Sensors

24 pages, 39218 KB

Open AccessArticle

Multi-Source Motion Inputs and FA-TA-BiLSTM for Lower-Limb Joint Angle Prediction

by Chao Yang, Peng Zhao, Yuanxiang Guo, Xin Han, Xueshan Gao and Junlin Deng

Sensors 2026, 26(16), 5235; https://doi.org/10.3390/s26165235 - 18 Aug 2026

Accurate lower-limb joint angle prediction can support motion-state perception and rehabilitation-oriented analysis. This offline feasibility study evaluated an FA-TA-BiLSTM model using surface electromyography (sEMG) features, together with historical hip and knee joint angles and angular velocities. Data were collected from five healthy adult [...] Read more.

Accurate lower-limb joint angle prediction can support motion-state perception and rehabilitation-oriented analysis. This offline feasibility study evaluated an FA-TA-BiLSTM model using surface electromyography (sEMG) features, together with historical hip and knee joint angles and angular velocities. Data were collected from five healthy adult male participants during level walking and sit-to-stand transitions, and the prediction horizon was 100 ms. Support vector regression (SVR), BiLSTM, and FA-TA-BiLSTM were compared under the same combined-input condition and evaluation protocol. The FA-TA-BiLSTM model achieved RMSE, MAE, and

R^{2}

values of 2.0684

^{\circ}

, 1.5920

^{\circ}

, and 0.9726 for hip prediction and 2.9604

^{\circ}

, 2.5142

^{\circ}

, and 0.9660 for knee prediction, respectively. These values were obtained using a mixed-participant chronological split and should be interpreted as preliminary within-cohort estimates rather than evidence of participant-independent generalization. Under this restricted protocol, FA-TA-BiLSTM produced lower errors than SVR and standard BiLSTM. The participant-level model ranking was consistent across the five participants, but exact pairwise comparisons did not reach significance after Holm adjustment. The current comparison does not isolate the incremental contribution of sEMG or individual attention modules; larger and more diverse cohorts, participant-independent validation, modality and module ablation, and causal online evaluation remain necessary. Full article

(This article belongs to the Section Intelligent Sensors)

17 pages, 2640 KB

Open AccessArticle

Personalized Detection of Functional-State Changes Through Continuous Gait Monitoring: A Methodology for Assistive-Device Users

by Janire Otamendi, Asier Zubizarreta, Imanol Torre and Cristina Sesma

Sensors 2026, 26(16), 5234; https://doi.org/10.3390/s26165234 - 18 Aug 2026

Abstract

Lower limb mobility impairments resulting from neurological diseases, trauma injuries or aging significantly impact the quality of life of individuals by limiting their autonomy. Rehabilitation plays an important role in addressing the challenges of these impairments, with early detection of changes in the [...] Read more.

Lower limb mobility impairments resulting from neurological diseases, trauma injuries or aging significantly impact the quality of life of individuals by limiting their autonomy. Rehabilitation plays an important role in addressing the challenges of these impairments, with early detection of changes in the functional state of individuals being essential. This enables therapies to be adjusted based on the current condition of the patient, thereby enhancing their effectiveness. Such early detection, however, requires continuous assessment by specialists, which is unfeasible given the existing limited resources. Since gait is a reflection of the physical and mental states of each individual, its continuous monitoring and subsequent data analysis can serve as a valuable tool for the aforementioned objective. This study proposes a methodology that, based on continuous gait monitoring data, detects significant changes in the functional state of patients who require an assistive device for walking. Given the variability that may exist among different individuals, this methodology tackles the issue from an individualized approach generating personalized models for each individual using the OC-SVM technique. The proposed methodology was validated in nine healthy people who had different simulated functional states, obtaining an accuracy in the range of 70–97%. In addition, a one-year longitudinal study was also carried out with three post-stroke individuals to validate the methodology in real cases, obtaining an average accuracy of 78%. Full article

(This article belongs to the Section Biosensors)

24 pages, 3955 KB

Open AccessArticle

A Prompt-Guided and Quality-Aware Robust Text–Audio Intent Recognition Framework for Elderly Care

by Zhimin Wei, Shuhao Tian, Yanzhen Wang, Yao Wang, Xiaolong Zhou and Jianyong Li

Sensors 2026, 26(16), 5233; https://doi.org/10.3390/s26165233 - 18 Aug 2026

Abstract

In natural language understanding, intent recognition plays a central role in human–computer interaction. However, in elderly-care scenarios, acoustic signals are often affected by atypical speech patterns, slower speaking rates, and environmental noise, making audio information less reliable and reducing the effectiveness of conventional [...] Read more.

In natural language understanding, intent recognition plays a central role in human–computer interaction. However, in elderly-care scenarios, acoustic signals are often affected by atypical speech patterns, slower speaking rates, and environmental noise, making audio information less reliable and reducing the effectiveness of conventional text–audio fusion methods. To address this problem, we propose a prompt-guided and quality-aware text–audio intent recognition framework. Specifically, a χ²-based intent prototype soft prompt is introduced to enhance the semantic representation of text. Then, a residual-free text-guided cross-attention module is designed to refine degraded acoustic features using textual semantics as reliable guidance. In addition, a dynamic fusion gate is developed to adjust the contributions of text and audio based on modality reliability and intent-related information. Experiments on the MIntRec dataset with simulated acoustic degradation show that the proposed model achieves 60.90% accuracy, 60.80% weighted F1, and 57.90% macro-F1, outperforming several competitive baselines. These results indicate that the proposed framework can improve the robustness of intent recognition under challenging acoustic conditions in elderly-oriented interaction scenarios. Full article

(This article belongs to the Special Issue AI and Big Data for Smart Healthcare: Ensuring Privacy and Security)

28 pages, 3549 KB

Open AccessArticle

Battery Pack for IoT Devices in a Harsh Outdoor Environment

by Peter Ševčík, Michal Hodoň, Lukáš Formanek and Peter Šarafín

Sensors 2026, 26(16), 5232; https://doi.org/10.3390/s26165232 - 18 Aug 2026

Abstract

Outdoor Internet of Things (IoT) sensor nodes require battery systems for which their behaviour and implementation limits are characterized under low-temperature and variable-load conditions. This study documents a LiFePO₄ battery-pack prototype integrating a BQ29729DSET protection IC and a configured MAX17055ETB+T fuel gauge [...] Read more.

Outdoor Internet of Things (IoT) sensor nodes require battery systems for which their behaviour and implementation limits are characterized under low-temperature and variable-load conditions. This study documents a LiFePO₄ battery-pack prototype integrating a BQ29729DSET protection IC and a configured MAX17055ETB+T fuel gauge and descriptively compares its discharge runtime with that of a reference pack with a similar nominal capacity, comprising three parallel Samsung ICR18650-26H cells. Tests were conducted at −30 °C, 8 °C, and 25 °C under nominal load settings of 50, 100, and 200 mA. At 8 °C and 25 °C, the two configurations showed similar runtimes and nominal-current-based calculated capacities. At −30 °C, the LiFePO₄ assembly ran for 89.1 versus 66.0 h at 50 mA and 44.7 versus 37.2 h at 100 mA. An analysis based on the typical MCP1700 dropout characteristic bounds the portions of these LiFePO₄ runtimes recorded below the assumed regulation threshold at approximately 1.1 h and 0.9 h, respectively; even subtracting those complete intervals leaves positive differences of 33.3% and 17.7% relative to the reference runtimes. Complete current logs were unavailable; therefore, capacity and energy are reported only as nominal-current estimates. In the ICR18650-26H reference pack at −30 °C, the calculated capacity increased anomalously from 3302 to 4087 mAh as the nominal setting increased from 50 to 200 mA. The ICR cutoff remained above the estimated regulator-dropout thresholds, so dropout does not explain the anomaly; temperature, conditioning, and run-order effects cannot be excluded. Protection trip points and fuel-gauge accuracy were not experimentally verified. Our contribution is therefore reproducible design documentation combined with preliminary low-temperature runtime evidence rather than validation of a fully monitored and protected battery pack. Full article

(This article belongs to the Section Internet of Things)

18 pages, 2956 KB

Open AccessArticle

Experimental Characterization of Optical Camera Communication with Commercial Cameras Leveraging FPS and Rolling Shutter

by Juan Carlos Torres Zafra, Juan Sebastian Betancourt Perlaza, Carlos Ivan del Valle Morales, Ricardo Vergaz Benito and Jose Manuel Sanchez Pena

Sensors 2026, 26(16), 5231; https://doi.org/10.3390/s26165231 - 18 Aug 2026

Abstract

Optical Camera Communication (OCC) enables data reception using common CMOS cameras and commercial webcams. However, applying multi-level modulation with rolling-shutter sensors is constrained by temporal acquisition parameters that may be undocumented or not directly accessible, making it challenging since many existing solutions rely [...] Read more.

Optical Camera Communication (OCC) enables data reception using common CMOS cameras and commercial webcams. However, applying multi-level modulation with rolling-shutter sensors is constrained by temporal acquisition parameters that may be undocumented or not directly accessible, making it challenging since many existing solutions rely on specialized hardware or require high processing complexity. This paper demonstrates that reliable multi-level OCC can be achieved using only unmodified commercial hardware and straightforward signal processing by experimentally characterizing and validating a 4-level pulse width modulation (4-PWM) link. Data are encoded in the duty cycle of the transmitted signal and recoveblack from the width of the captublack rolling-shutter stripes. Two internal timing parameters are estimated directly from the captublack images without access to the internal camera timing: the row readout period (34.38

μ

s), obtained from the spatial periodicity of the stripes, and the effective integration time (490

μ

s), inferblack from the deformation of the received constellation with carrier frequency. A single-parameter model is derived to describe this deformation and is validated at two carrier frequencies differing by a factor of four, pblackicting constellation compression, a fixed point at a duty cycle of 0.5, and constellation collapse (followed by inversion) when the exposure-to-carrier-period ratio reaches 0.5. We evaluate system performance under different exposure settings, showing that automatic camera control strongly degrades multi-level detection (BER of 0.290, with mean image level variation constrained to

0.14 %

compablack to

61 %

under fixed exposure). Under optimal fixed-exposure operating conditions, a prospective 15 min transmission achieved zero bit errors over 35,878 bits at 40 bps, corresponding to a

95 %

upper confidence bound on the BER of

8.4 \times 10^{- 5}

. These results reveal a practical balance between cost, complexity, and performance, demonstrating that 4-PWM rolling-shutter OCC is a viable solution for Internet of Things (IoT) signaling and low-rate data transmission using commercially available devices. Full article

(This article belongs to the Section Optical Sensors)

18 pages, 937 KB

Open AccessArticle

Long-Term Validation of a Consumer- vs. Industrial-Grade Indoor Radon and Air Quality Monitor at a School in Utah

by John D. Beard, Maren M. Ball, Christopher Kitras and Philip B. Lundrigan

Sensors 2026, 26(16), 5230; https://doi.org/10.3390/s26165230 - 18 Aug 2026

Abstract

In recent years, the demand for indoor radon and air quality monitors has increased while more affordable consumer-grade monitors have become available. The aim of our study was to assess the validity of the less expensive, non-calibrated, consumer-grade Lüft (SunRADON LLC, [...] Read more.

In recent years, the demand for indoor radon and air quality monitors has increased while more affordable consumer-grade monitors have become available. The aim of our study was to assess the validity of the less expensive, non-calibrated, consumer-grade Lüft (SunRADON LLC, Melbourne, FL, USA) compared to the more expensive, annually and manufacturer calibrated, industrial-grade Model 1028 XP (SunRADON LLC, Melbourne, FL, USA). We collected data about 24 h moving averages of barometric pressure, carbon dioxide (CO₂), radon, relative humidity, temperature, and volatile organic compounds (VOC) for approximately 29 weeks in 2024 in each of six rooms in a school in Utah. The mean difference in the co-located Lüfts’ and Model 1028 XPs’ 24 h moving average radon was −0.05 pCi/L of air (95% confidence interval: −0.24, 0.14, p = 0.50) and the concordance correlation coefficient (CCC) was 0.92. The Lüfts’ and Model 1028 XPs’ 24 h moving averages were similar for barometric pressure (CCC: 0.87), but the Lüfts’ 24 h moving averages were lower than the Model 1028 XPs’ for CO₂, relative humidity, and VOC and higher for temperature. The Lüft provided valid measurements of 24 h moving averages for barometric pressure and radon, but not for CO₂, relative humidity, temperature, and VOC. Full article

(This article belongs to the Section Environmental Sensing)

22 pages, 21195 KB

Open AccessArticle

Training-FreeDetector-to-Promptable-Segmenter Integration for Stacked Cartons: Prompt Accuracy–Efficiency Trade-Offs

by Liang Yu, Qi Gao, Shuaiqi Yang, Hao Qiu and Xiaoyan Meng

Sensors 2026, 26(16), 5229; https://doi.org/10.3390/s26165229 - 18 Aug 2026

Abstract

Detector-assisted promptable segmentation can reuse box annotations, but prompt design creates an accuracy–efficiency trade-off in dense scenes. We study a training-free interface in which a detector trained with bounding-box annotations localizes stacked cartons and a frozen segmenter generates masks. Four interfaces are evaluated: [...] Read more.

Detector-assisted promptable segmentation can reuse box annotations, but prompt design creates an accuracy–efficiency trade-off in dense scenes. We study a training-free interface in which a detector trained with bounding-box annotations localizes stacked cartons and a frozen segmenter generates masks. Four interfaces are evaluated: Point-Single, Box, ambiguity-aware Point-Max, and Grid-Iter with sparse positive points and one mask-logit feedback pass. On a leakage-audited split, COCO mask AP, fixed-threshold Hungarian-matched metrics, paired bootstrap uncertainty, and synchronized latency were measured. On 773 test images, Box and Grid-Iter achieved mask AP values of 0.8984 and 0.8988. Their paired AP difference was 0.0007 (95% CI,

- 0.0035

to 0.0047), while matched-mIoU and F1 intervals also included zero. Box required 604.67 ms/image versus 677.33 ms/image for Grid-Iter. A YOLO26s–SAM 3 Box reference achieved a mask AP value of 0.9115 at 320.74 ms/image, whereas mask-supervised YOLOv9c-seg achieved a value of 0.9235 AP on the same cleaned split. The results indicate a metric-dependent Pareto trade-off rather than overall Grid-Iter superiority and position detector-to-segmenter prompting as a reproducible annotation–accuracy option for densely stacked cartons. Full article

(This article belongs to the Special Issue AI-Based Computer Vision Sensors & Systems—2nd Edition)

38 pages, 16290 KB

Open AccessArticle

ELI: A Conversational LLM-Based Interface for Human–AI Driving Teams and Its Impact on Performance and Driver Status

by Evelyn Vasquez, Alanis Negroni, Juan Peña, Iyadunni Adenuga and Juan Medina-Lee

Sensors 2026, 26(16), 5228; https://doi.org/10.3390/s26165228 - 18 Aug 2026

Abstract

Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a [...] Read more.

Highly automated vehicles often rely on takeover requests (TORs) that lack contextual transparency, treat drivers as passive fallbacks, and lead to poor situational awareness. To address this challenge, this study presents the Empowering Language Interaction (ELI) framework, a conversational interface powered by a large language model that supports bidirectional negotiation and collaborative human–AI teamwork. Using the CARLA driving simulator, 28 participants compared ELI with a conventional TOR baseline in both urban and peri-urban driving scenarios. The study employed a multidimensional evaluation approach, integrating telemetry data on driving performance with continuous monitoring of physiological indicators (electrocardiogram and electrodermal activity) and subjective questionnaires to assess driver trust and engagement. Results indicated that ELI sustained continuous driver engagement and improved the subjective comprehension of the vehicle’s state. Physiologically, the conversational interface maintained active cognitive load, preventing the abrupt autonomic spikes characteristic of traditional takeover requests. Furthermore, ELI outperformed the TOR baseline in safety metrics by reducing out-of-lane events and maintaining greater safety margins. Conversational interaction has shown potential to transform drivers from passive supervisors into active teammates, improving joint decision-making without inducing over-reliance on the automated system. Full article

(This article belongs to the Special Issue Sensors and Sensing Technologies for Traffic, Driving and Transportation)

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21 pages, 5790 KB

Open AccessArticle

A Decoupled Fractional-Order Kalman Filter for Accelerometer Tilt Angle Estimation

by Naiming Wu, Xu Liu, Houzeng Han and Jian Wang

Sensors 2026, 26(16), 5227; https://doi.org/10.3390/s26165227 - 18 Aug 2026

Abstract

Accelerometer-based tilt angle estimation is widely used in engineering monitoring, yet random noise and outliers degrade its accuracy. Integer-order Kalman filters suppress noise, but their Markovian model assumes that the current state alone is sufficient to predict the next, neglecting the influence of [...] Read more.

Accelerometer-based tilt angle estimation is widely used in engineering monitoring, yet random noise and outliers degrade its accuracy. Integer-order Kalman filters suppress noise, but their Markovian model assumes that the current state alone is sufficient to predict the next, neglecting the influence of earlier states on slowly varying processes. Fractional-order Kalman filters incorporate historical states into the prediction. However, the conventional formulation shares a single transition matrix between the state and covariance predictions, underestimating the prediction uncertainty, while the memory mechanism propagates gross errors across iterations. To overcome these limitations, this paper proposes a decoupled fractional-order Kalman filter (DFKF). The method assigns independent transition matrices to the state and covariance predictions, where a scaling coefficient inflates the predicted covariance to lower the prediction weight and strengthen reliance on measurements. A front-end gross-error pre-elimination strategy combining second-order differencing with adaptive peak detection is further introduced to block outlier propagation at the source before it enters the memory mechanism. Simulations under varying noise levels and gross-error conditions show that DFKF achieves a mean RMSE (Root Mean Square Error) of 0.147°, representing reductions of 17.4% and 6.4% over KF (0.178°) and FKF (0.157°), respectively, and a mean MaxAE (Maximum Absolute Error) of 0.548°, outperforming KF and FKF by 24.5% and 10.3%. Under gross-error conditions, DFKF converges in 0.012 s on average, approximately 2.8 times faster than KF and FKF, and the pre-elimination strategy restores accuracy to near-error-free levels. Full article

(This article belongs to the Special Issue Sensor Fusion: Kalman Filtering for Engineering Applications)

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27 pages, 44958 KB

Open AccessArticle

Monitoring the Shear Behavior of Reinforced Concrete Beams Using Fiber Optic Sensors Installed in the Compression Zone

by Johannes Rathgen and Vincent Oettel

Sensors 2026, 26(16), 5226; https://doi.org/10.3390/s26165226 - 18 Aug 2026

Abstract

A variety of measurement systems are available for monitoring existing concrete bridges with deficiencies in shear capacity. In addition to established systems, fiber optic sensors (FOS) offer significant potential for structural health monitoring. However, FOSs are often installed in the tension zone, where [...] Read more.

A variety of measurement systems are available for monitoring existing concrete bridges with deficiencies in shear capacity. In addition to established systems, fiber optic sensors (FOS) offer significant potential for structural health monitoring. However, FOSs are often installed in the tension zone, where crack formation may occur even under service loads, increasing the risk of sensor failure and potentially resulting in a loss of measurement capability. A promising approach to significantly reduce this risk is the installation of FOSs in the compression zone. However, it remains unclear whether measurements obtained from FOSs in the compression zone can be used to assess the load-bearing and deformation behavior of reinforced concrete beams and how they relate to measurements obtained in the tension zone. To address this question, shear tests were carried out on reinforced concrete beams with shear reinforcement ratios commonly used in practice. The experimental results demonstrate close agreement between measurements obtained from FOSs installed in the compression and tension zones under service loads. Furthermore, the findings and the characteristic strain profiles associated with shear and flexural failure provide a basis for assessing existing structures monitored using FOSs. Full article

(This article belongs to the Section Optical Sensors)

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12 pages, 405 KB

Open AccessArticle

Enhancing Shadow Fading Mitigation with RIS Using Two-Dimensional Orthogonal Pattern Division Multiple Access

by Zhiying Yang, Jianguo Yao, Ziwei Liu, Yanling Shi and Xiaodong Bai

Sensors 2026, 26(16), 5225; https://doi.org/10.3390/s26165225 - 18 Aug 2026

Abstract

This paper addresses multipath interference introduced by reconfigurable intelligent surface (RIS) in mitigating shadow fading in wireless communication systems. To tackle this, we propose the use of two-dimensional orthogonal pattern division multiple access (OPDMA) with optimal frequency-hopping patterns. OPDMA effectively reduces interference from [...] Read more.

This paper addresses multipath interference introduced by reconfigurable intelligent surface (RIS) in mitigating shadow fading in wireless communication systems. To tackle this, we propose the use of two-dimensional orthogonal pattern division multiple access (OPDMA) with optimal frequency-hopping patterns. OPDMA effectively reduces interference from multipath propagation, Doppler effect, and inter-user interactions, eliminating the need for complex equalization and channel estimation. This simplifies receiver design and enhances system performance. The paper introduces optimal frequency-hopping patterns with ideal autocorrelation and cross-correlation properties, constructed using a two-dimensional cyclic shift based on algebraic Costas arrays. Simulation results demonstrate that the RIS-assisted OPDMA system significantly reduces the bit error rate (BER), improves system throughput, and outperforms existing one-dimensional methods, improving communication quality and system efficiency. Full article

(This article belongs to the Special Issue Innovations in Vehicular Communication and Sensing Technologies)

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30 pages, 15497 KB

Open AccessArticle

CISRMamba: Cross-Modal Interaction and Scan-Routing Mamba for Multi-Sensor Flood Inundation Mapping

by Haoran Feng, Chenyang Xiao, Ruiyang Lin, Yuxuan Chen, Linxing Liang and Bin Lin

Sensors 2026, 26(16), 5224; https://doi.org/10.3390/s26165224 - 18 Aug 2026

Abstract

Accurate flood mapping is essential for disaster response, yet optical–SAR fusion remains difficult when clouds, speckle noise, and terrain interference cause the two sensors to provide uneven or conflicting evidence. Multimodal models may suffer from “modality collapse”, while fixed scan patterns make water [...] Read more.

Accurate flood mapping is essential for disaster response, yet optical–SAR fusion remains difficult when clouds, speckle noise, and terrain interference cause the two sensors to provide uneven or conflicting evidence. Multimodal models may suffer from “modality collapse”, while fixed scan patterns make water boundaries difficult to delineate. To address these issues, we propose CISRMamba, a multimodal state space model (SSM) based on a lightweight MobileMamba backbone. A Deformable Scan Router (DSR) adapts the scan trajectory to irregular flood contours while preserving SSM efficiency. Instead of forcing all regions into a rigid “align-then-fuse” process, the Feature Complementary Module (FCM) suppresses discrepant responses, supplies complementary information where one modality is incomplete, or enhances consistent evidence according to the local optical–SAR relationship. The Refined Feature Integration (RFI) module reduces distributional shift and modality collapse through a difference-guided residual, while the Spectral Refinement Block (SRB) recovers high-frequency spatial details weakened by downsampling. Experiments on CAU-Flood and Wuhan show that CISRMamba achieves mIoU scores of 91.53% and 58.67%, respectively, outperforming baselines based on CNNs, Transformers, and Mamba. These results indicate that CISRMamba combines context modeling with boundary-level refinement for efficient flood monitoring under heterogeneous weather and scene conditions. Full article

(This article belongs to the Special Issue Remote Sensing Image Fusion and Object Tracking)

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24 pages, 1600 KB

Open AccessArticle

Fine-Grained and Flexible Dual Authentication for IoT-Connected Healthcare Sensor Networks

by Huiying Hou, Jianyu Miao, Yucong Ma, Xuerui Gan and Xuefeng Li

Sensors 2026, 26(16), 5223; https://doi.org/10.3390/s26165223 - 18 Aug 2026

Abstract

IoT-connected healthcare sensor networks require authenticated and privacy-preserving data exchange among wearable sensors, mobile medical terminals, edge gateways, cloud servers, and medical institutions. Existing authentication schemes for healthcare IoT often bind signatures directly to user identities, exposing sensitive personal or institutional information and [...] Read more.

IoT-connected healthcare sensor networks require authenticated and privacy-preserving data exchange among wearable sensors, mobile medical terminals, edge gateways, cloud servers, and medical institutions. Existing authentication schemes for healthcare IoT often bind signatures directly to user identities, exposing sensitive personal or institutional information and imposing heavy verification costs on resource-constrained sensing devices. To address this problem, we propose a fine-grained and flexible dual authentication scheme for healthcare sensor networks. In the proposed scheme, health data and diagnoses are signed with a fresh signing key and a fine-grained access control policy each time, so that the signer identity remains hidden while authorized entities can still modify permitted parts of signed data. No entity other than an authorized entity can trace a malicious signer or modify signed data without changing the data source. To support lightweight verification in sensor-edge-cloud deployments, we further present a verifiable outsourced authentication scheme that outsources time-consuming pairing operations to cloud servers; the online verification process then requires only six multiplication operations. As a fundamental technical component, we present a practical attribute-based sanitizable signature with shorter signature and key lengths and more efficient signing and signature-changing operations than the state-of-the-art policy-based sanitizable signature (P3S). Formal security analysis and experiments demonstrate the security and practicality of the proposed scheme for privacy-preserving healthcare sensing and medical data exchange. Full article

(This article belongs to the Section Internet of Things)

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21 pages, 15647 KB

Open AccessArticle

OATrack: A Quality-Gated Progressive Association Framework with a YOLO Detection Cache for UAV Small-Object Multi-Object Tracking

by Lei Qi, Liejun Wang and Shaochen Jiang

Sensors 2026, 26(16), 5222; https://doi.org/10.3390/s26165222 - 18 Aug 2026

Abstract

Unmanned aerial vehicle (UAV) multi-object tracking (MOT) is challenging because small targets produce unstable confidence and localization, while viewpoint changes and short occlusions make association ambiguous. We present OATrack, an integrated association framework tailored to UAV small-object tracking. OATrack combines detection–track pair quality [...] Read more.

Unmanned aerial vehicle (UAV) multi-object tracking (MOT) is challenging because small targets produce unstable confidence and localization, while viewpoint changes and short occlusions make association ambiguous. We present OATrack, an integrated association framework tailored to UAV small-object tracking. OATrack combines detection–track pair quality based on detection reliability and current-state geometric consistency, quality-gated track-state protection with a track-specific scale prototype, and three-stage progressive assignment for active-track matching and recently lost-track recovery. A standardized YOLO11m-smallobj detection cache supplies identical detections to all association methods and enables reproducible fixed-input evaluation. On VisDrone-MOT val, OATrack reaches HOTA 38.5847 versus 38.4797 for official Ultralytics ByteTrack while reducing identity switches (IDSW) by 69.2%, track fragmentation by 29.7%, and false positives by 68.8%. On the complete 20-sequence UAVDT test split, OATrack improves HOTA, MOTA, and IDF1 from 41.4345, 7.5162, and 51.4916 to 42.7743, 24.8760, and 53.2829, respectively, and reduces IDSW from 639 to 295. HOTA-component and size/occlusion analyses further demonstrate stronger association consistency and false-positive control across the two UAV benchmarks. Full article

(This article belongs to the Section Remote Sensors)

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23 pages, 2266 KB

Open AccessArticle

Topological Data Analysis-Driven fNIRS Signal Processing for Alzheimer’s Disease Stage Identification

by Siyuan Liu, Hangcheng Wu, Cheng Sun, Yuanbin Qiu, Haoliang Wu, Yucong Wei, Yang Lv and Zheng Yang

Sensors 2026, 26(16), 5221; https://doi.org/10.3390/s26165221 - 18 Aug 2026

Abstract

This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer’s Disease (AD) stages. Alzheimer’s disease is increasingly understood as a disconnection syndrome, where the disruption of functional [...] Read more.

This paper proposes a novel Topological Data Analysis (TDA) pipeline to extract robust structural features from functional near-infrared spectroscopy (fNIRS) signals for the classification of Alzheimer’s Disease (AD) stages. Alzheimer’s disease is increasingly understood as a disconnection syndrome, where the disruption of functional brain networks precedes gross anatomical atrophy. However, traditional graph-theoretic approaches rely on arbitrary connectivity thresholds, which can obscure critical multi-scale topological information, and are sensitive to noise. To address this, our framework leverages Persistent Homology (PH) to analyse the topological evolution of brain networks across a continuous range of scales. By modeling 48-channel hemoglobin concentration time-series as high-dimensional point clouds via Granger causality metrics, we construct filtration sequences of Vietoris–Rips complexes. The resulting topological invariants, including 0—dimensional connected components, 1—dimensional loops, and 2—dimensional voids, are first examined through Persistence Diagrams. For classification, significant H0 and H1 features are converted into Persistence Images using Gaussian kernel smoothing, while H2 features are retained for qualitative topological interpretation. This transformation enables the integration of complex topological features into standard machine learning workflows. Our experimental results were evaluated on a subject-level held-out test set consisting only of original, non-augmented recordings. Data augmentation was applied only to the training set to alleviate class imbalance. The proposed topology-driven feature extraction method achieved 86% accuracy in multi-class diagnosis (NC vs. MCI vs. AD). This study validates the efficacy of TDA as a sophisticated signal processing tool for revealing intrinsic neurodegenerative patterns in hemodynamic data, offering an exploratory methodological proof-of-concept for AD stage classification. Full article

(This article belongs to the Special Issue Advanced Sensing and Signal Processing Technologies for Medical Applications)

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23 pages, 2978 KB

Open AccessArticle

An Absorption-Based PDMS/f-CNT Mass-Capacitor for Continuous Monitoring of Fire-Derived Organic Contamination

by Negar Heidari, Morteza Ghafar-Zadeh, Azadeh Amrollahi, Parviz Norouzi, Sebastian Magierowski and Ebrahim Ghafar-Zadeh

Sensors 2026, 26(16), 5220; https://doi.org/10.3390/s26165220 - 18 Aug 2026

Abstract

Firefighters are exposed to complex smoke containing volatile, semivolatile, aromatic, and particulate-associated organic contaminants that accumulate on skin, clothing, and protective equipment. Conventional gas sensors monitor only selected airborne species, while passive samplers require laboratory analysis and cannot provide continuous exposure assessment. To [...] Read more.

Firefighters are exposed to complex smoke containing volatile, semivolatile, aromatic, and particulate-associated organic contaminants that accumulate on skin, clothing, and protective equipment. Conventional gas sensors monitor only selected airborne species, while passive samplers require laboratory analysis and cannot provide continuous exposure assessment. To address this limitation, we developed a proof-of-concept mass-capacitor that integrates contaminant absorption and electrical sensing within a single polydimethylsiloxane/functionalized carbon nanotube (PDMS/f-CNT) composite coated on interdigitated electrodes (IDEs). Unlike conventional sensors that report the instantaneous concentration of selected compounds, the proposed platform functions as both a sorptive collector and an electrical transducer. It continuously converts contaminant uptake, retention, and release into a time-resolved electrical response. For firefighter applications, this mass-capacitor bridges the gap between passive sorptive samplers and conventional real-time gas sensors. It enables continuous tracking of the cumulative sorbed contamination burden without requiring offline laboratory analysis or restricting the measurement to selected airborne species. A custom potentiostat applied staircase cyclic excitation, while Fast Fourier Transform (FFT)-assisted processing enabled extraction of the differential charge,

Δ Q

as an indicator of contaminant loading. The sensor was evaluated using smoke generated from cotton, paper, wood, and synthetic fibers. Compared with PDMS alone, the PDMS/f-CNT composite produced an approximately 24-fold higher response with minimal humidity interference (≈80% RH). Limits of detection ranged from 0.08 to 0.26 ppm, while repeated smoke exposures produced stepwise increases in ΔQ, demonstrating continuous tracking of cumulative contaminant loading. These results establish the feasibility of the mass-capacitor concept and introduce a new approach for real-time monitoring of the accumulated organic contamination burden rather than the instantaneous concentration of individual airborne compounds. Full article

(This article belongs to the Special Issue Sensors Technologies for Measurements and Signal Processing: 2nd Edition)

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18 pages, 4912 KB

Open AccessArticle

Reliability of a Home-Based Smartphone Balance Assessment in Healthy Middle-Aged and Older Adults

by Elizabeth Coker and Anat V. Lubetzky

Sensors 2026, 26(16), 5219; https://doi.org/10.3390/s26165219 - 18 Aug 2026

Abstract

Smartphone accelerometry could enable longitudinal home-based balance testing, yet its reliability across tasks and outcome measures must be established. We assessed the test–retest reliability and measurement precision of a custom smartphone balance application. Sixty-nine healthy, community-dwelling middle-aged and older adults (ages 41–76 years) [...] Read more.

Smartphone accelerometry could enable longitudinal home-based balance testing, yet its reliability across tasks and outcome measures must be established. We assessed the test–retest reliability and measurement precision of a custom smartphone balance application. Sixty-nine healthy, community-dwelling middle-aged and older adults (ages 41–76 years) performed a 5 s home-based balance assessment weekly for 3 weeks. The assessment was directed by the application, which generated accelerometer-based sway metrics. Participants performed two 30 s trials each of feet together and tandem stance (eyes open, eyes closed) and single leg stance (eyes open only). Intraclass correlation coefficients (ICCs) and relative standard error of measurement (SEM%) were calculated for time-domain and frequency-domain measures. Outcomes achieved good-to-excellent reliability when averaged across three weekly assessments (ICC = 0.59–0.95), particularly mediolaterally. Time-domain measures, particularly mean acceleration (ICC = 0.84–0.92; SEM% = 11.1–23.5%), were the most reliable and precise outcomes overall, while frequency-domain measures showed lower reliability and precision at higher spectral bands (ICC as low as 0.59; SEM% up to 41.5%). As standing tasks became more difficult, reliability and precision declined correspondingly. We conclude that minimally supervised, home-based balance assessment can produce reliable, precise sway measures in healthy, screened, iPhone-owning middle-aged and older adults. This application carries potential for large-scale, remote balance monitoring for research purposes, though validation in more diverse and higher-risk populations will be needed before clinical application can be recommended. Full article

(This article belongs to the Special Issue Advanced Sensors for Health Monitoring in Older Adults: 2nd Edition)

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24 pages, 17473 KB

Open AccessArticle

A Prototype Distributed Six-Port Reflectometer-Based Transmission-Only Vector Network Analyzer

by Muhammad Hashir, Colin Gilmore and Joe LoVetri

Sensors 2026, 26(16), 5218; https://doi.org/10.3390/s26165218 - 18 Aug 2026

Abstract

A novel distributed Transmission-Only Vector Network Analyzer (TVNA) based on two Six-Port Reflectometers (SPRs) is experimentally verified. The prototype distributed SPR-TVNA measures

S_{21}

and operates with two independent RF sources, allowing the two ports of the Device Under Test (DUT) to be [...] Read more.

A novel distributed Transmission-Only Vector Network Analyzer (TVNA) based on two Six-Port Reflectometers (SPRs) is experimentally verified. The prototype distributed SPR-TVNA measures

S_{21}

and operates with two independent RF sources, allowing the two ports of the Device Under Test (DUT) to be widely separated with no common cables coming to a central location. Using the distributed SPR-TVNA allows for measurements of, e.g., distantly separated antennas, or taking grain bin imaging measurements that would otherwise require long coaxial cables. A key issue of these large DUTs is accurate measurement of the through-measurement,

S_{21}

. The distribution (separation) of the two RF sources in the SPR-VNA requires novel procedures to obtain accurate results. The results are confirmed using simulations and experiments, with the experimental results obtained using custom-built hardware. While, in theory, a full VNA can measure

S_{11}

and

S_{22}

, which is confirmed through simulation, the experimental results show significant errors in

S_{11}

and

S_{22}

. We thus call the current device a Transmission-Only VNA. The fundamental math of the SPR-VNA means there is a ±180° phase uncertainty that must be solved, e.g., via a wide-band sweep looking for phase jumps. The experimental

S_{21}

measurements of the prototype show maximum errors of 1.5 dB in magnitude and 6 degrees in phase over a bandwidth of 0.6–1.2 GHz. Full article

(This article belongs to the Section Electronic Sensors)

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43 pages, 51571 KB

Open AccessArticle

Adaptive Control of Lower-Limb Assistive Exoskeleton for Rehabilitation Using Deep Reinforcement Learning

by Ali Foroutannia, Masoud Mohammadian and Kumudu Munasinghe

Sensors 2026, 26(16), 5217; https://doi.org/10.3390/s26165217 - 17 Aug 2026

Abstract

Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes [...] Read more.

Lower-limb rehabilitation exoskeletons have the potential to improve gait recovery after stroke by providing intensive and repetitive training. However, conventional control strategies often rely on fixed control parameters and exhibit limited adaptability to patient-specific characteristics, sensor noise, and dynamic uncertainties. This paper proposes an adaptive control framework that combines deep reinforcement learning (RL) with model-based impedance control for personalised lower-limb exoskeleton assistance. Patient-specific biological parameters are incorporated into the simulation environment and reward formulation to improve adaptability and robustness. Three state-of-the-art deep RL algorithms, Deep Deterministic Policy Gradient (DDPG), Twin Delayed Deep Deterministic Policy Gradient (TD3), and Soft Actor-Critic (SAC), are evaluated in a continuous control environment under varying signal-to-noise ratio (SNR) conditions ranging from 5 dB to noise-free conditions. Results demonstrate that TD3 achieves the most stable learning performance, obtaining a mean reward of −354.24 under noise-free conditions, while DDPG provides the highest joint-angle tracking accuracy with an RMSE of 0.0369 rad. SAC exhibits superior robustness in noisy environments, achieving the highest learning ratio of 0.51 at 5 dB SNR. Furthermore, the proposed personalised framework reduces tracking errors by up to 27% compared with non-personalised baseline approaches. The findings indicate that integrating patient-specific information with RL-based adaptive control can significantly enhance robustness, tracking performance, and personalisation in exoskeleton-assisted gait rehabilitation, providing a promising direction for future intelligent rehabilitation systems. Full article

(This article belongs to the Section Wearables)

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