacted for the TD group. The lower SE values suggest improved regularity for participants at slower speeds and in the AT environment. This study highlights the potential of AT to improve the walking biomechanics of children with CP in acute exposure, but further work is needed to investigate the AT condition as a gait rehabilitation environment. Full article

(This article belongs to the Special Issue Sensors for Human Movement Recognition and Analysis)

13 pages, 1144 KiB

Open AccessCommunication

The Design of a Computer Vision Sensor Based on a Low-Power Edge Detection Circuit

by Suhyeon Lee, Yu Chan Yun, Seung Min Heu, Kyu Hyun Lee, Seung Joon Lee, Kyungmin Lee, Jiin Moon, Hyuna Lim, Taeun Jang, Minkyu Song and Soo Youn Kim

Sensors 2025, 25(10), 3219; https://doi.org/10.3390/s25103219 - 20 May 2025

Abstract

We propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that performs edge mask computation and detection during the analog-to-digital (A/D) conversion process to output 1-bit edge images. By utilizing the characteristics of the edge that can obtain a 1-bit image, the edge mask [...] Read more.

We propose a complementary metal-oxide-semiconductor (CMOS) image sensor (CIS) that performs edge mask computation and detection during the analog-to-digital (A/D) conversion process to output 1-bit edge images. By utilizing the characteristics of the edge that can obtain a 1-bit image, the edge mask and thresholding operations are performed simultaneously during the A/D conversion process, thereby reducing memory capacity along with a high number of frames per second (FPS). Additionally, by implementing a 1-bit analog-to-digital converter (ADC) instead of a high-resolution ADC and counter through the 1-bit edge data obtained from the edge mask operation, both static and dynamic power consumption are reduced. The proposed CIS, fabricated with a one-poly six-metal CIS process with a 4T-active pixel sensor, has a core area of 2.546 mm 1.923 mm in a chip area of 2.558 mm × 4.3 mm. The total power consumption is 1.52 mW at 23 FPS, with power supplies of 2.8V and 1.5V for the analog domain and 1.5V for the digital domain. Full article

(This article belongs to the Special Issue Recent Advances in CMOS Image Sensor)

22 pages, 3465 KiB

Open AccessArticle

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

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

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

Abstract

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

ϕ

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

ϕ

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

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

ϕ

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

ϕ

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

ϕ

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

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

21 pages, 7997 KiB

Open AccessArticle

Simulating Pulp Vitality Measurements via Digital Optical Twins: Influence of Dental Components on Spectral Transmission

by David Hevisov, Thomas Peter Ertl and Alwin Kienle

Sensors 2025, 25(10), 3217; https://doi.org/10.3390/s25103217 - 20 May 2025

Abstract

Optical diagnostic techniques represent an attractive complement to conventional pulp vitality tests, as they can provide direct information about the vascular status of the pulp. However, the complex, multi-layered structure of a tooth significantly influences the detected signal and, ultimately, the diagnostic decision. [...] Read more.

Optical diagnostic techniques represent an attractive complement to conventional pulp vitality tests, as they can provide direct information about the vascular status of the pulp. However, the complex, multi-layered structure of a tooth significantly influences the detected signal and, ultimately, the diagnostic decision. Despite this, the impact of the various dental components on light propagation within the tooth, particularly in the context of diagnostic applications, remains insufficiently studied. To help bridge this gap and potentially enhance diagnostic accuracy, this study employs digital optical twins based on the Monte Carlo method. Using incisor and molar models as examples, the influence of tooth and pulp geometry, blood concentration, and pulp composition, such as the possible presence of pus, on spectrally resolved transmission signals is demonstrated. Furthermore, it is shown that gingival blood absorption can significantly overlay the pulpal measurement signal, posing a substantial risk of misdiagnosis. Strategies such as shifting the illumination and detection axes, as well as time-gated detection, are explored as potential approaches to suppress interfering signals, particularly those originating from the gingiva. Full article

(This article belongs to the Special Issue Vision- and Image-Based Biomedical Diagnostics—2nd Edition)

30 pages, 2545 KiB

Open AccessArticle

Adaptive Cooperative Search Algorithm for Air Pollution Detection Using Drones

by Il-kyu Ha

Sensors 2025, 25(10), 3216; https://doi.org/10.3390/s25103216 - 20 May 2025

Abstract

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore [...] Read more.

Drones are widely used in urban air pollution monitoring. Although studies have focused on single-drone applications, collaborative applications for air pollution detection are relatively underexplored. This paper presents a 3D cube-based adaptive cooperative search algorithm that allows two drones to collaborate to explore air pollution. The search space is divided into cubic regions, and each drone explores the upper or lower halves of the cubes and collects data from their vertices. The vertex with the highest measurement is selected by comparing the collected data, and an adjacent cube-shaped search area is generated for exploration. The search continues iteratively until any vertex measurement reaches a predefined threshold. An improved algorithm is also proposed to address the divergence and oscillation that occur during the search. In simulations, the proposed method consumed 21 times less CPU time and required 23 times less search distance compared to linear search. Additionally, the cooperative search method using multiple drones was more efficient than single-drone exploration in terms of the same parameters. Specifically, compared to single-drone exploration, the collaborative drone search reduced CPU time by a factor of 2.6 and search distance by approximately a factor of 2. In experiments in real-world scenarios, multiple drones equipped with the proposed algorithm successfully detected cubes containing air pollution above the threshold level. The findings serve as an important reference for research on drone-assisted target exploration, including air pollution detection. Full article

(This article belongs to the Section Environmental Sensing)

15 pages, 1134 KiB

Open AccessArticle

Prediction of Influence of Environmental Factors on the Toxicity of Pentachlorophenol on E. coli-Based Bioassays

by Sulivan Jouanneau and Gerald Thouand

Sensors 2025, 25(10), 3215; https://doi.org/10.3390/s25103215 - 20 May 2025

Abstract

Evaluating the impact of pollutants on ecosystems and human health is crucial. To achieve this, a wide range of bioassays, using organisms of different trophic levels, are available. Extrapolating the results of these bioassays to real environmental conditions remains a major challenge. This [...] Read more.

Evaluating the impact of pollutants on ecosystems and human health is crucial. To achieve this, a wide range of bioassays, using organisms of different trophic levels, are available. Extrapolating the results of these bioassays to real environmental conditions remains a major challenge. This study addresses this challenge by aiming to develop an algorithm capable of predicting the effect of environmental conditions on the impact of a toxicant, pentachlorophenol (PCP). Three abiotic factors were considered: pH, temperature, and conductivity. In the absence of the toxicant, the activity of Escherichia coli is influenced only by pH and temperature. However, exposed to PCP, the sensitivity of the bacteria was affected by these three factors. From these data, a predictive model was established to assess the intensity of the toxic effect induced by PCP. This model was validated using a validation dataset and demonstrated a strong correlation between the experimental and predicted values (r² ≈ 0.9). Thus, this approach enables the effective prediction of PCP’s effects by accounting for environmental variations. This proof of concept constitutes a potential alternative, complementary to conventional models like BLMs (focused on water chemistry for metals) and QSARs (linking structure to intrinsic toxicity), which often overlook the complexities of real-world environmental conditions. Full article

(This article belongs to the Special Issue Feature Papers in Biosensors Section 2025)

23 pages, 28335 KiB

Open AccessArticle

A Target Tracking Method Based on a Pyramid Channel Attention Mechanism

by Dongxuan Zhao, Yunsong Li, Jiaxing Li, Xiping Duan, Ning Ma and Yuhe Wang

Sensors 2025, 25(10), 3214; https://doi.org/10.3390/s25103214 - 20 May 2025

Abstract

To enhance the tracking performance of transformer-based trackers in complex scenes, we propose a novel visual object tracking method that incorporates three key components: a pyramid channel attention mechanism, a hierarchical cross-attention structure, and an attention-guided multi-layer perceptron. The pyramid channel attention mechanism [...] Read more.

To enhance the tracking performance of transformer-based trackers in complex scenes, we propose a novel visual object tracking method that incorporates three key components: a pyramid channel attention mechanism, a hierarchical cross-attention structure, and an attention-guided multi-layer perceptron. The pyramid channel attention mechanism dynamically enhances informative feature channels across different scales, while the hierarchical cross-attention structure facilitates effective feature interaction. The attention-guided multi-layer perceptron introduces nonlinear transformations under attention guidance to improve feature representation. Experimental results on benchmark datasets demonstrate the superior performance of the proposed method. Full article

(This article belongs to the Section Navigation and Positioning)

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20 pages, 1265 KiB

Open AccessArticle

A Short-Term Risk Prediction Method Based on In-Vehicle Perception Data

by Xinpeng Yao, Nengchao Lyu and Mengfei Liu

Sensors 2025, 25(10), 3213; https://doi.org/10.3390/s25103213 - 20 May 2025

Abstract

Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. [...] Read more.

Advanced driving assistance systems (ADASs) provide rich data on vehicles and their surroundings, enabling early detection and warning of driving risks. This study proposes a short-term risk prediction method based on in-vehicle perception data, aiming to support real-time risk identification in ADAS environments. A variable sliding window approach is employed to determine the optimal prediction window lead length and duration. The method incorporates Monte Carlo simulation for threshold calibration, Boruta-based feature selection, and multiple machine learning models, including the light gradient-boosting machine (LGBM), with performance interpretation via SHAP analysis. Validation is conducted using data from 90 real-world driving sessions. Results show that the optimal prediction lead time and window length are 1.6 s and 1.2 s, respectively, with LGBM achieving the best predictive performance. Risk prediction effectiveness is enhanced when integrating information across the human–vehicle–road environment system. Key features influencing prediction include vehicle speed, accelerator operation, braking deceleration, and the reciprocal of time to collision (TTCi). The proposed approach provides an effective solution for short-term risk prediction and offers algorithmic support for future ADAS applications. Full article

(This article belongs to the Special Issue Intelligent Traffic Safety and Security)

17 pages, 52654 KiB

Open AccessArticle

Hazelnut Yield Estimation: A Vision-Based Approach for Automated Counting of Hazelnut Female Flowers

by Nicola Giulietti, Sergio Tombesi, Michele Bedodi, Carol Sergenti, Marco Carnevale and Hermes Giberti

Sensors 2025, 25(10), 3212; https://doi.org/10.3390/s25103212 - 20 May 2025

Abstract

Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous [...] Read more.

Accurate estimation of hazelnut yield is crucial for optimizing resource management and harvest planning. Although the number of female flowers on a flowering plant is a reliable indicator of annual production, counting them remains difficult because of their extremely small size and inconspicuous shape and color. Currently, manual flower counting is the only available method, but it is time-consuming and prone to errors. In this study, a novel vision-based method for automatic flower counting specifically designed for hazelnut plants (Corylus avellana) exploiting a commercial high-resolution imaging system and an image-tiling strategy to enhance small-object detection is proposed. The method is designed to be fast and scalable, requiring less than 8 s per plant for processing, in contrast to 30–60 min typically required for manual counting by human operators. A dataset of 2000 labeled frames was used to train and evaluate multiple female hazelnut flower detection models. To improve the detection of small, low-contrast flowers, a modified YOLO11x architecture was introduced by adding a P2 layer, improving the preservation of fine-grained spatial information and resulting in a precision of 0.98 and a Mean Average Precision (mAP@50-95) of 0.89. The proposed method has been validated on images collected from hazelnut groves and compared with manual counting by four experienced operators in the field, demonstrating its ability to detect small, low-contrast flowers despite occlusions and varying lighting conditions. A regression-based bias correction was applied to compensate for systematic counting deviations, further improving accuracy and reducing the mean absolute percentage error to 27.44%, a value comparable to the variability observed in manual counting. The results indicate that the system can provide a scalable and efficient alternative to traditional female flower manual counting methods, offering an automated solution tailored to the unique challenges of hazelnut yield estimation. Full article

(This article belongs to the Section Sensing and Imaging)

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17 pages, 3638 KiB

Open AccessArticle

Biomechanical Effects of a Passive Back-Support Exosuit During Simulated Military Lifting Tasks—An EMG Study

by Muhammad Ammar Marican, Lavern Dharma Chandra, Yunqi Tang, Muhammad Nur Shahril Iskandar, Cheryl Xue Er Lim and Pui Wah Kong

Sensors 2025, 25(10), 3211; https://doi.org/10.3390/s25103211 - 20 May 2025

Abstract

Military operators performing vehicle maintenance work are at times subject to onerous tasks such as lifting and transporting heavy loads, potentially in confined spaces. As this presents a risk for developing musculoskeletal injury, it is of interest to evaluate if a passive back-support [...] Read more.

Military operators performing vehicle maintenance work are at times subject to onerous tasks such as lifting and transporting heavy loads, potentially in confined spaces. As this presents a risk for developing musculoskeletal injury, it is of interest to evaluate if a passive back-support exosuit could help reduce back muscle load. This study used wireless electromyographic (EMG) sensors to evaluate the biomechanical effects of exosuits during lifting tasks. Ten male participants performed military-relevant lifting tasks with and without wearing the exosuit in randomised orders. The lifting tasks included (1) vertical lifts of different weights (15 and 25 kg) onto different platform heights (0.5 m and 1.2 m) and (2) a lateral walk task across 4 m in a confined space while carrying a 39 kg weight. EMG activities of three back muscle groups (longissimus, iliocostalis, and multifidus) were measured and normalised to maximal isometric back extension tasks. The results showed no significant differences in muscle activation between conditions in most lifting tasks, except for a reduction in longissimus muscle activity when using the exosuit during lateral walking. Individual responses varied substantially, with some participants showing reduced muscle activity, while others did not. These findings highlight the challenges in implementing exosuits in reducing back muscle load during military lifting tasks. While passive back-support exosuits may provide benefits to some users, their effectiveness varies among individuals and may be task-dependent. Full article

(This article belongs to the Special Issue Wearable Sensors for Biomechanics Applications—2nd Edition)

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21 pages, 5767 KiB

Open AccessArticle

On the Minimum Quantity of Mobile Sensor Nodes for Full Coverage in Hybrid WSN

by Monik Silva Sousa and João Viana da Fonseca Neto

Sensors 2025, 25(10), 3210; https://doi.org/10.3390/s25103210 - 20 May 2025

Abstract

A main challenge in deploying wireless sensor networks (WSNs) is determining the minimum quantity of sensor nodes required to fully cover the region of interest while avoiding coverage holes. This study proposes a method to compute the number of nodes needed to monitor [...] Read more.

A main challenge in deploying wireless sensor networks (WSNs) is determining the minimum quantity of sensor nodes required to fully cover the region of interest while avoiding coverage holes. This study proposes a method to compute the number of nodes needed to monitor a circular region and a distributed control strategy based on circular formations to move dynamic agents to their desired positions. The method addresses the coverage problem, ensuring that each point in the monitored region is detected without losing connectivity. In addition, the study compares this approach with a sensor node allocation method based on Voronoi diagrams, highlighting the need for an algorithm that computes the desired positions of the agents to provide guaranteed flawless coverage; the proposed method achieves this by obtaining the desired final positions. The hybrid WSN architecture, together with the proposed method, achieves full coverage efficiently and better utilizes the detection circumference of sensors compared to traditional rectangular monitoring regions. Full article

(This article belongs to the Section Sensor Networks)

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20 pages, 8249 KiB

Open AccessArticle

A Wideband and High-Power RF Switching Design

by Xindong Huang, Chengying Chen and Shaokang Zhou

Sensors 2025, 25(10), 3209; https://doi.org/10.3390/s25103209 - 20 May 2025

Abstract

This paper presents an RF switch chip with a wide operating bandwidth from 6 to 18 GHz, designed for RF front-end applications in mobile communications. A series-parallel topology combined with a stacked transistor structure was employed to improve power handling while maintaining low [...] Read more.

This paper presents an RF switch chip with a wide operating bandwidth from 6 to 18 GHz, designed for RF front-end applications in mobile communications. A series-parallel topology combined with a stacked transistor structure was employed to improve power handling while maintaining low insertion loss and high isolation. To further optimize isolation and return loss, LC resonant circuits were introduced by utilizing off-state transistors as capacitive elements. Compared to existing designs, the proposed switch achieved an improved trade-off between bandwidth, power capacity, and port performance. Measurement results showed insertion loss below 1.917 dB, isolation above 38.839 dB, return loss better than 13.075 dB, and 1 dB input compression point above 32 dBm at 12 GHz, confirming the effectiveness and novelty of the broadband design. Full article

(This article belongs to the Section Electronic Sensors)

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27 pages, 1433 KiB

Open AccessArticle

A Hybrid Security Framework for Train-to-Ground (T2G) Communication Using DOA-Optimized BPNN Detection, Bayesian Risk Scoring, and RL-Based Response

by Chaoyuan Sun, Weijiao Zhang, Peng Sun, Hui Wang and Chunhui Yang

Sensors 2025, 25(10), 3208; https://doi.org/10.3390/s25103208 - 20 May 2025

Abstract

With the widespread adoption of wireless communication technologies in modern high-speed rail systems, the Train-to-Ground (T2G) communication system for Electric/Diesel Multiple Units (EMU/DMU) has become essential for train operation monitoring and fault diagnosis. However, this system is increasingly vulnerable to various cyber-physical threats, [...] Read more.

With the widespread adoption of wireless communication technologies in modern high-speed rail systems, the Train-to-Ground (T2G) communication system for Electric/Diesel Multiple Units (EMU/DMU) has become essential for train operation monitoring and fault diagnosis. However, this system is increasingly vulnerable to various cyber-physical threats, necessitating more intelligent and adaptive security protection mechanisms. This paper presents an intelligent security defense framework that integrates intrusion detection, risk scoring, and response mechanisms to enhance the security and responsiveness of the T2G communication system. First, feature selection is performed on the TON_IoT dataset to develop a Dream Optimization Algorithm (DOA)-optimized backpropagation neural network (DOA-BPNN) model for efficient anomaly detection. A Bayesian risk scoring module then quantifies detection outcomes and classifies risk levels, improving threat detection accuracy. Finally, a Q-learning-based reinforcement learning (RL) module dynamically selects optimal defense actions based on identified risk levels and attack patterns to mitigate system threats. Experimental results demonstrate improved performance in both multi-class and binary classification tasks compared to conventional methods. The implementation of the Bayesian risk scoring and decision-making modules leads to a 63.56% reduction in system risk scores, confirming the effectiveness and robustness of the proposed approach in an experimental environment. Full article

(This article belongs to the Section Internet of Things)

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16 pages, 3593 KiB

Open AccessArticle

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

by Thilini S. Karunarathna and Zilu Liang

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

Abstract

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

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

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

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23 pages, 6798 KiB

Open AccessArticle

A Digital Signal Processing-Based Multi-Channel Acoustic Emission Acquisition System with a Simplified Analog Front-End

by Gan Tang

Sensors 2025, 25(10), 3206; https://doi.org/10.3390/s25103206 - 20 May 2025

Abstract

Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and [...] Read more.

Advanced multi-channel acoustic emission (AE) monitoring systems often rely on complex and costly architectures, especially those requiring custom FPGA-based hardware. In this work, we present a digital signal processing (DSP)-based approach to high-performance AE acquisition, implemented using a simplified analog front-end (AFE) and a commercially available synchronous data acquisition (DAQ) card (NI USB-6356). This design eliminates the need for specialized FPGA development, improving accessibility and reducing system complexity. A key feature of the system is the replacement of traditional analog filters with a software-defined digital band-pass filtering module implemented in LabVIEW. This allows for real-time or post-processing filtering with adjustable parameters, enhancing flexibility in data analysis. The system supports 8-channel synchronous sampling at 1.25 MS/s, and performance evaluations demonstrate a dynamic range of 79.22 dB and a signal-to-noise ratio (SNR) of 85.39 dB. These results confirm the system’s ability to maintain high fidelity in AE signal acquisition without the need for dedicated hardware filtering or custom DAQ hardware. The proposed method offers a practical and validated alternative for multi-channel AE monitoring, with potential applications in structural health monitoring, materials testing, and other engineering domains. Full article

(This article belongs to the Special Issue Sensor Data-Driven Fault Diagnosis Techniques)

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