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Volume 15
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J. Sens. Actuator Netw., Volume 15, Issue 1 (February 2026) – 23 articles

Cover Story (view full-size image): Solar Insecticidal Lamp Internet of Things (SIL-IoT) nodes enable sustainable pest control but suffer frequent faults in harsh farmlands, compromising data reliability and pest management effectiveness. Designing fault detection (FD) methods for SIL-IoT faces two key challenges: electromagnetic interference and edge device resource constraints. This survey systematically analyzes FD solutions tailored to these obstacles, covering signal processing for noisy data, lightweight models for edge deployment, and low-cost training for scarce fault data. A case study validates these approaches. This work provides a structured FD framework that extends beyond SIL-IoT to broader resource-constrained IoT applications, offering insights for reliable deployment in challenging environments. View this paper
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17 pages, 596 KB  
Article
Temporal Attentive Graph Networks for Financial Surveillance: An Incremental Multi-Scale Framework
by Wei Zhang, Yimin Shen, Hang Zhou, Bo Zhou, Xianju Zheng and Xiang Chen
J. Sens. Actuator Netw. 2026, 15(1), 23; https://doi.org/10.3390/jsan15010023 - 16 Feb 2026
Viewed by 1158
Abstract
Systemic risk propagation in modern financial markets is characterized by non-linear contagion and rapid topological evolution, rendering traditional static monitoring methods ineffective. Existing Graph Neural Networks (GNNs) often struggle to capture “structural breaks” during crises due to their reliance on static adjacency assumptions [...] Read more.
Systemic risk propagation in modern financial markets is characterized by non-linear contagion and rapid topological evolution, rendering traditional static monitoring methods ineffective. Existing Graph Neural Networks (GNNs) often struggle to capture “structural breaks” during crises due to their reliance on static adjacency assumptions and isotropic aggregation. To address these challenges, this study proposes the Temporal Attentive Graph Networks (TAGN), a dynamic framework designed for extreme volatility prediction and financial surveillance. TAGN constructs an incremental multi-scale graph by fusing high-frequency trading data, supply chain linkages, and institutional co-holdings to model heterogeneous risk transmission channels. Technically, it employs a deeply coupled GAT-GRU architecture, where the Graph Attention Network (GAT) dynamically assigns weights to contagion sources, and the Gated Recurrent Unit (GRU) memorizes the trajectory of structural evolution. Extensive experiments on the S&P 500 dataset (2018–2024) demonstrate that TAGN significantly outperforms state-of-the-art baselines, including WinGNN and PatchTST, achieving an AUC of 0.890 and a Precision at 50 of 61.5%. Notably, a risk early-warning index derived from TAGN exhibits a 1–2 week lead time over the VIX index during major market stress events, such as the Silicon Valley Bank collapse. This research facilitates a paradigm shift from historical statistical estimation to dynamic network-aware sensing, offering interpretable tools for RegTech applications. Full article
(This article belongs to the Section Big Data, Computing and Artificial Intelligence)
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27 pages, 3230 KB  
Article
Enhanced MQTT Protocol for Securing Big Data/Hadoop Data Management
by Ferdaous Kamoun-Abid and Amel Meddeb-Makhlouf
J. Sens. Actuator Netw. 2026, 15(1), 22; https://doi.org/10.3390/jsan15010022 - 16 Feb 2026
Viewed by 1009
Abstract
Big data has significantly transformed data processing and analytics across various domains. However, ensuring security and data confidentiality in distributed platforms such as Hadoop remains a challenging task. Distributed environments face major security issues, particularly in the management and protection of large-scale data. [...] Read more.
Big data has significantly transformed data processing and analytics across various domains. However, ensuring security and data confidentiality in distributed platforms such as Hadoop remains a challenging task. Distributed environments face major security issues, particularly in the management and protection of large-scale data. In this article, we focus on the cost of secure information transmission, implementation complexity, and scalability. Furthermore, we address the confidentiality of information stored in Hadoop by analyzing different AES encryption modes and examining their potential to enhance Hadoop security. At the application layer, we operate within our Hadoop environment using an extended, secure, and widely used MQTT protocol for large-scale data communication. This approach is based on implementing MQTT with TLS, and before connecting, we add a hash verification of the data nodes’ identities and send the JWT. This protocol uses TCP at the transport layer for underlying transmission. The advantage of TCP lies in its reliability and small header size, making it particularly suitable for big data environments. This work proposes a triple-layer protection framework. The first layer is the assessment of the performance of existing AES encryption modes (CTR, CBC, and GCM) with different key sizes to optimize data confidentiality and processing efficiency in large-scale Hadoop deployments. Afterwards, we propose evaluating the integrity of DataNodes using a novel verification mechanism that employs SHA-3-256 hashing to authenticate nodes and prevent unauthorized access during cluster initialization. At the third tier, the integrity of data blocks within Hadoop is ensured using SHA-3-256. Through extensive performance testing and security validation, we demonstrate integration. Full article
(This article belongs to the Section Network Security and Privacy)
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30 pages, 3829 KB  
Article
A Feature Fusion Framework for Improved Autism Spectrum Disorder Prediction Using sMRI and Phenotype Information
by Bhagya Lakshmi Polavarapu, V. Dinesh Reddy, Mahesh Kumar Morampudi, Md Muzakkir Hussain and Ashu Abdul
J. Sens. Actuator Netw. 2026, 15(1), 21; https://doi.org/10.3390/jsan15010021 - 15 Feb 2026
Cited by 1 | Viewed by 1389
Abstract
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and severity, posing significant challenges for accurate diagnosis. Approaches that rely on a single data source, or unimodal data, often fail to capture the disorder’s inherent heterogeneity. [...] Read more.
Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition characterized by a wide range of symptoms and severity, posing significant challenges for accurate diagnosis. Approaches that rely on a single data source, or unimodal data, often fail to capture the disorder’s inherent heterogeneity. A multimodal approach, which integrates diverse data types, can create a more holistic and precise understanding of ASD. This paper introduces the Multimodal ASD (MMASD) framework, a novel predictive model for ASD. The MMASD framework is built upon two distinct input modalities: structural magnetic resonance imaging (sMRI) and corresponding phenotype data. The sMRI data provides detailed neuroanatomical metrics, including brain tissue segmentation, volumetric measurements, and cortical thickness. Complementing this, the phenotype data encompasses the clinical and behavioral characteristics of each individual. In the proposed framework, latent features are independently extracted from both modalities and then fused to generate a comprehensive representation of the multimodal information. These fused features are then used to predict ASD by leveraging the outputs of various classifiers. A majority voting ensemble is employed to determine the final prediction. The MMASD framework achieves a high accuracy of 97.27%, surpassing the performance of current state-of-the-art approaches and demonstrating the efficacy of integrating neuroimaging and clinical data for ASD prediction. Full article
(This article belongs to the Section Big Data, Computing and Artificial Intelligence)
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26 pages, 6997 KB  
Article
A Low-Cost Smart Helmet with Accident Detection and Emergency Response for Bike Riders
by Muhammad Irfan Minhas, Imran Shah, Yasir Ali and Fawaz Nashmi M Alhusayni
J. Sens. Actuator Netw. 2026, 15(1), 20; https://doi.org/10.3390/jsan15010020 - 13 Feb 2026
Viewed by 2934
Abstract
The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, [...] Read more.
The high rate of bike commuting around the globe has greatly transformed the mode of transportation in cities, but the high speeds of motorized cycling have contributed to a high rate of serious road trauma. Although conventional helmets offer necessary passive structural protection, they do not consider the most important aspect of the emergency response, which is the Golden Hour the time frame during which medical intervention can have the most significant impact. This paper is a development and validation of an autonomous, low-cost smart helmet architecture that is programmed to operate in real-time to detect accidents and autonomously inform the operator of accidents. The system is built up of an ESP32 microcontroller with a multi-modal sensor package, which comprises an inertial measurement unit (IMU), force-impact sensors, and MQ-3 alcohol sensors to conduct proactive safety screening. To overcome the single threshold limitation of unreliable systems, a time-windowed sensor-fusion algorithm was applied in order to distinguish between normal riding dynamics and bona fide collisions. This reasoning involves concurrent cues of high-G inertial rotations and physical impacting features over a time window of 500 ms to reduce spurious activations. The architecture of the system is completely self-sufficient and employs an in-built GPS-GSM module to send the geographical location through SMS without the need to have a smartphone connection. The prototype was also put through 150 experimental tests, with some conducted in laboratories, and real-world running tests in diverse terrains. The findings reveal an accuracy in detection of 93.7, a false positive rate (FPR) of 2.6 and a mean emergency alert latency of 2.8 s. In addition, it was found that structural integrity was confirmed at ECE 22.05 impact conditions using Finite Element Analysis (FEA), with a safety factor of 1.38. These quantitative results mean that the proposed system is an effective way to address a cultural shift between passive structural protection and active rescue intervention as a statistical and computationally efficient safety measure of modern micro-mobility. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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29 pages, 7662 KB  
Article
Next Generation Intelligent Mobile Edge Networks for Improving Service Provisioning in Indonesian Festivals
by Vittalis Ayu and Milena Radenkovic
J. Sens. Actuator Netw. 2026, 15(1), 19; https://doi.org/10.3390/jsan15010019 - 6 Feb 2026
Viewed by 996
Abstract
Indonesia is a country of vast geographical and cultural diversity, hosting numerous cultural festivals annually, such as Sekaten, Labuhan, and the Lembah Baliem Festival. However, as the world’s largest archipelago country, Indonesia faces geographical challenges in terms of ensuring the reliability of communication [...] Read more.
Indonesia is a country of vast geographical and cultural diversity, hosting numerous cultural festivals annually, such as Sekaten, Labuhan, and the Lembah Baliem Festival. However, as the world’s largest archipelago country, Indonesia faces geographical challenges in terms of ensuring the reliability of communication networks, particularly in maintaining user experience in high-density, short-duration traffic burst environments, such as festivals. The nation’s network connectivity relies heavily on satellite networks and Palapa Ring, a national fibre-optic backbone network that comprises a combination of inland and underwater networks, connecting major and remote islands to the global internet. Although this solution can provide a baseline for broadband connectivity, an adaptive intelligent mobile edge-based solution is needed to complement the existing network infrastructure in order to meet the dynamic demands of localised and transient traffic surges across multiple temporary, geographically dispersed festival sites in both urban and rural areas. In this paper, we present a multimodal study that combines network connectivity measurements during a festival with an extensive user analysis of festival participants and organisers to investigate reliability gaps in user experience regarding network connectivity. Our findings show that internet connectivity was intermittently disrupted during the festival, and our user analysis revealed a gap between customer expectations and perceptions of network service quality and the provision of application services in a heterogeneous festival environment. To address this challenge, we propose a novel next-generation intelligent festival mobile edge framework, MobiFest, which integrates the multi-layer Cognitive Cache which has geospatial–temporal edge intelligence for localised service provisioning to improve the delivery of application services in both urban and rural festival environments. In our extensive experiments, we employ smart garbage as our use case and demonstrate how our complex, multimodal intelligent network protocol SmartGarbiC, designed based on MobiFest for garbage management services, outperforms state-of-the-art and benchmark protocols. Full article
(This article belongs to the Section Communications and Networking)
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21 pages, 4405 KB  
Article
Performance Benchmarking of 5G SA and NSA Networks for Wireless Data Transfer
by Miha Pipan, Marko Šimic and Niko Herakovič
J. Sens. Actuator Netw. 2026, 15(1), 18; https://doi.org/10.3390/jsan15010018 - 2 Feb 2026
Viewed by 2294
Abstract
This paper presents test results of the performance comparison of 5G standalone (SA) and non-standalone (NSA) networks in the context of gathering data of remote sensors and machines. The study evaluates key network characteristics such as latency, throughput, jitter and packet loss (for [...] Read more.
This paper presents test results of the performance comparison of 5G standalone (SA) and non-standalone (NSA) networks in the context of gathering data of remote sensors and machines. The study evaluates key network characteristics such as latency, throughput, jitter and packet loss (for UDP protocol only) using standardized tests to gain insights into the impact of these factors on real-time and data-intensive communication. In addition, a range of communication protocols including OPC UA, Modbus, MQTT, AMQP, CoAP, EtherCAT and gRPC were tested to assess their efficiency, scalability and suitability with different send data sizes. By conducting experiments in a controlled hardware environment, we have analyzed the impact of the 5G architecture on protocol behavior and measured the transmission performance at different data sizes and connection configurations. Particular attention is paid to protocol overhead, data transfer rates and responsiveness, which are crucial for industrial automation and IoT deployments. The results show that SA networks consistently offer lower latency and more stable performance, where robust and low-latency data transfer is essential. In contrast, lightweight IoT protocols such as MQTT and CoAP demonstrate reliable operation in both SA and NSA environments due to their low overhead and adaptability. These insights are equally important for time-critical industrial protocols such as EtherCAT and OPC UA, where stability and responsiveness are crucial for automation and control. The study highlights current limitations of 5G networks in supporting both remote sensing and industrial use cases, while providing guidance for selecting the most suitable communication protocols depending on network infrastructure and application requirements. Moreover, the results indicate directions for configuring and optimizing future 5G networks to better meet the demands of remote sensing systems and Industry 4.0 environments. Full article
(This article belongs to the Section Communications and Networking)
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15 pages, 3269 KB  
Article
Mitigating Salinity Effects in UWOC Using Integrated Polarization-Multiplexed MIMO Architecture
by Sushank Chaudhary
J. Sens. Actuator Netw. 2026, 15(1), 17; https://doi.org/10.3390/jsan15010017 - 2 Feb 2026
Viewed by 818
Abstract
Underwater wireless optical communication (UWOC) has emerged as a key enabler for Internet of Underwater Things (IoUT) and autonomous sensing networks, but its reliability is severely affected by salinity-induced attenuation, scattering, and turbulence. This work presents a high-speed and salinity-resilient UWOC architecture that [...] Read more.
Underwater wireless optical communication (UWOC) has emerged as a key enabler for Internet of Underwater Things (IoUT) and autonomous sensing networks, but its reliability is severely affected by salinity-induced attenuation, scattering, and turbulence. This work presents a high-speed and salinity-resilient UWOC architecture that jointly exploits Polarization Division Multiplexing (PDM) and Multiple-Input Multiple-Output (MIMO) diversity to enhance link capacity and robustness in realistic oceanic conditions. Two 1 Gbps NRZ data channels at 1550 nm were transmitted using continuous-wave lasers and evaluated using a hybrid OptiSystem–MATLAB simulation framework with full channel modeling of absorption, scattering, turbulence, and salinity (32–36 ppt). Results reveal that the proposed PDM-MIMO system achieves more than an order-of-magnitude bit-error-rate (BER) reduction compared with non-MIMO or single-polarization baselines, maintaining acceptable BER levels up to 20 m. Performance degradation with increasing salinity is quantified, and results confirm that combined PDM and spatial diversity effectively mitigate salinity-induced losses. The presented design demonstrates a viable and scalable solution for next-generation underwater sensing and communication networks in coastal and deep-sea ecosystems. Full article
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26 pages, 24395 KB  
Article
Deep Learning-Based Ink Droplet State Recognition for Continuous Inkjet Printing
by Jianbin Xiong, Jing Wang, Qi Wang, Jianxiang Yang, Xiangjun Dong, Weikun Dai and Qianguang Zhang
J. Sens. Actuator Netw. 2026, 15(1), 16; https://doi.org/10.3390/jsan15010016 - 1 Feb 2026
Viewed by 1028
Abstract
The high-quality droplet formation in continuous inkjet printing (CIJ) is crucial for precise character deposition on product surfaces. This process, where a piezoelectric transducer perturbs a high-speed ink stream to generate micro-droplets, is highly sensitive to parameters like ink pressure and transducer amplitude. [...] Read more.
The high-quality droplet formation in continuous inkjet printing (CIJ) is crucial for precise character deposition on product surfaces. This process, where a piezoelectric transducer perturbs a high-speed ink stream to generate micro-droplets, is highly sensitive to parameters like ink pressure and transducer amplitude. Suboptimal conditions lead to satellite droplet formation and charge transfer issues, adversely affecting print quality and necessitating reliable monitoring. Replacing inefficient manual inspection, this study develops MBSim-YOLO, a deep learning-based method for automated droplet detection. The proposed model enhances the YOLOv8 architecture by integrating MobileNetv3 to reduce computational complexity, a Bidirectional Feature Pyramid Network (BiFPN) for effective multi-scale feature fusion, and a Simple Attention Module (SimAM) to enhance feature representation robustness. A dataset was constructed using images captured by a CCD camera during the droplet ejection process. Experimental results demonstrate that MBSim-YOLO reduces the parameter count by 78.81% compared to the original YOLOv8. At an Intersection over Union (IoU) threshold of 0.5, the model achieved a precision of 98.2%, a recall of 99.1%, and a mean average precision (mAP) of 98.9%. These findings confirm that MBSim-YOLO achieves an optimal balance between high detection accuracy and lightweight performance, offering a viable and efficient solution for real-time, automated quality monitoring in industrial continuous inkjet printing applications. Full article
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26 pages, 3401 KB  
Article
Toward an Integrated IoT–Edge Computing Framework for Smart Stadium Development
by Nattawat Pattarawetwong, Charuay Savithi and Arisaphat Suttidee
J. Sens. Actuator Netw. 2026, 15(1), 15; https://doi.org/10.3390/jsan15010015 - 1 Feb 2026
Viewed by 1483
Abstract
Large sports stadiums require robust real-time monitoring due to high crowd density, complex spatial configurations, and limited network infrastructure. This research evaluates a hybrid edge–cloud architecture implemented in a national stadium in Thailand. The proposed framework integrates diverse surveillance subsystems, including automatic number [...] Read more.
Large sports stadiums require robust real-time monitoring due to high crowd density, complex spatial configurations, and limited network infrastructure. This research evaluates a hybrid edge–cloud architecture implemented in a national stadium in Thailand. The proposed framework integrates diverse surveillance subsystems, including automatic number plate recognition, face recognition, and panoramic cameras, with edge-based processing to enable real-time situational awareness during high-attendance events. A simulation based on the stadium’s physical layout and operational characteristics is used to analyze coverage patterns, processing locations, and network performance under realistic event scenarios. The results show that geometry-informed sensor deployment ensures continuous visual coverage and minimizes blind zones without increasing camera density. Furthermore, relocating selected video processing tasks from the cloud to the edge reduces uplink bandwidth requirements by approximately 50–75%, depending on the processing configuration, and stabilizes data transmission during peak network loads. These findings suggest that processing location should be considered a primary architectural design factor in smart stadium systems. The combination of edge-based processing with centralized cloud coordination offers a practical model for scalable, safety-oriented monitoring solutions in high-density public venues. Full article
(This article belongs to the Section Big Data, Computing and Artificial Intelligence)
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24 pages, 15789 KB  
Data Descriptor
Multi-Background UAV Spraying Behavior Recognition Dataset for Precision Agriculture
by Chang Meng, Lei Shu and Leijing Bai
J. Sens. Actuator Netw. 2026, 15(1), 14; https://doi.org/10.3390/jsan15010014 - 26 Jan 2026
Viewed by 1329
Abstract
The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection [...] Read more.
The rapid growth of precision agriculture has accelerated the deployment of plant protection unmanned aerial vehicles (UAVs). However, reliable data resources for vision-based intelligent supervision of operational states, such as whether a UAV is currently spraying, remain limited. Most publicly available UAV detection datasets target urban security and surveillance scenarios, where annotations emphasize object localization rather than agricultural operation state recognition, making them insufficient for farmland spraying supervision. Therefore, agricultural-oriented data resources are needed to cover diverse backgrounds and include operation state labels, thereby supporting both academic research and practical deployment. In this study, we construct and release the first multi-background dataset dedicated to agricultural UAV spraying behavior recognition. The dataset contains 9548 high-quality annotated images spanning the following six typical backgrounds: green cropland, bare farmland, orchard, woodland, mountainous terrain, and sky. For each UAV instance, we provide both a bounding box and a binary operation state label, namely spraying and flying without spraying. We further conduct systematic benchmark evaluations of mainstream object detection algorithms on this dataset. The dataset captures agriculture-specific challenges, including a high proportion of small objects, substantial scale variation, motion blur, and complex dynamic backgrounds, and can be used to assess algorithm robustness in real-world agricultural settings. Benchmark results show that YOLOv5n achieves the best overall performance, with an accuracy of 97.86% and an mAP@50 of 98.30%. This dataset provides critical data support for automated supervision of plant protection UAV spraying operations and precision agriculture monitoring platforms. Full article
(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)
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18 pages, 4674 KB  
Article
AI Correction of Smartphone Thermal Images: Application to Diabetic Plantar Foot
by Hafid Elfahimi, Rachid Harba, Asma Aferhane, Hassan Douzi and Ikram Damoune
J. Sens. Actuator Netw. 2026, 15(1), 13; https://doi.org/10.3390/jsan15010013 - 26 Jan 2026
Cited by 1 | Viewed by 939
Abstract
Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated [...] Read more.
Prevention of complications related to diabetic foot (DF) can now be performed using smartphone-connected thermal cameras. However, the absolute error associated with these devices remains particularly high, compromising measurement reliability, especially under variable environmental conditions. To address this, we introduce a physiologically motivated two-region segmentation task (forehead + plantar foot) to enable stable temperature correction. First, we developed a fully automated joint method for this task, building upon a new multimodal thermal–RGB dataset constructed with detailed annotation procedures. Five deep learning methods (U-Net, U-Net++, SegNet, DE-ResUnet, and DE-ResUnet++) were evaluated and compared to traditional baselines (Adaptive Thresholding and Region Growing), demonstrating the clear advantage of data-driven approaches. The best performance was achieved by the DE-ResUnet++ architecture (Dice score: 98.46%). Second, we validated the correction approach through a clinical study. Results showed that the variance of corrected temperatures was reduced by half compared to absolute values (p < 0.01), highlighting the effectiveness of the correction approach. Furthermore, corrected temperatures successfully distinguished DF patients from healthy controls (p < 0.01), unlike absolute temperatures. These findings suggest that our approach could enhance the performance of smartphone-connected thermal devices and contribute to the early prevention of DF complications. Full article
(This article belongs to the Special Issue IoT and Networking Technologies for Smart Mobile Systems)
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26 pages, 6505 KB  
Article
Hybrid Wavelet–Transformer–XGBoost Model Optimized by Chaotic Billiards for Global Irradiance Forecasting
by Walid Mchara, Giovanni Cicceri, Lazhar Manai, Monia Raissi and Hezam Albaqami
J. Sens. Actuator Netw. 2026, 15(1), 12; https://doi.org/10.3390/jsan15010012 - 22 Jan 2026
Viewed by 975
Abstract
Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric [...] Read more.
Accurate global irradiance (GI) forecasting is essential for improving photovoltaic (PV) energy management, stabilizing renewable power systems, and enabling intelligent control in solar-powered applications, including electric vehicles and smart grids. The highly stochastic and non-stationary nature of solar radiation, influenced by rapid atmospheric fluctuations and seasonal variability, makes short-term GI prediction a challenging task. To overcome these limitations, this work introduces a new hybrid forecasting architecture referred to as WTX–CBO, which integrates a Wavelet Transform (WT)-based decomposition module, an encoder–decoder Transformer model, and an XGBoost regressor, optimized using the Chaotic Billiards Optimizer (CBO) combined with the Adam optimization algorithm. In the proposed architecture, WT decomposes solar irradiance data into multi-scale components, capturing both high-frequency transients and long-term seasonal patterns. The Transformer module effectively models complex temporal and spatio-temporal dependencies, while XGBoost enhances nonlinear learning capability and mitigates overfitting. The CBO ensures efficient hyperparameter tuning and accelerated convergence, outperforming traditional meta-heuristics such as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). Comprehensive experiments conducted on real-world GI datasets from diverse climatic conditions demonstrate the outperformance of the proposed model. The WTX–CBO ensemble consistently outperformed benchmark models, including LSTM, SVR, standalone Transformer, and XGBoost, achieving improved accuracy, stability, and generalization capability. The proposed WTX–CBO framework is designed as a high-accuracy decision-support forecasting tool that provides short-term global irradiance predictions to enable intelligent energy management, predictive charging, and adaptive control strategies in solar-powered applications, including solar electric vehicles (SEVs), rather than performing end-to-end vehicle or photovoltaic power simulations. Overall, the proposed hybrid framework provides a robust and scalable solution for short-term global irradiance forecasting, supporting reliable PV integration, smart charging control, and sustainable energy management in next-generation solar systems. Full article
(This article belongs to the Special Issue AI and IoT Convergence for Sustainable Smart Manufacturing)
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26 pages, 3535 KB  
Review
A Survey on Fault Detection of Solar Insecticidal Lamp Internet of Things: Recent Advance, Challenge, and Countermeasure
by Xing Yang, Zhengjie Wang, Lei Shu, Fan Yang, Xuanchen Guo and Xiaoyuan Jing
J. Sens. Actuator Netw. 2026, 15(1), 11; https://doi.org/10.3390/jsan15010011 - 19 Jan 2026
Viewed by 734
Abstract
Ensuring food security requires innovative, sustainable pest management solutions. The Solar Insecticidal Lamp Internet of Things (SIL-IoT) represents such an advancement, yet its reliability in harsh, variable outdoor environments is compromised by frequent component and sensor faults, threatening effective pest control and data [...] Read more.
Ensuring food security requires innovative, sustainable pest management solutions. The Solar Insecticidal Lamp Internet of Things (SIL-IoT) represents such an advancement, yet its reliability in harsh, variable outdoor environments is compromised by frequent component and sensor faults, threatening effective pest control and data integrity. This paper presents a comprehensive survey on fault detection (FD) for SIL-IoT systems, systematically analyzing their unique challenges, including electromagnetic interference, resource constraints, data scarcity, and network instability. To address these challenges, we investigate countermeasures, including blind source separation for signal decomposition under interference, lightweight model techniques for edge deployment, and transfer/self-supervised learning for low-cost fault modeling across diverse agricultural scenarios. A dedicated case study, utilizing sensor fault data of SIL-IoT, demonstrates the efficacy of these approaches: an empirical mode decomposition-enhanced model achieved 97.89% accuracy, while a depthwise separable-based convolutional neural network variant reduced computational cost by 88.7% with comparable performance. This survey not only synthesizes the state of the art but also provides a structured framework and actionable insights for developing robust, efficient, and scalable FD solutions, thereby enhancing the operational reliability and sustainability of SIL-IoT systems. Full article
(This article belongs to the Special Issue Fault Diagnosis in the Internet of Things Applications)
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21 pages, 1676 KB  
Article
Fuzzy Logic-Based Data Flow Control for Long-Range Wide Area Networks in Internet of Military Things
by Rachel Kufakunesu, Herman C. Myburgh and Allan De Freitas
J. Sens. Actuator Netw. 2026, 15(1), 10; https://doi.org/10.3390/jsan15010010 - 14 Jan 2026
Viewed by 807
Abstract
The Internet of Military Things (IoMT) relies on Long-Range Wide Area Networks (LoRaWAN) for low-power, long-range communication in critical applications like border security and soldier health monitoring. However, conventional priority-based flow control mechanisms, which rely on static classification thresholds, lack the adaptability to [...] Read more.
The Internet of Military Things (IoMT) relies on Long-Range Wide Area Networks (LoRaWAN) for low-power, long-range communication in critical applications like border security and soldier health monitoring. However, conventional priority-based flow control mechanisms, which rely on static classification thresholds, lack the adaptability to handle the nuanced, continuous nature of physiological data and dynamic network states. To overcome this rigidity, this paper introduces a novel, domain-adaptive Fuzzy Logic Flow Control (FFC) protocol specifically tailored for LoRaWAN-based IoMT. While employing established Mamdani inference, the FFC system innovatively fuses multi-parameter physiological data (body temperature, blood pressure, oxygen saturation, and heart rate) into a continuous Health Score, which is then mapped via a context-optimised sigmoid function to dynamic transmission intervals. This represents a novel application-layer semantic integration with LoRaWAN’s constrained MAC and PHY layers, enabling cross-layer flow optimisation without protocol modification. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency relative to traditional static priority architectures. Seamlessly integrated into the NS-3 LoRaWAN simulation framework, the FFC protocol demonstrates superior performance in IoMT communications. Simulation results confirm that FFC significantly enhances reliability and energy efficiency while reducing latency compared with traditional static priority-based architectures. It achieves this by prioritising high-priority health telemetry, proactively mitigating network congestion, and optimising energy utilisation, thereby offering a robust solution for emergent, health-critical scenarios in resource-constrained environments. Full article
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26 pages, 3863 KB  
Article
A Pre-Industrial Prototype for a Tele-Operable Drone-Mountable Electrical Sensor
by Khaled Osmani, Marc Florian Meyer and Detlef Schulz
J. Sens. Actuator Netw. 2026, 15(1), 9; https://doi.org/10.3390/jsan15010009 - 13 Jan 2026
Viewed by 893
Abstract
This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through [...] Read more.
This paper presents a pre-industrial, laboratory-stage version of an innovative sensor box designed to enable remote measurement of electrical currents. The proposed prototype functions as a drone-mounted payload that can be deployed onto overhead transmission lines. Utilizing Hall-effect sensors, electronic signal processing through filtering, and digital data transmission via Arduino and Bluetooth, the instantaneous line currents are visualized in MATLAB (R2023a) as time-based curves. The sensor box can also be remotely released from the transmission line once measurements are complete, allowing a fully autonomous mode of operation. Laboratory tests demonstrated promising results for real-world applications, with measurement efficiencies ranging from 92% to 98% under various test conditions, including stress tests involving harmonics and total harmonic distortion up to 40%. Future work will focus on implementing effective shielding against high electric fields to further enhance reliability and advance the sensor’s industrialization as a novel solution for power grid digitalization. Full article
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35 pages, 2688 KB  
Review
Measurement Uncertainty and Traceability in Upper Limb Rehabilitation Robotics: A Metrology-Oriented Review
by Ihtisham Ul Haq, Francesco Felicetti and Francesco Lamonaca
J. Sens. Actuator Netw. 2026, 15(1), 8; https://doi.org/10.3390/jsan15010008 - 7 Jan 2026
Cited by 1 | Viewed by 1334
Abstract
Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning [...] Read more.
Upper-limb motor impairment is a major consequence of stroke and neuromuscular disorders, imposing a sustained clinical and socioeconomic burden worldwide. Quantitative assessment of limb positioning and motion accuracy is fundamental to rehabilitation, guiding therapy evaluation and robotic assistance. The evolution of upper-limb positioning systems has progressed from optical motion capture to wearable inertial measurement units (IMUs) and, more recently, to data-driven estimators integrated with rehabilitation robots. Each generation has aimed to balance spatial accuracy, portability, latency, and metrological reliability under ecological conditions. This review presents a systematic synthesis of the state of measurement uncertainty, calibration, and traceability in upper-limb rehabilitation robotics. Studies are categorised across four layers, i.e., sensing, fusion, cognitive, and metrological, according to their role in data acquisition, estimation, adaptation, and verification. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol was followed to ensure transparent identification, screening, and inclusion of relevant works. Comparative evaluation highlights how modern sensor-fusion and learning-based pipelines achieve near-optical angular accuracy while maintaining clinical usability. Persistent challenges include non-standard calibration procedures, magnetometer vulnerability, limited uncertainty propagation, and absence of unified traceability frameworks. The synthesis indicates a gradual transition toward cognitive and uncertainty-aware rehabilitation robotics in which metrology, artificial intelligence, and control co-evolve. Traceable measurement chains, explainable estimators, and energy-efficient embedded deployment emerge as essential prerequisites for regulatory and clinical translation. The review concludes that future upper-limb systems must integrate calibration transparency, quantified uncertainty, and interpretable learning to enable reproducible, patient-centred rehabilitation by 2030. Full article
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24 pages, 1146 KB  
Systematic Review
Industrial Wireless Networks in Industry 4.0: A Systematic Review
by Christos Tsallis, Panagiotis Papageorgas, Dimitrios Piromalis and Radu Adrian Munteanu
J. Sens. Actuator Netw. 2026, 15(1), 7; https://doi.org/10.3390/jsan15010007 - 6 Jan 2026
Viewed by 1988
Abstract
Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and [...] Read more.
Industrial wireless sensor and actuator networks (IWSANs) are central to Industry 4.0, supporting distributed sensing, actuation, and communication in cyber-physical production systems. Unlike previous studies, which focus on isolated constraints, this review synthesises recent work across eight coupled dimensions. These span reliability and fault tolerance, security and trust, time synchronisation, energy harvesting and power management, media access control (MAC) and scheduling, interoperability, routing and topology control, and real-world validation, within a unified comparative framework. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, a Scopus search identified 60 primary publications published between 2022 and 2025. The analysis shows a clear shift from reactive designs to predictive approaches that incorporate learning methods and energy considerations. Fault detection now relies on deep learning (DL) and statistical modelling, security incorporates trust and intrusion detection, and new synchronisation and MAC schemes approach wired levels of determinism. Regarding applied contributions, the analysis notes that routing and energy harvesting advances extend network lifetime. However, gaps remain in mobility support, interoperability across protocol layers, and field validation. The present work outlines these open issues and highlights research directions needed to mature IWSANs into robust infrastructure for Industry 4.0 and the emerging Industry 5.0 vision. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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23 pages, 725 KB  
Article
From Sound to Risk: Streaming Audio Flags for Real-World Hazard Inference Based on AI
by Ilyas Potamitis
J. Sens. Actuator Netw. 2026, 15(1), 6; https://doi.org/10.3390/jsan15010006 - 1 Jan 2026
Viewed by 1681
Abstract
Seconds count differently for people in danger. We present a real-time streaming pipeline for audio-based detection of hazardous life events affecting life and property. The system operates online rather than as a retrospective analysis tool. Its objective is to reduce the latency between [...] Read more.
Seconds count differently for people in danger. We present a real-time streaming pipeline for audio-based detection of hazardous life events affecting life and property. The system operates online rather than as a retrospective analysis tool. Its objective is to reduce the latency between the occurrence of a crime, conflict, or accident and the corresponding response by authorities. The key idea is to map reality as perceived by audio into a written story and question the text via a large language model. The method integrates streaming, zero-shot algorithms in an online decoding mode that convert sound into short, interpretable tokens, which are processed by a lightweight language model. CLAP text–audio prompting identifies agitation, panic, and distress cues, combined with conversational dynamics derived from speaker diarization. Lexical information is obtained through streaming automatic speech recognition, while general audio events are detected by a streaming version of Audio Spectrogram Transformer tagger. Prosodic features are incorporated using pitch- and energy-based rules derived from robust F0 tracking and periodicity measures. The system uses a large language model configured for online decoding and outputs binary (YES/NO) life-threatening risk decisions every two seconds, along with a brief justification and a final session-level verdict. The system emphasizes interpretability and accountability. We evaluate it on a subset of the X-Violence dataset, comprising only real-world videos. We release code, prompts, decision policies, evaluation splits, and example logs to enable the community to replicate, critique, and extend our blueprint. Full article
(This article belongs to the Topic Trends and Prospects in Security, Encryption and Encoding)
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35 pages, 4677 KB  
Article
A Comprehensive Multiple Linear Regression Modeling and Analysis of LoRa User Device Energy Consumption
by Josip Lorincz, Marko Kusačić, Edin Čusto and Zoran Blažević
J. Sens. Actuator Netw. 2026, 15(1), 5; https://doi.org/10.3390/jsan15010005 - 29 Dec 2025
Viewed by 1438
Abstract
The rapid expansion of Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) protocol technologies in large-scale Internet of Things (IoT) deployments highlights the need for precise and analytically grounded energy consumption (EC) estimation of battery-powered LoRa end devices (DVs). Since LoRa [...] Read more.
The rapid expansion of Long Range (LoRa) and Long Range Wide Area Network (LoRaWAN) protocol technologies in large-scale Internet of Things (IoT) deployments highlights the need for precise and analytically grounded energy consumption (EC) estimation of battery-powered LoRa end devices (DVs). Since LoRa DV instantaneous EC strongly depends on key transmission parameters, primarily including spreading factor (SF), transmit (Tx) power, and LoRa message packet size (PS), accurate modelling of their combined influence is essential for optimizing LoRa end DV lifetime, ensuring energy-efficient network operation, and supporting transmission parameter-adaptive communication strategies. Motivated by these needs, this paper presents a comprehensive multiple linear regression modelling framework for quantifying LoRa end DV EC during one transmission and reception LoRa end DV Class A communication cycle. The study is based on extensive high-resolution electric-current measurements collected over 69 measurement sets spanning different combinations of SFs, Tx power levels, and PS values. Based on measurement results, a total of 14 multiple linear regression models are developed, each capturing the joint impact of two transmission parameters while holding the third fixed. The developed regression models are mathematically formulated using linear, interaction, and polynomial terms to accurately express nonlinear EC behavior. Detailed statistical accuracy assessments demonstrate excellent goodness of fit of the developed EC multiple linear regression models. Complementary numerical analyses of regression models EC data distribution further validate regression models’ reliability, and highlight transmission parameter-driven variability of Lora end DV EC. The results of numerical analyses for LoRa end DV EC data distribution show that specific combinations of SF, Tx power, and PS transmit parameters amplify or mitigate EC differences, demonstrating that their joint variability patterns can significantly alter instantaneous energy demand across operating conditions. These interactions underscore the importance of modelling parameters together, rather than in isolation. The developed regression models provide interpretable mathematical formulations of instantaneous LoRa end DV EC prediction for transmission at different combinations of transmission parameters, and offer practical value for energy-aware configuration, battery-lifetime planning, and optimization of LoRa network-based IoT systems. Full article
(This article belongs to the Special Issue Applications of Wireless Sensor Networks: Innovations and Future Trends)
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21 pages, 65310 KB  
Article
The Effect of Electromagnetic Pulse Attacks on USB Camera Performance
by Gang Wei, Lei Shu, Wei Lin, Xing Yang, Ru Han, Kailiang Li and Kai Huang
J. Sens. Actuator Netw. 2026, 15(1), 4; https://doi.org/10.3390/jsan15010004 - 29 Dec 2025
Viewed by 2232
Abstract
The camera is a core device for modern surveillance and data collection, widely used in various fields including security, transportation, and healthcare. However, their widespread deployment has proportionally escalated associated security risks. This paper initially examines the current state of research on attack [...] Read more.
The camera is a core device for modern surveillance and data collection, widely used in various fields including security, transportation, and healthcare. However, their widespread deployment has proportionally escalated associated security risks. This paper initially examines the current state of research on attack methods targeting camera systems, providing a comprehensive review of various attack techniques and their security implications. Subsequently, we focus on a specific attack method against universal serial bus (USB) cameras, known as electromagnetic pulse (EMP) attacks, which utilize EMP to prevent the system from detecting the cameras. We simulated EMP attacks using a solar insecticidal lamp (which generates EMP by releasing high-voltage pulses) and a commercially available EMP generator. The performance of the cameras under various conditions was evaluated by adjusting the number of filtering magnetic rings on the USB cable and the distance between the camera and the interference source. The results demonstrate that some USB cameras are vulnerable to EMP attacks. Although EMP attacks might not invariably cause image distortion or permanent damage, their covert nature can lead to false detection of system failures, data security, and system maintenance. Based on these findings, it is recommended to determine the optimal number of shielding rings for cameras or their safe distance from EMP sources through the experimental approach outlined in this study, thereby enhancing the security and resilience of USB camera enabled systems in specific scenarios. Full article
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32 pages, 2768 KB  
Review
Fiber-Optic Gyroscopes: Architectures, Signal Processing, Error Compensation, and Emerging Trends
by Yerlan Tashtay, Nurzhigit Smailov, Daulet Naubetov, Akezhan Sabibolda, Yerzhan Nussupov, Nurzhamal Kashkimbayeva, Yersaiyn Mailybayev and Askhat Batyrgaliyev
J. Sens. Actuator Netw. 2026, 15(1), 3; https://doi.org/10.3390/jsan15010003 - 25 Dec 2025
Cited by 1 | Viewed by 2847
Abstract
Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant [...] Read more.
Fiber-optic gyroscopes (FOGs) have become one of the most important elements of modern inertial navigation systems due to their high accuracy, reliability, and independence from external signals such as satellite navigation. This review analyzes and discusses the key FOG architectures: interferometric (IFOG), resonant (RFOG), digital (DFOG), and hybrid (HFOG). The concepts of their functioning, structural features, and the main advantages and limitations of each architecture are examined. Particular focus is placed on advanced signal-processing and error-compensation algorithms, including filtering techniques, noise suppression, mitigation of thermal and mechanical drifts, and emerging machine learning (ML) based approaches. The analysis of these architectures is carried out in terms of major parameters that determine accuracy, robustness, and miniaturization potential. Various applications of FOGs in space systems, ground platforms, marine and underwater navigation, aviation, and scientific research are also being considered. Finally, the latest development trends are summarized, with a particular focus on miniaturization, integration with additional sensors, and the introduction of digital and AI-driven solutions, aimed at achieving higher accuracy, long-term stability, and resilience to real-world disturbances. Full article
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31 pages, 1929 KB  
Article
Robust Physical-Layer Key Generation Using UWB in Industrial IoT: A Measurement-Based Analysis
by Lorenzo Mario Amorosa, Stefano Caputo, Lorenzo Mucchi and Gianni Pasolini
J. Sens. Actuator Netw. 2026, 15(1), 2; https://doi.org/10.3390/jsan15010002 - 23 Dec 2025
Viewed by 727
Abstract
This paper addresses the confidentiality of wireless communications in industrial internet-of-things environments by investigating the feasibility of secret key generation for link-layer encryption using ultra wideband (UWB) signals. Taking advantage of the nanosecond-level temporal resolution offered by ultra wideband, we exploit channel reciprocity [...] Read more.
This paper addresses the confidentiality of wireless communications in industrial internet-of-things environments by investigating the feasibility of secret key generation for link-layer encryption using ultra wideband (UWB) signals. Taking advantage of the nanosecond-level temporal resolution offered by ultra wideband, we exploit channel reciprocity to extract highly detailed, noise-like channel measurements, in line with the physical-layer security paradigm. Three key generation algorithms, operating in both the time and frequency domains, are evaluated using real-world data collected through a dedicated measurement campaign in an industrial setting. The analysis, conducted under realistic conditions, examines the impact of practical impairments, such as imperfect channel reciprocity and timing misalignments, on the key agreement rate and the length of the generated keys. The results confirm the strong potential of ultra wideband technology to enable robust physical-layer security, offering a viable and efficient solution for securing wireless communications in complex and dynamic industrial internet-of-things environments. Full article
(This article belongs to the Special Issue Industrial Networks of the Future Across the Edge-to-Cloud Continuum)
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28 pages, 4342 KB  
Article
Parkinson’s Disease Classification Using Machine Learning and Wrist Rigidity Measurements from an Active Orthosis
by Adriano Alves Pereira, Daniel Hilário da Silva, Caio Tonus Ribeiro, Caroline Valentini de Queiroz, Luanne Cardoso Mendes, Leandro Rodrigues da Silva Souza, Selma Terezinha Milagre, Adriano de Oliveira Andrade and Carlos Dias Maciel
J. Sens. Actuator Netw. 2026, 15(1), 1; https://doi.org/10.3390/jsan15010001 - 19 Dec 2025
Viewed by 983
Abstract
Background: Rigidity is a cardinal symptom of Parkinson’s Disease (PD), yet its clinical evaluation remains largely subjective and susceptible to errors. This study introduces an innovative method for objectively classifying individuals with PD by combining an active wrist orthosis with Machine Learning (ML) [...] Read more.
Background: Rigidity is a cardinal symptom of Parkinson’s Disease (PD), yet its clinical evaluation remains largely subjective and susceptible to errors. This study introduces an innovative method for objectively classifying individuals with PD by combining an active wrist orthosis with Machine Learning (ML) models. Methods: The orthosis, equipped with current and force sensors, recorded biomechanical signals during passive wrist flexion and extension, from which twelve quantitative features were extracted. Data were collected from 30 participants (15 with PD and 15 Healthy Controls). Nineteen supervised ML algorithms were systematically evaluated through feature selection, cross-validation, and hyperparameter tuning. Results: Using all twelve features, QDA achieved an accuracy of 0.889 and sensitivity of 1.000, followed by GPC (0.778) and LDA (0.778). After applying feature selection with the Correlation-based Feature Subset to reduce redundancy, Extra Trees reached 0.833 accuracy, while both QDA and GPC maintained accuracies of 0.778. This consistency across models, even with a reduced feature set, highlights the robustness of the extracted biomarkers. Conclusions: These findings confirm that wrist rigidity signals provide discriminative quantitative information between PD patients and HC and are able to support PD classification, combining engineering innovation with clinical practice that highlights the potential of integrating wearable devices and ML as a personalized healthcare in PD. Full article
(This article belongs to the Section Actuators, Sensors and Devices)
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