You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.

Search for Articles:

Title / Keyword

Author / Affiliation / Email

Journal

Article Type

Advanced Search

Section

Special Issue

Volume

Issue

Number

Page

Logical OperatorOperator

Search Text

Search Type

Enhanced Reaction Time Measurement System Based on 3D Accelerometer in Athletics
Modular IoT Sensing and Data Pipeline for Digital Twin–Enabled Real-Time Aircraft Structural Health Monitoring
Flexible Sensor Foil Based on Polymer Optical Waveguide for Haptic Assessment
A Codesign Framework for the Development of Next Generation Wearable Computing Systems
Comparative Analysis of the Accuracy and Robustness of the Leap Motion Controller 2

Journal Description

Sensors

Sensors is an international, peer-reviewed, open access journal on the science and technology of sensors, published semimonthly online by MDPI. The Polish Society of Applied Electromagnetics (PTZE), Japan Society of Photogrammetry and Remote Sensing (JSPRS), Spanish Society of Biomedical Engineering (SEIB), International Society for the Measurement of Physical Behaviour (ISMPB), Chinese Society of Micro-Nano Technology (CSMNT) and more are affiliated with Sensors and their members receive discounts on the article processing charges.

Open Access — free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SCIE (Web of Science), PubMed, MEDLINE, PMC, Ei Compendex, Inspec, Astrophysics Data System, and other databases.
Journal Rank: JCR - Q2 (Instruments and Instrumentation) / CiteScore - Q1 (Instrumentation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 17.8 days after submission; acceptance to publication is undertaken in 2.6 days (median values for papers published in this journal in the second half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Testimonials: See what our editors and authors say about Sensors.
Companion journals for Sensors include: Chips, Targets, AI Sensors and IJMD.
Journal Cluster of Instruments and Instrumentation: Actuators, AI Sensors, Instruments, Metrology, Micromachines and Sensors.

Impact Factor: 3.5 (2024); 5-Year Impact Factor: 3.7 (2024)

Imprint Information Journal Flyer Open Access ISSN: 1424-8220

Latest Articles

23 pages, 7609 KB

Open AccessArticle

Performance Evaluation of Multi-Modal Radar Signal Processing in Dense Co-Existent Environments

by Anum Pirkani, Fatemeh Norouzian, Ali Bekar, Muge Bekar and Marina Gashinova

Sensors 2026, 26(8), 2317; https://doi.org/10.3390/s26082317 (registering DOI) - 9 Apr 2026

The wide-scale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals [...] Read more.

The wide-scale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

► Show Figures

Figure 1

27 pages, 3278 KB

Open AccessArticle

Multimodal PPG-Based Arrhythmia Detection Using a CLIP-Initialized Multi-Task U-Net and LLM-Assisted Reporting

by Youngho Huh, Minhwan Noh, Dongwoo Ji, Yuna Oh and Sukkyu Sun

Sensors 2026, 26(8), 2316; https://doi.org/10.3390/s26082316 (registering DOI) - 9 Apr 2026

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, [...] Read more.

Photoplethysmography (PPG) has emerged as an attractive modality for non-invasive cardiovascular monitoring due to its low cost, unobtrusive nature, and ubiquity in consumer wearable devices. Despite its potential, existing PPG-based arrhythmia detection systems remain limited in scope: (i) most target only atrial fibrillation, (ii) temporal localization of abnormal segments is rarely provided, and (iii) deep learning models lack explainability, hindering adoption in clinical workflows. We present a comprehensive and fully integrated framework for multi-class arrhythmia detection, segmentation, and explainability based on PPG waveforms, Heart Rate Variability (HRV), and structured clinical metadata. The proposed system introduces a CLIP-style contrastive learning module aligning PPG waveforms with clinical variables and rhythm-state textual descriptions using BioBERT; a multitask U-Net architecture performing 4-class classification and 1D segmentation; a Retrieval-Augmented Generation (RAG) pipeline leveraging Gemini Flash large language models to produce guideline-grounded diagnostic reports; and a real-time Streamlit-based web platform supporting inference, visualization, and database storage. The system significantly improves classification accuracy (from 86.27% to 91.19%) and segmentation Dice (from 0.5815 to 0.7167). These results demonstrate the feasibility of a robust, multimodal, and explainable PPG-based arrhythmia monitoring system for real-world applications. Full article

(This article belongs to the Section Wearables)

► Show Figures

Figure 1

33 pages, 6306 KB

Open AccessArticle

High-Fidelity Weak Signal Extraction for Coiled Tubing Acoustic Telemetry via Micro-Lever Suspension and Joint Denoising

by Yingjian Xie, Hao Geng, Zhihao Wang, Haojie Xu, Hu Han and Dong Yang

Sensors 2026, 26(8), 2315; https://doi.org/10.3390/s26082315 - 9 Apr 2026

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. [...] Read more.

In Coiled Tubing (CT) acoustic telemetry, the reliability of surface signal reception is severely challenged by the “contact dead zone” of traditional probes and complex nonstationary environmental noise. To address these issues, this paper proposes a hardware-software integrated solution for high-fidelity signal extraction. In terms of hardware, a novel pickup probe based on the micro-lever principle is developed. By utilizing a pivoted lever structure with an optimized arm ratio of 2.6 to 1 and a full pressure-balanced mechanism, the design physically overcomes the contact dead zone inherent in traditional pressure-compensating probes and effectively isolates low frequency common-mode interference through a lateral floating architecture. In terms of software, a joint denoising model combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and wavelet thresholding is proposed. A cross-correlation coefficient criterion is introduced to adaptively screen intrinsic mode functions and eliminate residual fluid turbulence noise. Field experiments on a 1500 ft full-scale circulation loop demonstrate that the proposed probe improves the detection sensitivity of the radial breathing mode by approximately 20.6 dB compared to the baseline, while effectively eliminating stick-slip friction noise during dynamic tripping. Furthermore, the joint algorithm increases the Signal to noise Ratio by an additional 16.9 dB under typical pumping conditions of 0.5 bpm, with a normalized cross-correlation exceeding 0.96. These results verify that the proposed method effectively solves the bottleneck of weak signal detection in deep wells, providing robust technical support for CT telemetry operations. Full article

(This article belongs to the Section Industrial Sensors)

► Show Figures

Figure 1

19 pages, 623 KB

Open AccessArticle

A Unified AI-Driven Multimodal Framework Integrating Visual Sensing and Wearable Sensors for Robust Human Motion Monitoring in Biomedical Applications

by Qiang Chen, Xiaoya Wang, Ranran Chen, Surui Hua, Yufei Li, Siyuan Liu and Yan Zhan

Sensors 2026, 26(8), 2314; https://doi.org/10.3390/s26082314 - 9 Apr 2026

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to [...] Read more.

This study proposes a unified multimodal temporal motion state perception framework for optical imaging-oriented biomedical applications, integrating visual skeleton sequences, inertial measurement unit (IMU) signals, and surface electromyography (EMG) signals. The framework utilizes modality-specific encoders and a cross-modal temporal alignment attention mechanism to explicitly model temporal offsets from heterogeneous sensing streams. A multimodal temporal Transformer backbone is introduced to capture long-range motion dependencies and cross-modal interactions, while an uncertainty-aware fusion module dynamically allocates weights based on modality confidence. Experimental results demonstrate that the proposed approach achieves an accuracy of 94.37%, an F1-score of 93.95%, and a mean average precision of 96.02%, outperforming mainstream baseline models. Robustness evaluations further confirm stable performance under visual occlusion and sensor noise. These results indicate that the framework provides a highly accurate and robust solution for rehabilitation assessment, sports training monitoring, and wearable intelligent interaction systems. Full article

(This article belongs to the Special Issue Application of Optical Imaging in Medical and Biomedical Research)

► Show Figures

Figure 1

17 pages, 5304 KB

Open AccessArticle

Design and Experimental Evaluation of a Shoulder Assistive Exoskeleton for Insulator Replacement

by Haoyuan Chen, Jia Yao, Ming Li, Hongwei Hu, Zhan Yang, Siyu Tu, Yalun Liu, Zimeng Wang and Zhao Guo

Sensors 2026, 26(8), 2313; https://doi.org/10.3390/s26082313 - 9 Apr 2026

Aiming to reduce muscle fatigue and prevent occupational injuries caused by prolonged lifting in insulator replacement operations, this study presents the design of an upper-limb exoskeleton. Firstly, this study performs kinematic analysis and phase segmentation of the lifting motion in the insulator replacement [...] Read more.

Aiming to reduce muscle fatigue and prevent occupational injuries caused by prolonged lifting in insulator replacement operations, this study presents the design of an upper-limb exoskeleton. Firstly, this study performs kinematic analysis and phase segmentation of the lifting motion in the insulator replacement operation. Based on the analysis, in terms of mechanical structure, the proposed upper-limb exoskeleton adopts a unilateral three-degree-of-freedom shoulder mechanism that biomimics the human glenohumeral joint, which reduces the misalignment between the exoskeleton and the human body. Meanwhile, a waist–back support structure is integrated into the exoskeleton to realize a more reasonable torque transmission path. In terms of the control strategy, based on the operation’s phase segmentation and dynamic modeling of the human upper limb, this study develops a neural network-based assistive control algorithm for insulator replacement operations, enabling the exoskeleton to provide phase-specific torque output. Experimental results demonstrate that, under a simulated insulator replacement operation with a 20 kg load, the exoskeleton significantly reduces the subject’s sEMG activity of the biceps brachii and triceps brachii, effectively alleviating muscle fatigue. Full article

(This article belongs to the Section Sensors and Robotics)

► Show Figures

Figure 1

23 pages, 5012 KB

Open AccessArticle

Field Evaluation of Temperature and Wind-Speed Sensor Performance Under Natural Icing Conditions for Power Meteorological Monitoring

by Hualong Zheng and Xiaoyu Liu

Sensors 2026, 26(8), 2312; https://doi.org/10.3390/s26082312 - 9 Apr 2026

Micro-meteorological monitoring systems have been widely deployed in power grids, providing essential data to support the prevention and mitigation of ice- and wind-related disasters. However, understanding of the associated error mechanisms and quantitative evaluations under freezing rain and snow remains limited, particularly in [...] Read more.

Micro-meteorological monitoring systems have been widely deployed in power grids, providing essential data to support the prevention and mitigation of ice- and wind-related disasters. However, understanding of the associated error mechanisms and quantitative evaluations under freezing rain and snow remains limited, particularly in complex field environments. This study presents a field-based quantitative assessment of two key variables, air temperature and wind speed, based on comparative observations collected over multiple winter icing cycles. We analyze the coupled effects of low temperature, ice accretion, and solar radiation on temperature measurements through multi-configuration sensor comparison, and characterize the dynamic response of cup anemometers under icing conditions using cross-correlation lag analysis. Results show that temperature error is dominated by sensor installation configuration and solar radiation. Under weak solar radiation, unshielded sensors tend to record lower temperatures than a standard Stevenson screen, but once radiation exceeds 200 W/m², they warm rapidly and exhibit maximum positive biases of ~8–10 °C. Ice accretion further induces a cold bias of ~1 °C and a response lag of 5–18 min, while suppressing the rapid warming driven by shortwave radiation. For wind measurements, cup anemometers show clear underestimation during ice accretion, with the error increasing nonlinearly with ice thickness to ~20% before freezing-induced failure occurs. These findings provide a basis for improved sensor deployment and interpretation of field monitoring data in cold, humid, and icing-prone environments, although the quantitative results are site-dependent. Full article

(This article belongs to the Special Issue Remote Sensors for Climate Observation and Environment Monitoring)

► Show Figures

Figure 1

27 pages, 2114 KB

Open AccessArticle

MSFE-YOLO: A Steel Surface Defect Detection Algorithm Integrating Multi-Scale Frequency Domain and Defect-Aware Attention

by Siqi Su, Jiale Shen, Peiyi Lin, Wanhe Tang, Weijie Zhang and Zhen Chen

Sensors 2026, 26(8), 2311; https://doi.org/10.3390/s26082311 - 9 Apr 2026

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, [...] Read more.

Detecting surface defects on steel products is crucial for maintaining quality standards in industrial manufacturing. However, existing detection algorithms face several challenges, including the difficulty of capturing multi-scale defect characteristics with fixed receptive fields, insufficient utilization of defect edge and frequency domain features, and simplistic feature fusion strategies. In response to the above challenges, this paper proposed the Multi-Scale Frequency-Enhanced YOLO (MSFE-YOLO) algorithm that integrates multi-scale frequency domain enhancement with defect-aware attention mechanisms. First, a Multi-Scale Frequency-Enhanced Convolution (MSFC) module was constructed, which extracted multi-scale spatial features in parallel through depth-adaptive dilated convolutions, explicitly modeled high-frequency edge information using the Laplacian operator, and achieved adaptive fusion of multi-branch features via learnable weights. Second, a Cross-Stage Partial with Multi-Scale Defect-Aware Attention (C2MSDA) module was designed, integrating Sobel operator-based edge perception, multi-scale spatial attention, and adaptive channel attention to collaboratively enhance features across spatial, channel, and edge domains through a gated fusion strategy. Finally, an Adaptive Feature Fusion Enhancement (AFFE) module was proposed to achieve adaptive aggregation of multi-level features through a data-driven weight generation network and cross-scale feature interaction mechanism. Experimental results on the NEU-DET and GC10-DET datasets demonstrated that MSFE-YOLO achieved the mAP@0.5 of 79.8% and 66.7%, respectively, which were 1.7% and 2.1% higher than the benchmark model YOLOv11s respectively, while maintaining an inference speed of 89.3 FPS, which satisfied the real-time detection requirements in industrial scenarios. Full article

(This article belongs to the Special Issue AI-Based Visual Sensing for Object Detection)

► Show Figures

Figure 1

19 pages, 1977 KB

Open AccessArticle

Fe-Doped Carbon Quantum Dots with Magneto-Fluorescent Dual Modality for Fluorescence and Magnetic Resonance Readouts

by Xianzhi Chub, Hamzah Kiran, Bableen Kaur, Mohammad Khalid Mahmoud, Taleen Alkhayyat, Avery Ramirez, Alexis Kim, Yunfei Zhang, Shuo Wu, Matthew Yacoboski and He Wei

Sensors 2026, 26(8), 2310; https://doi.org/10.3390/s26082310 - 9 Apr 2026

Magneto-fluorescent carbon quantum dots (CQDs) promise compact, dual-readout nanomaterials; however, achieving pronounced photoluminescence alongside magnetic functionality in a simple, scalable formulation remains difficult, especially for emerging doped CQDs. Here, we report Fe-doped carbon quantum dots (Fe-CQDs) as an emerging quantum-dot platform that integrates [...] Read more.

Magneto-fluorescent carbon quantum dots (CQDs) promise compact, dual-readout nanomaterials; however, achieving pronounced photoluminescence alongside magnetic functionality in a simple, scalable formulation remains difficult, especially for emerging doped CQDs. Here, we report Fe-doped carbon quantum dots (Fe-CQDs) as an emerging quantum-dot platform that integrates fluorescence with magnetic-resonance (MR) relaxometry within a single ultrasmall, carbonaceous nanostructure. To enable this, Fe-CQDs are prepared through a straightforward two-step, low-temperature route that uses a magnetic deep eutectic solvent precursor followed by mild carbonization in air at atmospheric pressure. Under UV excitation, the Fe-CQDs display bright blue emission centered at 439 nm, and their optical behavior is characterized by UV-Vis absorption, photoluminescence spectroscopy, and fluorescence microscopy. Meanwhile, dynamic light scattering indicates a narrowly distributed nanoscale hydrodynamic diameter, and X-ray diffraction together with FT-IR supports a carbonaceous framework enriched with oxygenated surface functionalities, consistent with aqueous dispersibility and environmentally responsive photophysics in water, while XPS supports Fe incorporation in an Fe(III)-dominated chemical environment. Importantly, Fe incorporation enables intrinsic MR relaxometric readout, establishing an intrinsic fluorescence/MR dual modality. As a proof-of-concept, Fe-CQDs were tested with a representative per- and polyfluoroalkyl substance (PFAS), showing parallel fluorescence and MR response trends at ppm levels in natural water matrices from Millerton Lake with Stern–Volmer analysis and a NaCl-based ionic strength control. Overall, these results position Fe-CQDs as a versatile magneto-fluorescent nanomaterial for dual-readout screening workflows and motivate future surface engineering and dopant tuning to improve selectivity and expand toward multi-modal readouts. Full article

(This article belongs to the Special Issue Advanced Magnetic and Fluorescent Nanomaterial Sensors: Design, Development, and Application)

► Show Figures

Figure 1

34 pages, 7604 KB

Open AccessArticle

Geometrically Optimized FDM-Printed Conductive TPU Bend Sensors for Hand Rehabilitation

by Ahmet Özkurt, Damla Gürkan Kuntalp, Ozan Kayacan, Özlem Kayacan and Selnur Narin Aral

Sensors 2026, 26(8), 2309; https://doi.org/10.3390/s26082309 (registering DOI) - 9 Apr 2026

Flexible resistive bend sensors are essential for monitoring human movement in smart rehabilitation and soft robotics. However, widespread adoption is currently hindered by a trade-off between the high cost of metal-film technologies and the performance degradation (significant hysteresis and non-linearity) of low-cost carbon/polymer [...] Read more.

Flexible resistive bend sensors are essential for monitoring human movement in smart rehabilitation and soft robotics. However, widespread adoption is currently hindered by a trade-off between the high cost of metal-film technologies and the performance degradation (significant hysteresis and non-linearity) of low-cost carbon/polymer composites. This study presents a geometrically customizable bending sensor fabricated from conductive thermoplastic polyurethane (TPU) using Fused Deposition Modeling (FDM) technology as an accessible alternative to commercial sensors. By parametrically optimizing physical dimensions—including trace width, layer thickness, and pattern geometry—the sensors were tailored to achieve target resistance values within a target window of 20–50 kΩ (achieved: ~44 kΩ nominal) for specific finger-joint applications. Electromechanical characterization revealed a negative gauge factor (GF), where resistance decreases upon bending or elongation due to conductive pathway formation and densification within the polymer matrix. This behavior cannot affect sensor operation, and required bend-resistance responses were acquired using geometrical optimization. To compensate for inherent viscoelastic-induced hysteresis and non-linear behavior, a third-degree polynomial modeling approach was implemented. This modeling approach yielded a coefficient of determination (R²) of approximately 0.90. Compared to standard commercial sensors, the proposed FDM-printed design successfully overcomes geometric limitations while offering a cost-effective, high-performance solution for tailor-made wearable technologies and smart rehabilitation gloves. Full article

(This article belongs to the Section Sensors Development)

► Show Figures

Figure 1

15 pages, 2633 KB

Open AccessArticle

A Sensitive Multichannel Fluorescent Polymer Sensor Array for the Detection of Protein Fluctuations in Serum

by Junwhee Yang, Colby Alves, Kanwal Nazir, Mingdi Jiang, Nicolas Araujo and Vincent M. Rotello

Sensors 2026, 26(8), 2308; https://doi.org/10.3390/s26082308 - 9 Apr 2026

Serum contains diverse proteins whose concentrations vary with pathological conditions such as cancer, liver disease, neurological disorder, and infections. Conventional methods like serum protein electrophoresis (SPEP) and enzyme-linked immunosorbent assay (ELISA) are gold standards for protein identification; however, they are time-consuming and can [...] Read more.

Serum contains diverse proteins whose concentrations vary with pathological conditions such as cancer, liver disease, neurological disorder, and infections. Conventional methods like serum protein electrophoresis (SPEP) and enzyme-linked immunosorbent assay (ELISA) are gold standards for protein identification; however, they are time-consuming and can miss abnormal serum protein levels. Inspired by chemical nose sensing based on selective sensor–analyte interactions, we synthesized five pyrene-conjugated fluorescent polymers (PFPs) with distinct side-chain head groups to construct a multichannel fluorescence sensor array. These polymers were screened for sensitivity to changes in serum protein levels using linear discriminant analysis (LDA), a machine learning method. This process led to the successful discovery of two PFPs that effectively detect protein level fluctuations. These PFPs provided a sensitive sensor array capable of generating a high-content response pattern (fingerprint) with six fluorescence channels. This sensor array successfully discriminated protein level fluctuations in serum with 98% jackknife classification accuracy and 95% unknown identification accuracy. This polymer sensor array holds strong potential as a diagnostic tool for serum-based samples and can be extended to other applications related to protein identification. Full article

(This article belongs to the Special Issue Design and Application of Nanosensor Arrays)

► Show Figures

Graphical abstract

21 pages, 1405 KB

Open AccessArticle

Trust-Aware and Energy-Efficient Federated Learning for Secure Sensor Networks at the Edge

by Manuel J. C. S. Reis

Sensors 2026, 26(8), 2307; https://doi.org/10.3390/s26082307 - 9 Apr 2026

The widespread adoption of large-scale sensor networks in privacy-sensitive and safety-critical applications has intensified the demand for secure, trustworthy, and energy-efficient learning mechanisms at the network edge. Federated learning has emerged as a promising paradigm for privacy preservation by enabling collaborative model training [...] Read more.

The widespread adoption of large-scale sensor networks in privacy-sensitive and safety-critical applications has intensified the demand for secure, trustworthy, and energy-efficient learning mechanisms at the network edge. Federated learning has emerged as a promising paradigm for privacy preservation by enabling collaborative model training without sharing raw sensor data. However, most existing federated approaches inadequately address trust management, communication efficiency, and energy constraints, which are critical in real-world sensor-based systems. This paper proposes a trust-aware and energy-efficient federated learning framework specifically designed for secure sensor networks operating in resource-constrained edge environments. The proposed approach integrates lightweight trust metrics, trust-driven model aggregation, and adaptive communication scheduling to mitigate the impact of unreliable or malicious nodes while reducing unnecessary energy expenditure. By dynamically weighting client contributions based on trust and participation efficiency, the framework enhances robustness and learning stability under heterogeneous sensing conditions. Experimental results show that the proposed method maintains significantly higher accuracy under adversarial participation while reducing communication overhead and cumulative energy consumption. In particular, the framework improves model accuracy by up to 3.2% under heterogeneous conditions, reduces communication overhead by 28%, and decreases cumulative energy consumption by 31% compared with conventional federated learning approaches. Full article

(This article belongs to the Special Issue Sensor Security and Beyond)

► Show Figures

Figure 1

26 pages, 8452 KB

Open AccessArticle

Design of an Ultra-Sensitive Multi-Resonant Moore Fractal SRR Microwave Sensor for Non-Invasive Blood Glucose Monitoring

by Zaid A. Abdul Hassain, Malik J. Farhan and Taha A. Elwi

Sensors 2026, 26(8), 2306; https://doi.org/10.3390/s26082306 - 9 Apr 2026

This study details the design and development of an ultra-sensitive microwave sensor for non-invasive blood glucose monitoring, achieved by analyzing variations in the response of a split-ring resonator (SRR) through advanced engineering methodologies. There were three design phases in the development process. In [...] Read more.

This study details the design and development of an ultra-sensitive microwave sensor for non-invasive blood glucose monitoring, achieved by analyzing variations in the response of a split-ring resonator (SRR) through advanced engineering methodologies. There were three design phases in the development process. In the first phase, a standard SRR design was used. It had a resonant frequency of 2.975 GHz in S₂₁ and a sensitivity of only 0.0032 dB/(mg/dL). In the second phase, an interdigital capacitor (IDC) was added to the SRR structure. This made it work better and made it more sensitive, with a sensitivity of 0.015 dB/(mg/dL) at 4.1 GHz. The third phase was to use a fourth-order Moore fractal geometry to improve the resonance properties of the design a lot. From the obtained S₁₁, the maximum sensitivity was 0.042 dB/(mg/dL), which was a huge improvement in sensing efficiency compared to earlier designs. Several resonant frequencies were recorded between 4.84 and 7.56 GHz. The addition of the fractal structure made the electromagnetic field stronger in the resonant space and made the waves interact more with small changes in the biological medium, all without changing the sensor’s size (80 mm × 40 mm). These results show that fractal architecture is a promising way to create non-invasive, accurate, and easily integrated sensors in biological systems that can continuously measure blood glucose levels. Full article

(This article belongs to the Special Issue Microwaves for Biomedical Applications and Sensing)

► Show Figures

Figure 1

24 pages, 5776 KB

Open AccessArticle

RISE-VIO: Robust Initialization and Targeted Pose Robustification for INS-Centric Visual–Inertial Odometry Under Degraded Visual Conditions

by Xiaowei Xu, Ran Ju, Wenhua Jiao and Lijuan Li

Sensors 2026, 26(8), 2305; https://doi.org/10.3390/s26082305 - 8 Apr 2026

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system [...] Read more.

Feature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences. We present RISE-VIO, a real-time inertial-navigation-system-centric (INS-centric) visual–inertial odometry system that improves robustness by introducing GNC-style robustification into two failure-critical stages: initialization and per-frame pose estimation. For robust initialization, we develop a GNC-based decoupled rotation–translation initialization module with a two-stage observability gate, consisting of (i) rotation-compensated parallax-rate screening and (ii) a spectral-stability test on the linear global translation (LiGT) system. For online robustness, we design an IMU-prior-guided GNC-EPnP module to selectively downweight or reject outlier correspondences during pose estimation. Experiments on public benchmark datasets show that RISE-VIO achieves more reliable initialization and more stable trajectory estimation in challenging visual conditions while maintaining real-time performance. Additional Monte Carlo perspective-n-point (PnP) evaluations further support the robustness of the proposed pose estimation module under severe outlier contamination. Full article

(This article belongs to the Special Issue Multi-Sensor Resilient Fusion Perception, Positioning and Navigation Systems)

19 pages, 3130 KB

Open AccessArticle

SGMLN: Sentiment-Guided Mutual Learning Network for Multimodal Sarcasm Detection

by Yiran Wang, Xin Zhao and Yongtang Bao

Sensors 2026, 26(8), 2304; https://doi.org/10.3390/s26082304 - 8 Apr 2026

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of [...] Read more.

Social networks such as Twitter have grown rapidly and are now flooded with sarcastic comments, both in text and in images. Detecting sarcasm in multimodal data has significant social value and is attracting increasing research attention. However, most studies overlook the role of sentiment, even though sentiment information in text is closely linked to clues of sarcasm. Additionally, few consider how text and images align semantically. To address these issues, we propose a sentiment-guided mutual learning network (SGMLN) for multimodal sarcasm detection. SGMLN utilizes sentiment information to inform the combination of text and image features, and employs mutual learning to facilitate knowledge sharing among classifiers. We design a sentiment-guided attention layer that injects sentiment into both modalities, producing features that capture sarcasm more effectively. Sentic-BERT extracts sentiment-aware vectors from text, using word-level sentiment as a mask. In mutual learning, a logistic distribution function measures differences between classifiers, improving knowledge transfer between modalities. This step boosts multimodal understanding and model performance. By introducing sentiment-aware representations and semantic alignment, SGMLN bridges the gap between text and images, making them more consistent. Experiments on public datasets demonstrate that our model is effective and outperforms alternatives. Full article

(This article belongs to the Section Sensing and Imaging)

► Show Figures

Figure 1

22 pages, 5849 KB

Open AccessArticle

Multi-Scale Fourier Temporal Network for Multi-Source Precipitation Nowcasting

by Jing Huang, Shanmin Yang, Xiaojie Li and Xi Wu

Sensors 2026, 26(8), 2303; https://doi.org/10.3390/s26082303 - 8 Apr 2026

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively [...] Read more.

Accurate precipitation nowcasting plays an important role in disaster prevention and hydrometeorological applications, yet it remains highly challenging due to the complex spatiotemporal variability and multi-scale structural characteristics of precipitation systems. Existing deep learning methods are largely data-driven and often struggle to effectively exploit multi-source observations or learn physically meaningful representations. To address these limitations, this study proposes a Multi-Scale Frequency–Temporal Network (MS-FTNet) for precipitation nowcasting. The framework leverages Fourier transform-based frequency-domain modeling to achieve an interpretable multi-scale decomposition of precipitation dynamics. Specifically, low-frequency components capture large-scale stratiform patterns and their temporal evolution, while high-frequency components represent localized convective structures and abrupt variations. Building on this, a Global Feature Collaboration (GFC) module integrates global frequency-domain representations with multi-scale convolutional features, and an Adaptive Temporal Fusion (ATF) module enhances temporal dependency modeling. Experiments on the SEVIR dataset demonstrate that MS-FTNet consistently outperforms representative baseline models in terms of MSE, CSI, and LPIPS, particularly for heavy precipitation events and longer forecast lead times. Full article

(This article belongs to the Topic Recent Progress and Applications in Quantitative Remote Sensing)

► Show Figures

Figure 1

24 pages, 628 KB

Open AccessArticle

Vehicle-Conditional Split-Conformal Calibration for Risk-Budgeted Sub-Second Proxy-Triggered Vehicle Instability Warnings from Past-Only Sensor Slices

by Jinzhe Yang, Jianzheng Liu, Kai Tian, Yier Lin and Junxia Zhang

Sensors 2026, 26(8), 2302; https://doi.org/10.3390/s26082302 - 8 Apr 2026

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy [...] Read more.

Emergency maneuvers can drive vehicles into severe instability regimes within sub-second time scales, motivating last-moment warning interfaces with auditable false-alarm budgets. We study a proxy-triggered imminent-recognition setting: given a 0.1 s past-only slice of onboard signals, decide whether a conservative physics-defined instability proxy will trigger within the next

τ = 0.2

s. The contribution is, therefore, a calibrated warning for a safety-relevant surrogate event, not a claim of predicting crashes or true instability outcomes directly. Because the corpus is terminal-phase aligned, the default causal monitor (

w = d = 0.1

s,

k = 2

) is warnable on only

18.3 %

of event runs; we, therefore, report run-level effectiveness both overall and conditional on warnability. We learn a lightweight hazard scorer and convert its scores into an operator-facing alarm rule via split-conformal calibration on held-out negative slices, exposing a slice-level false-alarm budget

α

with finite-sample, one-sided control of the marginal slice-level false positive rate (FPR) on exchangeable negatives. To address fleet heterogeneity, we additionally calibrate vehicle-conditioned (Mondrian) thresholds, enabling per-vehicle risk budgeting without retraining separate models. On the held-out test split at

τ = 0.2

s, the scorer achieves AUPRC

\approx 0.251

against a base rate of

0.638 %

, AUROC

\approx 0.986

, and ECE

\approx 0.034

. After calibration at

α = 5 %

, realized slice-level FPR concentrates near the prescribed budget while slice-level TPR on imminent positives remains high (≈0.982). We explicitly separate this slice-level guarantee from empirical run-level metrics such as FAR

_{run}

, EWR on warnable runs, and lead time, and we report dependence and shift diagnostics to delineate where the guarantee may degrade. The reported

μ

-sensitivity analyses concern run-level descriptor perturbation and omission rather than validation of a within-run friction estimator with temporal lag. The result is a transparent, risk-budgeted monitoring primitive for last-moment vehicle-stability warning under clearly stated exchangeability assumptions. Full article

(This article belongs to the Section Vehicular Sensing)

37 pages, 1614 KB

Open AccessReview

Non-Invasive Electrochemical Biosensors for Fibromyalgia: A Path Toward Objective Physiological Monitoring and Personalized Management

by María Moreno-Guzmán, Juan Pablo Hervás-Pérez, Edurne Úbeda-D'Ocasar and Marta Sánchez-Paniagua

Sensors 2026, 26(8), 2301; https://doi.org/10.3390/s26082301 (registering DOI) - 8 Apr 2026

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, [...] Read more.

Fibromyalgia (FM) is a complex chronic syndrome marked by widespread musculoskeletal pain, neurocognitive dysfunction (“fibro-fog”), and autonomic disturbances. Clinical management remains challenging due to subjective symptom reporting and the lack of definitive diagnostics. Emerging evidence points to a multifactorial origin involving central sensitization, neuroendocrine imbalance, and systemic immune-inflammatory alterations. A wide array of candidate biomarkers has been reported in FM, encompassing neurotransmitters (serotonin, norepinephrine), excitatory and inhibitory amino acids, metabolic and glycolytic enzymes, stress-related proteins, autoantibodies, oxidative stress markers and pro-inflammatory cytokines. This molecular heterogeneity reflects the systemic and multidimensional nature of FM. However, most of these biomarkers have been primarily investigated in serum or plasma, where analytical validation and reference ranges are more established. In contrast, the exploration of salivary biomarkers—although highly attractive due to its non-invasive, stress-free, and repeatable collection—remains comparatively limited. Saliva contains a reduced concentration range of many systemic markers and is strongly influenced by circadian rhythms, stress, flow rate, and oral health conditions. While promising candidates such as α-amylase, cortisol, calgranulins, and selected metabolic enzymes have shown potential in saliva, many proposed FM-related biomarkers lack full analytical validation, standardized protocols, and clinically defined reference intervals in this matrix. In this context, non-invasive electrochemical biosensors represent a transformative technological approach. Advanced electrode architectures incorporating nucleic acid probes, redox reporters, and nanostructured materials offer high sensitivity in low-volume and low-concentration biofluids such as saliva. The integration of multiplexed biomarker panels into portable platforms could enable real-time, longitudinal monitoring of FM pathophysiology, supporting phenotype stratification, personalized therapeutic adjustment, and objective disease activity tracking. Full article

► Show Figures

Figure 1

27 pages, 4791 KB

Open AccessArticle

Combining Fast Orthogonal Search with Deep Learning to Improve Low-Cost IMU Signal Accuracy

by Jialin Guan, Eslam Mounier, Umar Iqbal and Michael J. Korenberg

Sensors 2026, 26(8), 2300; https://doi.org/10.3390/s26082300 - 8 Apr 2026

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system [...] Read more.

Inertial measurement units (IMUs) in low-cost navigation systems suffer from significant drift and noise errors due to sensor biases, scale factor instability, and nonlinear stochastic noise. This paper proposes a hybrid error compensation approach that combines Fast Orthogonal Search (FOS), a nonlinear system identification technique, with deep Long Short-Term Memory (LSTM) neural networks to improve IMU signal accuracy in GNSS-denied navigation. The FOS algorithm efficiently models deterministic error patterns (such as bias drift and scale factor errors) using a small training dataset, while the LSTM learns the IMU’s complex time-dependent error dynamics from much longer training data. In the proposed method, FOS is first used to predict the output of a high-end IMU based on that of a low-end IMU, and the trained FOS model is then used to extend the training data for an LSTM-based predictor. We demonstrate the efficacy of this FOS–LSTM hybrid on real vehicular IMU data by training with a limited segment of high-precision reference measurements and testing on extended operation periods. The hybrid model achieves high predictive accuracy for predicting the high-end signal based on the low-end signal, with a mean squared error below 0.1% and yields more stable velocity estimates than models using FOS or LSTM alone. Although long-term position drift is not fully eliminated, the proposed method significantly reduces short-term uncertainty in the inertial solution. These results highlight a promising synergy between model-based system identification and data-driven learning for sensor error calibration in navigation systems. Key contributions include FOS-based pseudo-label bootstrapping for data-efficient LSTM training and a navigation-level evaluation illustrating how signal correction impacts dead reckoning drift. Full article

(This article belongs to the Special Issue Innovative Sensor Systems and Applications: Enabling Navigation, Perception, and Decision-Making Across Domains)

► Show Figures

Figure 1

11 pages, 481 KB

Open AccessProtocol

AI-Guided Remission: Protocol for a Conversational Agent (Chatbot) for Dosing Activity and Footwear Progression After Diabetic Limb Reconstruction

by Lucian M. Feraru, David C. Klonoff, Bijan Najafi, Magdalena Antoszewska and David G. Armstrong

Sensors 2026, 26(8), 2299; https://doi.org/10.3390/s26082299 - 8 Apr 2026

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home [...] Read more.

Background: Diabetic foot ulcers recur frequently after healing. The first three months carry the highest risk. Remission is a vulnerable phase that demands precise self-care and timely feedback. Evidence supports thermometry and protective footwear with gradual return to activity, yet adherence at home is inconsistent. Objective: To describe the design and planned evaluation of a conversational agent (chatbot) that guides patients through the remission phase following diabetic limb reconstruction. Methods: This protocol describes a conversational agent (chatbot) that turns remission guidance into daily actions, grounded in clinical expertise and established care guidelines. Walking is dosed like a drug, with careful titration based on tissue response. The agent integrates automatic data capture (smartphone step counts, skin temperature, shoe step streams, smartwatch step streams, Bluetooth thermometry when available, and app session timestamps) with manual patient entries (shoe wear time, skin redness persistence, and symptom checks). It doses walking activity, guides footwear break-in, prompts photo-confirmed concerns, following clinician-informed rules and escalation pathways. We define data quality checks for missingness and physiologic plausibility, and the agent reinforces reducing weight-bearing activity when risk signals appear. We outline device drift. The study is designed as a single-arm feasibility pilot (n = 30) to assess engagement, safety, and implementation fidelity. Results: No clinical outcome results are reported because this is a protocol study and enrollment has not yet begun. This study presents the prespecified sensing-to-decision workflow, escalation logic, and pilot endpoints, along with internal technical verification procedures (e.g., message delivery reliability, data completeness checks, and rule-engine consistency testing). Conclusions: A remission chatbot is a plausible method to extend specialist support into the home, reflecting integration of clinical expertise with digital health tools. This protocol defines how feasibility, safety, and usability will be evaluated. Clinical efficacy should be confirmed in future studies. Full article

(This article belongs to the Special Issue AI-Enabled Sensing Technology for Smart Healthcare and Precision Diagnosis)

► Show Figures

Graphical abstract

14 pages, 1436 KB

Open AccessArticle

Non-Linear Center-of-Pressure Features Associated with Fall History in Older Adults: An Exploratory Analysis

by Dai Wakabayashi and Yohei Okada

Sensors 2026, 26(8), 2298; https://doi.org/10.3390/s26082298 - 8 Apr 2026

Postural sway derived from center-of-pressure (CoP) trajectories is widely used to assess balance and fall risk in older adults, but conventional linear metrics mainly quantify sway magnitude and may overlook temporal organization. Guided by the loss-of-complexity hypothesis, we re-examined associations between fall history [...] Read more.

Postural sway derived from center-of-pressure (CoP) trajectories is widely used to assess balance and fall risk in older adults, but conventional linear metrics mainly quantify sway magnitude and may overlook temporal organization. Guided by the loss-of-complexity hypothesis, we re-examined associations between fall history and linear and non-linear CoP metrics in an open-access dataset. Quiet-standing trials under eyes-open and eyes-closed conditions were analyzed in adults ≥60 years (fallers n = 19; non-fallers n = 57). To reduce confounding, propensity score matching was performed using age, sex, body mass index, activities of daily living level, illness status, number of medications, disability status, and orthosis/prosthesis use. Linear and non-linear indices, including recurrence quantification analysis, detrended fluctuation analysis, fractal dimension, multiscale entropy, stabilogram diffusion analysis, and sway density measures, were examined. After matching, no CoP metric differed significantly between groups. However, SHAP-based exploratory analysis suggested that non-linear features related to temporal structure and multiscale organization contributed more prominently to model output than conventional magnitude-based metrics. Given the limited sample size, these findings should be interpreted as exploratory and hypothesis-generating. Full article

(This article belongs to the Special Issue Sensor-Based Methods for Kinematics, Kinetics, and Physiology in Motion: Applications and Metrological Characterization)

► Show Figures

Figure 1

More Articles...

Submit to Sensors Review for Sensors

Journal Menu

Journal Browser

► Journal Browser

Highly Accessed Articles

View More...

Latest Books

More Books and Reprints...

E-Mail Alert

News

4 March 2026
MDPI’s 2025 Best Paper Awards—Award-Winning Papers Announced

9 October 2025
Meet Us at the 3^rd International Conference on AI Sensors and Transducers, 2–7 August 2026, Jeju, South Korea

9 April 2026
International Day of Human Space Flight—12 April 2026

More News & Announcements...

Topics

Propose a Topic

Topic in Land, Remote Sensing, Sensors, Applied Sciences

Recent Progress and Applications in Quantitative Remote Sensing Topic Editors: Huawei Wan, Yu Wang, Hongmin Zhou, Longhui Lu
Deadline: 30 April 2026

Topic in Applied Sciences, Electronics, Micromachines, Sensors, Telecom

Microwave Components and Devices: Emerging Technologies and Applications Topic Editors: Sensong An, Bowen Zheng
Deadline: 31 May 2026

Topic in Applied Sciences, Optics, Sensors, Materials, Fibers, Photonics, Micromachines

Distributed Optical Fiber Sensors Topic Editors: Jian Li, Hao Wu, Giancarlo C. Righini, Zhe Ma, Yahui Wang
Deadline: 30 June 2026

Topic in Drones, Electronics, Eng, JMSE, Robotics, Sensors, Vehicles

Advanced Technologies and Applications for Unmanned Systems Topic Editors: Jinchao Chen, Chao Chen, Yingjie Zhang
Deadline: 31 July 2026

More Topics

Conferences

Propose a Conference Collaboration

9–10 July 2026 Meet Us Virtually at the 1st International Online Conference on Sensor and Actuator Networks (CSAN 2026), 9–10 July 2026

2–7 August 2026 Abstract Submission Deadline is Approaching

2–7 August 2026 Abstract Submission Deadline Extended!

More Conferences...

Special Issues

Propose a Special Issue

Special Issue in Sensors

Wearable Systems for Monitoring Joint Kinematics Guest Editors: Arianna Carnevale, Daniela Lo Presti
Deadline: 10 April 2026

Special Issue in Sensors

Applications of Sensor Technologies in Motion Perception and Position Tracking Guest Editor: Maki Sugimoto
Deadline: 10 April 2026

Special Issue in Sensors

Advanced Optics and Sensing Technologies for Telescopes Guest Editor: Donglin Ma
Deadline: 10 April 2026

Special Issue in Sensors

Sensor Enabled Smart Energy Solutions Guest Editors: Hamid Reza Shaker, Rahman Dashti
Deadline: 10 April 2026

More Special Issues

Topical Collections

Topical Collection in Sensors

Sensors for Globalized Healthy Living and Wellbeing Collection Editor: Panicos Kyriacou

Topical Collection in Sensors

IoT and Smart Homes Collection Editors: Sara Comai, Fabio Salice

Topical Collection in Sensors

Sensors and Robotics for Digital Agriculture Collection Editors: Dionysis Bochtis, Aristotelis C. Tagarakis

Topical Collection in Sensors

EEG-Based Brain–Computer Interface for a Real-Life Appliance Collection Editors: Hyungmin Kim, Song Joo Lee

More Topical Collections

Back to TopTop