Sensors

14 pages, 3051 KB

Open AccessArticle

An Oscillating-Flow Microfluidic PCR Method for Rapid and Flexible Detection of Periodontal Pathogens

by Zhenqing Li, Yueqing Wang, Bo Yang, Jing Yang, Yuan Zeng, Shinichi Sekine and Yoshinori Yamaguchi

Sensors 2026, 26(7), 2126; https://doi.org/10.3390/s26072126 (registering DOI) - 29 Mar 2026

Abstract

Polymerase chain reaction (PCR) is widely regarded as the gold standard for nucleic acid analysis; however, conventional thermal cycling limits its applicability in rapid and compact analytical systems. Here, we report an oscillating-flow microfluidic PCR method that enables rapid and flexible amplification by [...] Read more.

Polymerase chain reaction (PCR) is widely regarded as the gold standard for nucleic acid analysis; however, conventional thermal cycling limits its applicability in rapid and compact analytical systems. Here, we report an oscillating-flow microfluidic PCR method that enables rapid and flexible amplification by repeatedly shuttling the reaction mixture between two fixed-temperature zones. Unlike continuous-flow PCR, the proposed approach decouples PCR cycle number from microchannel geometry, allowing programmable cycling while reducing chip footprint. To enhance analytical reliability, polymer-assisted surface passivation using polyvinylpyrrolidone was employed to suppress nonspecific adsorption in polydimethylsiloxane (PDMS) microchannels, significantly improving amplification efficiency. Using Porphyromonas gingivalis and Treponema denticola as representative periodontal pathogens, 35-cycle amplification was completed within 20 min with reliable product yield. The proposed method advances oscillating-flow PCR toward a robust analytical strategy for rapid pathogen detection and related microfluidic nucleic acid analysis. Full article

(This article belongs to the Special Issue Intelligent Microfluidics)

19 pages, 2965 KB

Open AccessArticle

Wearable Sensors Reveal Head–Sternum Dissociation as a Latent Deficit in Active Aging

by András Salamon and Gabriella Császár

Sensors 2026, 26(7), 2125; https://doi.org/10.3390/s26072125 (registering DOI) - 29 Mar 2026

Abstract

Background: Traditional functional mobility assessments often fail to detect subclinical postural decline in active aging populations. This study introduces the Head–Sternum Dissociation Index as a novel digital biomarker to identify latent sensorimotor deficits before macroscopic balance failure occurs. Methods: Ninety-four participants (Young, Middle-Aged [...] Read more.

Background: Traditional functional mobility assessments often fail to detect subclinical postural decline in active aging populations. This study introduces the Head–Sternum Dissociation Index as a novel digital biomarker to identify latent sensorimotor deficits before macroscopic balance failure occurs. Methods: Ninety-four participants (Young, Middle-Aged Civil, Middle-Aged Dancers, and Older Adults) performed instrumented limits of stability tasks, specifically functional and lateral reach tests, utilizing a three-sensor inertial measurement unit configuration. Postural strategies were quantified via the Head–Sternum Dissociation Index and the peak ratio of corrective micro-movements, validating the sensor output against a gold-standard force platform. Results: A significant kinematic breakpoint in postural control was identified at age 55 (p < 0.001). However, Middle-Aged Civilians exhibited early kinematic divergence despite maintaining normal Timed Up and Go test performance. Receiver operating characteristic analysis revealed distinct, sex-specific physiological limits: aging males predominantly adopted a rigid “Stiffness” strategy (peak ratio ≤ 1.15, head–sternum dissociation threshold > 0.63°), while females utilized a broader, more permissive “Continuous” strategy (head–sternum dissociation threshold > 0.31°). Notably, recreational rhythmic training (dance) completely neutralized this age-related decay, with middle-aged dancers maintaining highly efficient, youthful stabilization profiles (Cohen’s d = 2.20). Conclusions: The Head–Sternum Dissociation Index, combined with relative corrective frequency, successfully phenotypes early sensorimotor erosion. These findings advocate for the integration of sex-specific kinematic screening into primary care, allowing clinicians to prescribe targeted interventions well before clinical fall risk manifests. Full article

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

► Show Figures

Figure 1

26 pages, 3241 KB

Open AccessArticle

Dynamic Modelling and Control Strategy Analysis of a Lower-Limb Exoskeleton

by Huanrong Xiao, Teng Ran and Afang Jin

Sensors 2026, 26(7), 2124; https://doi.org/10.3390/s26072124 (registering DOI) - 29 Mar 2026

Abstract

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle [...] Read more.

Lower-limb exoskeleton robots play a pivotal role in rehabilitation medicine and assistive augmentation, where precise dynamic modelling and trajectory tracking control are fundamental to effective assistance. Existing models predominantly focus on hip and knee rotational degrees of freedom, with insufficient attention to ankle dynamics and pelvic translation. To address these limitations, this paper establishes a sagittal-plane dynamic model comprising nine generalised coordinates, treating the human lower limb and exoskeleton as an integrated coupled system. A seven-segment kinematic model encompassing the trunk, bilateral thighs, shanks, and feet is constructed via a modified Denavit–Hartenberg parameter method, and dynamic equations are derived using Lagrangian formulation. Three control strategies—PD control, PD with gravity compensation, and the computed torque method—are designed and evaluated through simulations using gait data from five subjects (two self-collected, three from a public dataset) acquired via Vicon motion capture. Results demonstrate that the computed torque method achieves a joint angle tracking root mean square error (RMSE) of 0.59°, representing an 86.3% improvement over conventional PD control, while maintaining a low control torque RMS of 4.44 N·m. The controller exhibits stable tracking performance across walking speeds of 0.4–1.45 m/s, validating the effectiveness of the proposed model and control strategies. Full article

(This article belongs to the Section Sensors and Robotics)

33 pages, 14869 KB

Open AccessArticle

A Flash Group Creation Algorithm for P300 Brain–Computer Interface Integration with Irregular Assistive Technology Keyboard Layouts

by Jane E. Huggins, Palash Biswas, James K. Huggins and Rishabh Chandel

Sensors 2026, 26(7), 2123; https://doi.org/10.3390/s26072123 (registering DOI) - 29 Mar 2026

Abstract

An event-related potential (ERP)-based brain–computer interface (BCI), or P300 BCI, has long been intended for communication access for individuals with severe motor impairments. BCI access to communication tools, websites, and augmentative and alternative communication (AAC) keyboards requires aligning BCI stimuli to screens with [...] Read more.

An event-related potential (ERP)-based brain–computer interface (BCI), or P300 BCI, has long been intended for communication access for individuals with severe motor impairments. BCI access to communication tools, websites, and augmentative and alternative communication (AAC) keyboards requires aligning BCI stimuli to screens with differing numbers of various-sized keys in partially populated grid layouts. Six design priorities were defined for creating and ordering flash groups: identifiability, unpredictability, perceptibility, minimality, anti-adjacency, and equality. Building on the checkerboard paradigm, multiple algorithmic approaches were evaluated on simulated AAC screens to create the magic square paradigm (MSP) for flash group creation for irregular key layouts. The MSP algorithm was then used for BCI access to the dynamic screens of a commercial AAC device that combines text-based and icon-based language representations and the resulting flash groups analyzed for design priorities of anti-adjacency and equality. The 126,944 flash groups created for 5778 selections on AAC screens had 0 groups with side-by-side adjacency, 0.02% with adjacency to an amalgamated key, and 6% with diagonally adjacent keys. The average difference between the shortest and longest flash groups was 1.9 keys. The MSP provides a novel method to access dynamic AAC keyboards with irregular layouts and multiple key sizes. Full article

(This article belongs to the Special Issue Combining Brain-Computer Interfaces and Assistive Biosensing Technologies)

17 pages, 5699 KB

Open AccessArticle

Data-Driven Material Selection for Flexible Wearable Sensors Under Environmental Coupling Conditions

by Yanping Lu, Myun Kim and Hanwen Zhang

Sensors 2026, 26(7), 2122; https://doi.org/10.3390/s26072122 (registering DOI) - 29 Mar 2026

Abstract

Flexible wearable electronics have shown strong potential for medical and health monitoring; however, conventional materials often fail to simultaneously satisfy the requirements of signal stability, wear comfort, and environmental adaptability under dynamic use conditions. To address this issue, this study proposes a data-driven [...] Read more.

Flexible wearable electronics have shown strong potential for medical and health monitoring; however, conventional materials often fail to simultaneously satisfy the requirements of signal stability, wear comfort, and environmental adaptability under dynamic use conditions. To address this issue, this study proposes a data-driven material selection framework for flexible wearable sensors based on the extreme gradient boosting (XGBoost) algorithm. The model integrates user perception, material physical parameters, and environmental coupling performance indicators to enable intelligent material matching and recommendation. Experimental results show that the proposed model achieves a recommendation accuracy of 94.5%, outperforming conventional comparison methods. Among the candidate materials, silver nanowires (AgNWs) exhibit superior overall performance, including a higher signal-to-noise ratio, lower skin-contact impedance, and stronger sweat resistance. In physiological monitoring experiments, the maximum deviation of the sensor response was below 3% under both static and motion conditions. In environmental coupling tests, the recommended material improved the system signal-to-noise ratio by 68% and reduced 24-h sensitivity decay by 75%. These results indicate that the proposed XGBoost-based framework can effectively support material selection for flexible wearable sensors and improve signal reliability and environmental adaptability in complex application scenarios. Full article

(This article belongs to the Special Issue Wearable Devices for Physical Activity and Healthcare Monitoring)

► Show Figures

Figure 1

31 pages, 8420 KB

Open AccessArticle

RTOS-Integrated Time Synchronization for Self-Deployable Wireless Sensor Networks

by Sarah Goossens, Valentijn De Smedt, Lieven De Strycker and Liesbet Van der Perre

Sensors 2026, 26(7), 2121; https://doi.org/10.3390/s26072121 (registering DOI) - 29 Mar 2026

Abstract

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents [...] Read more.

The deployment of Wireless Sensor Networks (WSNs) remains challenging and time consuming due to the manual commissioning, configuration, and maintenance of resource-constrained Internet of Things (IoT) devices. Achieving precise network-wide time synchronization in such systems further increases this deployment complexity. This paper presents a novel Real-Time Operating System (RTOS)-integrated time synchronization method that distributes an absolute Coordinated Universal Time (UTC) reference across the network using a single Global Navigation Satellite System (GNSS)-enabled host. The method extends the semantics of the RTOS tick count by directly linking it to a global time reference. Consequently, sensor nodes obtain a notion of UTC time and can execute time-critical tasks at precisely defined moments without requiring a dedicated Real-Time Clock (RTC) or GNSS module on each sensor node. This design reduces both hardware cost and overall system complexity. Experimental results obtained on custom-developed hardware running FreeRTOS demonstrate a task synchronization error below ±30 μs between the GNSS reference and a sensor node operating at a clock frequency of 32 MHz. Such precise network-wide synchronization enables more efficient channel utilization, reduces power consumption, and improves the accuracy of both local and coordinated task execution across multiple devices in WSNs. It therefore serves as a key enabler for self-deployable WSNs. Full article

(This article belongs to the Special Issue Analysis, Synchronization and Control of Complex IoT and Sensor Networks)

► Show Figures

Figure 1

23 pages, 26982 KB

Open AccessArticle

Free Space Estimation Based on Superpixel Clustering for Assisted Driving

by Oswaldo Vitales, Ruth Aguilar-Ponce and Javier Vigueras

Sensors 2026, 26(7), 2120; https://doi.org/10.3390/s26072120 (registering DOI) - 29 Mar 2026

Abstract

Free space detection in assisted driving applications is essential to provide information to vehicles about traversable surfaces and potential obstacles to be avoided. The current trend in free space detection favors the use of deep learning techniques. However, Deep Neural Networks require extensive [...] Read more.

Free space detection in assisted driving applications is essential to provide information to vehicles about traversable surfaces and potential obstacles to be avoided. The current trend in free space detection favors the use of deep learning techniques. However, Deep Neural Networks require extensive training that considers as many scenarios as possible, which makes it difficult to create a model that can be generalized to all types of surfaces. Additionally, their lack of explainability contrasts with the growing interest in geometrically grounded and safety-oriented design principles for autonomous vehicle systems. To address these limitations, we propose a geometric approach that incorporates coplanarity conditions and normal vector estimation, removing the dependence on datasets for different types of surfaces. Additionally, the stereoscopic images are clustered in superpixels. The use of images clustered in superpixels allows us to obtain shorter processing times, in addition to taking advantage of the spatial and color information provided by the superpixels to increase the robustness of the three-dimensional reconstruction of the scene. Experimental results show that the proposed superpixel-based approach achieves competitive performance compared to unsegmented dense stereo methods, while significantly reducing algorithmic complexity. These results demonstrate the viability of integrating superpixel clustering into stereo-based free space estimation frameworks. Full article

(This article belongs to the Section Vehicular Sensing)

► Show Figures

Graphical abstract

14 pages, 2237 KB

Open AccessArticle

In-Shoe Foot Temperature Patterns During Lying, Sitting and Standing Postures: Baseline Data from Healthy Individuals

by Stephen Mizzi, Tiziana Mifsud, Anabelle Mizzi, Mark Borg, Robert Farrugia and Owen Falzon

Sensors 2026, 26(7), 2119; https://doi.org/10.3390/s26072119 (registering DOI) - 29 Mar 2026

Abstract

This study aimed to establish normative in-shoe plantar foot temperature patterns across three static postures—lying, sitting, and standing—in healthy individuals, providing a clinically relevant baseline for interpreting in-shoe thermograms in diabetic or peripheral arterial disease (PAD) populations. A single-center prospective study included 20 [...] Read more.

This study aimed to establish normative in-shoe plantar foot temperature patterns across three static postures—lying, sitting, and standing—in healthy individuals, providing a clinically relevant baseline for interpreting in-shoe thermograms in diabetic or peripheral arterial disease (PAD) populations. A single-center prospective study included 20 healthy adults (40 limbs; 22–74 years) who underwent vascular and neurological screening prior to data collection. Plantar temperature was continuously recorded using Tarsos^® Smart Insoles with 21 embedded sensors per foot during three consecutive 10 min phases: supine, sitting, and standing. Data were analyzed for regional differences across the toes, metatarsals, arch, and heel using statistical and visual methods. Distinct posture-related temperature patterns were observed. The arch consistently exhibited the highest temperatures, while the toes remained cooler across all phases. Supine positioning resulted in relatively uniform temperature increases, whereas sitting and standing demonstrated more-stable but region-specific patterns, with slower rates of temperature change and more pronounced regional variation. Compared with barefoot thermography, the in-shoe condition showed greater heat retention and reduced evaporative cooling, highlighting the importance of context-specific baseline data. These findings demonstrate the influence of posture on plantar thermal distribution in the in-shoe environment and support the use of embedded monitoring systems for continuous assessment where surface thermography is not feasible. Full article

(This article belongs to the Section Wearables)

► Show Figures

Figure 1

26 pages, 56576 KB

Open AccessArticle

YOLOv10-Intrusion: An Improved YOLOv10-Based Algorithm for Vehicle Area Intrusion Detection

by Chuanyue Jie and Fuyang Ke

Sensors 2026, 26(7), 2118; https://doi.org/10.3390/s26072118 (registering DOI) - 29 Mar 2026

Abstract

In intelligent transportation systems and urban traffic management, accurate vehicle area intrusion detection based on surveillance imagery plays a critical role in ensuring road safety and operational efficiency. However, under real-world road surveillance conditions characterized by complex backgrounds, varying illumination, occlusion, and scale [...] Read more.

In intelligent transportation systems and urban traffic management, accurate vehicle area intrusion detection based on surveillance imagery plays a critical role in ensuring road safety and operational efficiency. However, under real-world road surveillance conditions characterized by complex backgrounds, varying illumination, occlusion, and scale variations, mainstream detection algorithms often suffer from high false detection and missed detection rates, limiting their reliability and practical deployment. To address these challenges, this paper proposes YOLOv10-Intrusion, a high-precision vehicle area intrusion detection framework based on an improved version of YOLOv10s. The proposed algorithm incorporates Omni-Dimensional Dynamic Convolution (ODConv) and a custom-designed RCS_M module to enhance feature extraction and fine-grained recognition capability. In addition, a Bidirectional Feature Pyramid Network (BiFPN) is employed to optimize multi-scale feature fusion at the neck level. These improvements collectively reduce false detections and missed detections while improving model recall and mean Average Precision (mAP). Furthermore, the Wise-IoU (WIoU) loss function replaces the original Complete IoU (CIoU) loss to accelerate convergence and stabilize bounding box regression under complex surveillance conditions. A dedicated vehicle area intrusion dataset is constructed from real-world road surveillance footage, covering five vehicle categories across diverse road environments and lighting conditions. Experimental results demonstrate that, compared with the baseline YOLOv10s, YOLOv10-Intrusion achieves improvements of 1.5, 3.3, 3.6, and 2.8 percentage points in Precision, Recall, mAP@0.5, and mAP@0.5:0.95, respectively, and outperforms other mainstream detection algorithms in vehicle area intrusion detection tasks. Full article

(This article belongs to the Section Intelligent Sensors)

► Show Figures

Figure 1

42 pages, 6313 KB

Open AccessArticle

When Lie Groups Meet Hyperspectral Images: Equivariant Manifold Network for Few-Shot HSI Classification

by Haolong Ban, Junchao Feng, Zejin Liu, Yue Jiang, Zhenxing Wang, Jialiang Liu, Yaowen Hu and Yuanshan Lin

Sensors 2026, 26(7), 2117; https://doi.org/10.3390/s26072117 (registering DOI) - 29 Mar 2026

Abstract

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions [...] Read more.

Hyperspectral imagery (HSI) offers rich spectral signatures and fine-grained spatial structures for remote sensing, but practical HSI classification is often constrained by scarce labels and complex geometric disturbances, including translation, rotation, scaling, and shear. Existing deep models are typically developed under Euclidean assumptions and rely on data-hungry training pipelines, which makes them brittle in the few-shot regime. To address this challenge, we propose EMNet, a Lie-group-based Equivariant Manifold Network for few-shot HSI classification that explicitly encodes geometric invariance and improves discriminative accuracy. EMNet couples an SE(2)-based Equivariance-Guided Module (EGM) to enforce equivariance to translations and rotations with an affine Lie-group-based Characteristic Filtering Convolution (CFC) that models scaling and shearing on the feature manifold while adaptively suppressing redundant responses. Extensive experiments on WHU-Hi-HongHu, Houston2013, and Indian Pines demonstrate state-of-the-art performance with competitive complexity, achieving OAs of 95.77% (50 samples/class), 97.37% (50 samples/class), and 96.09% (5% labeled samples), respectively, and yielding up to +3.34% OA, +6.01% AA, and +4.14% Kappa over the strong DGPF-RENet baseline. Under a stricter 25-samples-per-class protocol with 10 repeated random hold-out splits, EMNet consistently improves the mean accuracy while exhibiting lower variance, indicating better stability to sampling uncertainty. On the city-scale Xiongan New Area dataset with extreme long-tail imbalance (1580 × 3750 pixels, 256 bands, and 5.925 M labeled pixels), EMNet further boosts OA from 85.89% to 93.77% under the 1% labeled-sample protocol, highlighting robust generalization for large-area mapping. Beyond point estimates, we report mean ± SD/SE across repeated splits and provide rigorous statistical validation by computing Yule’s Q statistic for class-wise behavior similarity, performing the Friedman test with Nemenyi post hoc comparisons for multi-method ranking significance, and presenting 95% confidence intervals together with Cohen’s d effect sizes to quantify practical improvement. Full article

(This article belongs to the Special Issue Hyperspectral Sensing: Imaging and Applications)

16 pages, 34530 KB

Open AccessArticle

A Hybrid θ*-APF-Q Framework for Energy-Aware Path Planning of Unmanned Surface Vehicles Under Wind and Current

by Xiaojie Sun, Zhanhong Dong, Xinbo Chen, Lifan Sun and Yanheng An

Sensors 2026, 26(7), 2116; https://doi.org/10.3390/s26072116 (registering DOI) - 29 Mar 2026

Abstract

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer [...] Read more.

Safe and energy-aware navigation is still difficult for unmanned surface vehicles (USVs), especially in cluttered waters where obstacles, smooth motion, and wind or current effects must be considered at the same time. If these issues are handled separately, the path may become longer and the vehicle may turn more often, which raises propulsion effort and hurts stability. To reduce these problems, a hybrid path planning method called

θ^{*}

-APF-Q is proposed, and it combines global planning, learning-based decisions, and local adjustment in a three-layer structure. First, an any-angle

θ^{*}

global planner is employed to generate a near-optimal backbone trajectory by line-of-sight pruning, thereby reducing redundant waypoints and limiting detours. Second, an enhanced tabular Q-learning model is executed in an expanded eight-direction action space, and policy learning is guided by a multi-objective reward that jointly encourages distance reduction, alignment with ocean current and wind-induced forces for energy saving, smooth heading variation to suppress excessive steering, and maintenance of a safety margin near obstacles. Third, an adaptive artificial potential field (APF) module is used for real-time local correction, providing repulsion in high-risk regions and assisting trajectory smoothing to reduce unnecessary turning operations. A decision bias strategy further couples instantaneous APF forces with long-term state–action values, while the influence weight is adaptively adjusted according to environmental complexity. The algorithm is validated on the randomly generated marine grid maps and on the real-world satellite map scenario, with comparisons against a conventional four-direction Q-learning baseline. Across randomized tests, average path length, turning frequency, and the composite energy indicator are reduced by 22.3%, 55.6%, and 26.4%, respectively, and the success rate increases by 16%. The results indicate that integrating global guidance, adaptive learning, and local reactive decision making supports practical, energy-aware USV navigation. Full article

(This article belongs to the Special Issue Intelligent Sensing and Control Technology for Unmanned Vehicles)

► Show Figures

Figure 1

28 pages, 5206 KB

Open AccessArticle

CEA-DETR: A Multi-Scale Feature Fusion-Based Method for Wind Turbine Blade Surface Defect Detection

by Xudong Luo, Ruimin Wang, Jianhui Zhang, Junjie Zeng and Xiaohang Cai

Sensors 2026, 26(7), 2115; https://doi.org/10.3390/s26072115 (registering DOI) - 28 Mar 2026

Abstract

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this [...] Read more.

Wind turbine blade surface defect detection remains challenging due to large variations in defect scales, blurred edge textures, and severe interference from complex backgrounds, which often lead to insufficient detection accuracy and high false and missed detection rates. To address these issues, this paper proposes an improved RTDETR-based detection framework, termed CEA-DETR, for wind turbine blade surface defect inspection. First, a Cross-Scale Multi-Edge feature Extraction (CSME) backbone is designed by integrating multi-scale pooling and edge-enhancement units with a dual-domain feature selection mechanism, enabling effective extraction of fine-grained texture and edge features across different scales. Second, an Efficient Multi-Scale Feature Fusion Network (EMSFFN) is constructed to facilitate deep cross-level feature interaction through adaptive weighted fusion and multi-scale convolutional structures, thereby enhancing the representation of multi-scale defects. Furthermore, an adaptive sparse self-attention mechanism is introduced to reconstruct the AIFI module, strengthening global dependency modeling and guiding the network to focus on critical defect regions under complex background conditions. Experimental results demonstrate that CEA-DETR achieves mAP50 and mAP50:95 of 89.4% and 68.9%, respectively, representing improvements of 3.1% and 6.5% over the RT-DETR-r18 baseline. Meanwhile, the proposed model reduces computational cost (GFLOPs) by 20.1% and parameter count by 8.1%. These advantages make CEA-DETR more suitable for deployment on resource-constrained unmanned aerial vehicles (UAVs), enabling efficient and real-time autonomous inspection of wind turbine blades. Full article

(This article belongs to the Section Industrial Sensors)

► Show Figures

Figure 1

51 pages, 2286 KB

Open AccessReview

Investigation of Heart Rate Variability Indices in Motion Sickness

by Alfonso Maria Ponsiglione, Lorena Guerrini, Simona Pierucci, Vittorio Santoriello, Maria Romano, Marco Recenti, Hannes Petersen, Paolo Gargiulo and Carlo Ricciardi

Sensors 2026, 26(7), 2114; https://doi.org/10.3390/s26072114 (registering DOI) - 28 Mar 2026

Abstract

Motion sickness (MS), or kinetosis, is a condition experienced by some individuals in response to rhythmic or irregular body motion. Multiple studies have explored its neurobiological mechanisms and countermeasures, with the sensory-conflict hypothesis remaining the most accepted explanation. Heart-rate variability (HRV) and electrocardiography [...] Read more.

Motion sickness (MS), or kinetosis, is a condition experienced by some individuals in response to rhythmic or irregular body motion. Multiple studies have explored its neurobiological mechanisms and countermeasures, with the sensory-conflict hypothesis remaining the most accepted explanation. Heart-rate variability (HRV) and electrocardiography provide complementary autonomic nervous system perspectives that may support MS assessments. From an applied viewpoint, reliable HRV markers could enable the early detection and continuous monitoring of MS in real-world contexts, such as autonomous vehicles, where passenger comfort and safety are critical, motivating contact-free cardiac sensing for unobtrusive monitoring. This systematic review examines the value of HRV indices in MS, conducted under PRISMA guidelines across PubMed, Scopus, and the Web of Science. The included studies were grouped into four categories based on the methods used to induce MS: mechanical stimulus, real trip, visual stimulus, and virtual reality. Aggregated findings indicate that frequency–domain metrics, particularly the low frequency (LF)/high frequency (HF) ratio, HF power, and mean heart rate (mHR), are most frequently reported in relation to MS. Overall, autonomic dysregulation likely contributes to MS susceptibility, but standardized protocols are needed to validate HRV as a reliable marker. Full article

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

► Show Figures

Figure 1

20 pages, 1054 KB

Open AccessArticle

Closed-Form Approximations of Range Mutual Information for Integrated Sensing and Communication Systems

by Zhuoyun Lai, Hao Luo, Yinlu Wang, Yue Zhang and Biao Jin

Sensors 2026, 26(7), 2113; https://doi.org/10.3390/s26072113 (registering DOI) - 28 Mar 2026

Abstract

Sensing mutual information (SMI) is widely adopted as a performance metric for integrated sensing and communication (ISAC) to enhance both sensing and communication capabilities. However, conventional approaches derive SMI from amplitude and phase, whereas an explicit evaluation of range mutual information (RMI) remains [...] Read more.

Sensing mutual information (SMI) is widely adopted as a performance metric for integrated sensing and communication (ISAC) to enhance both sensing and communication capabilities. However, conventional approaches derive SMI from amplitude and phase, whereas an explicit evaluation of range mutual information (RMI) remains absent. In this paper, we investigate a novel closed-form approximation of RMI for ISAC. We first derive an explicit expression for the posterior probability density function (PDF) of the target range, which is formulated as a function of the signal’s autocorrelation and cross-correlation. Furthermore, we show that under high signal-to-noise ratio (SNR), the estimated range PDF approximates a Gaussian distribution in the sensing-unconstrained scenario and a truncated Gaussian distribution in the sensing-constrained scenario. Finally, we derive closed-form approximations of the RMI in both scenarios under high SNR. In the sensing-unconstrained scenario, the RMI is proportional to the delay interval, root-mean-square bandwidth, and SNR. In the constrained scenario, we obtain a closed-form RMI approximation by introducing an entropy correction term that quantifies the impact of boundary constraints. Additionally, we employ a maximum likelihood estimation (MLE) method to assess range estimation performance. Simulation results validate the accuracy of the theoretical results and the effectiveness of the proposed approximations. Full article

(This article belongs to the Section Communications)

24 pages, 4811 KB

Open AccessArticle

Lightweight Power Line Defect Detection Based on Improved YOLOv8n

by Yuhan Yin, Xiaoyi Liu, Kunxiao Wu, Ruilin Xu, Jianyong Zheng and Fei Mei

Sensors 2026, 26(7), 2112; https://doi.org/10.3390/s26072112 (registering DOI) - 28 Mar 2026

Abstract

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling [...] Read more.

To address the challenges of small targets, severe background clutter, and high deployment cost in UAV-based power-line defect detection, this paper proposes a lightweight defect detection model based on an improved YOLOv8n. In the downsampling stage, we design an improved lightweight adaptive downsampling module (ADownPro) to replace part of conventional convolutions, which uses a dual-branch parallel structure for stronger feature interaction and depthwise separable convolutions (DSConv) for complexity reduction. In the feature extraction stage, an integration of cross-stage partial connections and partial convolution (CSPPC) is proposed to replace the C2F module for efficient multi-scale feature fusion. In the detection head, mixed local channel attention (MLCA), which combines channel-spatial information and local–global contextual features, is introduced to strengthen defect-focused representations under complex backgrounds. For the loss function, a scale-annealed mixed-quality EIoU loss (SAMQ-EIoU) is proposed by combining iso-center scale transformation, scale factor annealing and focal-style quality reweighting to improve localization accuracy at high IoU thresholds. Experiments on a constructed dataset covering six typical defect categories show that the improved YOLOv8n achieves 91.4% mAP@0.50 and 64.5% mAP@0.50:0.95, with only 1.59 M parameters and 4.9 GFLOPs. Compared with mainstream detectors, the proposed model achieves a better balance between detection accuracy and lightweight design. In particular, compared with the recently proposed YOLOv8n-DSN and IDD-YOLO, it improves mAP@0.50 by 0.6% and 0.8%, and mAP@0.50:0.95 by 1.2% and 4.8%, respectively, while further reducing the parameter count by 1.00 M and 1.26 M, and the FLOPs by 1.7 G and 0.2 G. Moreover, the cross-dataset evaluation on the public UPID and SFID datasets further demonstrate the robustness and generalization ability of the proposed method. Full article

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

21 pages, 5258 KB

Open AccessArticle

Exploring the Potential of Multispectral Imaging for Automatic Clustering of Archeological Wall Painting Fragments

by Piercarlo Dondi, Lucia Cascone, Chiara Delledonne, Michela Albano, Elena Mariani, Marina Volonté, Marco Malagodi and Giacomo Fiocco

Sensors 2026, 26(7), 2111; https://doi.org/10.3390/s26072111 (registering DOI) - 28 Mar 2026

Abstract

The digital reconstruction of damaged archeological wall paintings is a challenging task due to severe material degradation, high fragmentation, and the lack of reference images. A crucial preliminary step is the separation and grouping of fragments originating from different wall paintings, which are [...] Read more.

The digital reconstruction of damaged archeological wall paintings is a challenging task due to severe material degradation, high fragmentation, and the lack of reference images. A crucial preliminary step is the separation and grouping of fragments originating from different wall paintings, which are often found mixed together at archeological sites. To address this issue, we explored the potential of multispectral imaging (MSI) for unsupervised fragment clustering, aiming to assess whether integrating multiple spectral bands can enhance fragment discrimination compared to using the visible band alone. As a test set, we examined five groups of wall painting fragments from a Roman domus (1st c. BC–1st c. AD) provided by the Archaeological Museum of Cremona (Italy). Images were acquired using the Hypercolorimetric Multispectral Imaging (HMI) system developed by Profilocolore^® Srl (Rome, Italy). Specifically, we considered visible reflectance (VIS), infrared reflectance (IR), infrared false color (IRFC), and Ultraviolet-induced Fluorescence (UVF) images. Through a systematic benchmarking study, we compared several state-of-the-art feature extraction and clustering methods across single- and multi-band configurations. Results show that combining MSI data can substantially enhance the system’s ability to correctly separate and group fragments, indicating a promising direction for future research. Full article

(This article belongs to the Special Issue Advances in Multispectral Sensing, Imaging Techniques and Computational Applications in Cultural Heritage)

27 pages, 6255 KB

Open AccessArticle

Lightweight Safety Helmet Wearing Detection Algorithm Based on GSA-YOLO

by Haodong Wang, Qiang Zhou, Zhiyuan Hao, Wentao Xiao and Luqing Yan

Sensors 2026, 26(7), 2110; https://doi.org/10.3390/s26072110 (registering DOI) - 28 Mar 2026

Abstract

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting [...] Read more.

Electric power station confined spaces are high-risk and complex environments characterized by significant illumination variations. Whether safety helmets are properly worn directly affects the operational safety of workers in confined spaces. However, helmet detection in such environments faces several challenges, including drastic lighting changes and difficulties in small-object detection. Moreover, existing object detection models typically contain a large number of parameters, making real-time helmet detection difficult to deploy on field devices with limited computational resources. To address these issues, this paper proposes a lightweight safety helmet wearing detection algorithm named GSA-YOLO. To mitigate the effects of severe illumination variation and detail loss in confined spaces, a GCA-C2f module integrating GhostConv and the CBAM attention mechanism is embedded into the backbone network. This design reduces the number of parameters and computational cost while enhancing the model’s feature extraction capability under challenging lighting conditions. To improve detection performance for occluded targets, an improved efficient channel attention (I-ECA) mechanism is introduced into the neck structure, which suppresses irrelevant channel features and enhances occluded object detection accuracy. Furthermore, to alleviate missed detections of small objects and inaccurate localization under low-light conditions, a P2 detection branch is added to the head, and the WIoU loss function is adopted to dynamically adjust the weights of hard and easy samples, thereby improving small-object detection accuracy and localization robustness. A confined space helmet detection dataset containing 5000 images was constructed through on-site data collection for model training and validation. Experimental results demonstrate that the proposed GSA-YOLO achieves an mAP@0.5 of 91.2% on the self-built dataset with only 2.3 M parameters, outperforming the baseline model by 2.9% while reducing the parameter count by 23.6%. The experimental results verify that the proposed algorithm is suitable for environments with significant illumination variation and small-object detection challenges. It provides a lightweight and efficient solution for on-site helmet detection in confined space scenarios, thereby contributing to the reduction in industrial safety accidents. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

29 pages, 7994 KB

Open AccessArticle

MBFTFuse: A Triple-Path Adversarial Network Based on Modality Balancing and Feature-Tracing Compensation for Infrared and Visible Image Fusion

by Mingxi Chen, Bingting Zha, Rui Yang, Yuran Tan, Shaojie Ma and Zhen Zheng

Sensors 2026, 26(7), 2109; https://doi.org/10.3390/s26072109 (registering DOI) - 28 Mar 2026

Abstract

Infrared and visible image fusion aims to integrate complementary information from heterogeneous images captured by different optical sensors based on distinct imaging principles; however, existing methods often exhibit modality bias, leading to weakened targets or the loss of crucial texture details. To address [...] Read more.

Infrared and visible image fusion aims to integrate complementary information from heterogeneous images captured by different optical sensors based on distinct imaging principles; however, existing methods often exhibit modality bias, leading to weakened targets or the loss of crucial texture details. To address this, we propose MBFTFuse, an adversarial fusion network based on modality balancing and feature tracing, which consists of a triple-path generator and dual discriminators. The architecture employs a generator with a triple-path structure: a central modality-balancing path for deep feature fusion and dual edge feature-tracing paths for modality-specific enhancement. Specifically, a multi-cognitive modality-balancing module is introduced to achieve feature weight equilibrium, while a Feature-Tracing Attention Module self-enhances single-modality features to compensate for information loss in the fusion results. Furthermore, a pixel loss based on intensity histograms is designed to optimize inter-modal balance at the pixel level. Comparative experiments against nine state-of-the-art methods across three public datasets demonstrate that MBFTFuse effectively highlights infrared targets while preserving intricate visible textures. The superior performance of this method in both quantitative metrics and downstream object detection tasks contributes to extending the boundaries of sensor-driven computer vision technologies. Full article

(This article belongs to the Special Issue Sensing and Imaging in Computer Vision)

16 pages, 66819 KB

Open AccessArticle

A DTW-Based Spatio-Temporal Synchronization Method for Radar and Camera Fusion

by Jingjing Li, Juan Liu, Xiuping Li, Chengliang Zhong and Xiyan Sun

Sensors 2026, 26(7), 2108; https://doi.org/10.3390/s26072108 (registering DOI) - 28 Mar 2026

Abstract

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, [...] Read more.

Roadside perception systems, also known as roadside units (RSUs), are critical in Vehicle-to-Everything (V2X) applications, yet spatio-temporal asynchrony between multiple sensors severely compromises the accuracy of fusion. In this paper, a spatio-temporal synchronization method for millimeter-wave (MMW) radar and camera fusion is proposed, integrating target matching based on dynamic time warping (DTW) with spatio-temporal parameter estimation. Leveraging the advantages of DTW in time-series alignment to calculate the similarity between radar and visual trajectories enables target matching and parameter estimation in sparse scenes. This method was validated on a real-world dataset containing over 30 pedestrian trajectories, covering scenarios with varying densities ranging from one to six pedestrians. The results indicate a temporal offset of 0.116 s between the camera and radar. Following synchronization, the average spatial deviation decreased from 1.4358 to 0.1074 m in the x-direction (i.e., across the road) and from 3.0732 to 0.1775 m in the y-direction (i.e., along the road). Consequently, this method provides an efficient solution for deploying roadside perception systems in sparse traffic environments. Full article

(This article belongs to the Collection Signal Processing, Control, and Estimation for Intelligent Sensor Systems)

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