Search Results (5,400)

Accurate color classification plays a critical role across diverse fields, from textile manufacturing and environmental monitoring to biomedical diagnostics. This study introduces a machine-learning-driven approach to spectral color sensing using SENSIPATCH, a compact, wearable sensor system; while SENSIPATCH integrates multiple sensing modalities, including bioimpedance, electrochemical, thermal, humidity, and vibrational sensors, this work specifically utilizes its spectrometer module, which comprises multi-wavelength LEDs and photodiodes. Targeting the classification of 100 standardized PANTONE colors, the proposed framework is evaluated under controlled lighting conditions to ensure repeatable spectral acquisition. The experimental design includes both firm and loose contact scenarios to emulate variability in wearable placement. A structured data-preprocessing pipeline involving baseline correction, bootstrapping, and Z-score normalization was employed to enhance signal quality and improve model generalization. Five machine learning models were evaluated: Random Forest, SVM, MLP, CNN, and LSTM. The MLP demonstrated the strongest classification performance. Notably, the MLP achieved consistent accuracy across both contact conditions, indicating robustness against sensor placement variations. These results highlight the feasibility of compact LED-based wearable spectroscopy for reliable color classification under controlled measurement conditions, providing a baseline for future extensions to more diverse lighting conditions. Full article

This study presents an E-nose system for the identification and classification of volatile compounds in traditional Filipino alcoholic beverages, Basi, Bignay, Lambanog, and Tapuy. The system utilizes a gas sensor array composed of MQ3, MQ6, MQ8, MQ135, and MQ136 sensors, and implements a Support Vector Machine (SVM) algorithm with principal component analysis for classification and dimensionality reduction. The experimental process involves three main phases: absorption, data acquisition, and desorption. A total of 225 training samples per class and a total of 20 testing samples were used, evenly distributed among all classes. The SVM model achieved an accuracy of 85%, highlighting its effectiveness in distinguishing between the beverages. This work contributes to the advancement of low-cost, sensor-based solutions for quality control, standardization, and the cultural preservation of traditional Filipino wines. Full article

Anterior cruciate ligament (ACL) injury risk is elevated in female youth football, yet knee joint loading has mainly been studied under controlled laboratory conditions. This limits understanding of how injury risk emerges during realistic match situations. This study provided a field-based kinetic characterization of football-specific movements by estimating knee abduction moments (KAMs) using wearable sensors and machine learning. Fifty-two highly talented female youth players performed agility tasks during training, including structured exercises (F-EX) and game-based play (F-GAME). Full-body kinematics were collected with inertial measurement units, and a validated support vector machine model, trained on synchronized motion capture and force plate data, classified trials as high or low KAM. Across 662 change-in-direction trials, 9–12% were classified as high KAM in both conditions, indicating that potentially high-risk loading regularly occurs during routine actions. High KAM trials showed reduced knee and pelvis flexion, increased hip flexion, and greater pelvis rotation toward the cutting direction, reflecting upright, stiff movement strategies. Performance analyses revealed smaller cut angles in exercises and greater approach acceleration in game play, without differences in peak velocity. These findings demonstrate the feasibility of field-based kinetic screening and support a complex-systems perspective on ACL injury risk. Full article

Tiny Machine Learning (TinyML) enables Machine Learning (ML) models to run on resource-constrained devices, which is critical for Industrial Internet of Things (IIoT) systems requiring low latency, energy efficiency, and local decision-making. Nevertheless, deploying TinyML in IIoT remains challenging due to diverse applications, hardware, frameworks, and deployment methodologies, highlighting the need for a structured and focused review. Existing review articles mainly address general IoT or edge AI, leaving a critical gap in a unified and systematic understanding of TinyML applications, system components, and methodologies within IIoT contexts. Consequently, this systematic literature review (SLR) addresses this gap by analyzing 35 peer-reviewed studies published between 2018 and 2026, offering a comprehensive and structured synthesis of TinyML-enabled IIoT systems. The selected works are synthesized across three major dimensions: applications, system components, and methodologies. In terms of applications, TinyML is primarily used for predictive maintenance, equipment monitoring, anomaly detection, energy management, and general-purpose applications. The general category captures cross-domain solutions that do not fit into a single industrial application. A comparative analysis of all application categories is conducted in terms of accuracy, latency, memory, and energy. For system components, a structured comparison shows how hardware, software, and sensing choices shape performance and applicability. Hardware platforms are grouped by microcontroller families, highlighting dominant types. Software frameworks are summarized, showing the widespread use of lightweight toolchains for on-device inference. Sensor types are categorized, with vibration sensing most common. They are supported by other sensing methods such as vision, sound (acoustic), and environmental sensors. Finally, the methodologies examined in this SLR provide a comprehensive view of the data foundations, model selection, and optimization strategies. In short, this SLR converges diverse TinyML–IIoT applications, microcontroller-based hardware, lightweight software frameworks, sensing modalities, varied datasets, and optimization strategies, while also identifying challenges and future research directions. Full article

Abnormal gait associated with neuromuscular and musculoskeletal disorders represents a growing clinical burden, particularly in aging populations. This study presents a modular, low-cost Smart Rehabilitation Walker (SRW) that integrates multimodal sensing and real-time haptic feedback to enable simultaneous gait monitoring and corrective intervention in both clinical and home environments. The system combines force-sensing resistors for bilateral load symmetry assessment, inertial measurement units for fall detection, and surface electromyography (sEMG) for neuromuscular activity monitoring within a closed-loop assistive feedback architecture. A 15-day pilot study involving ten individuals with rheumatoid arthritis and clinically observed neurological gait abnormalities demonstrated measurable improvements in gait biomechanics. The Force Symmetry Index (FSI), calculated using the Robinson symmetry metric, decreased from an average of 0.9691 to 0.2019, corresponding to a 79.26% average reduction in inter-limb load asymmetry. Concurrently, sEMG measurements showed a substantial increase in neuromuscular activation (ΔEMG = 4.28), with statistical analysis confirming a significant improvement across participants (paired t-test: t(9) = 13.58, p < 0.001). To model rehabilitation trajectories, a nonlinear predictive framework based on Gaussian Process Regression achieved high predictive accuracy (R² ≈ 0.9, with a mean RMSE of 0.0385), while providing uncertainty-aware trend estimation. Validation using an independent amyotrophic lateral sclerosis gait dataset further demonstrated the transferability of the analytical pipeline. These results highlight the potential of sensor-enabled assistive walkers as scalable platforms for quantitative gait rehabilitation, adaptive feedback, and long-term mobility monitoring. Full article

Accurate classification of spindle rotational errors is critical for ensuring machining precision and operational reliability of CNC machine tools. However, existing methods face challenges in extracting discriminative feature information from vibration signals due to small inter-class differences and complex electromechanical interference. This paper proposes a novel deep learning model, MFAFENet, based on multi-sensor collaboration and multi-scale feature information adaptive fusion. Vibration signals from three mounting positions are transformed into time-frequency information representations via Short-time Fourier Transform. The proposed network adaptively fuses multi-scale feature information from parallel branches with different kernel sizes through a branch attention mechanism. An efficient channel attention module is then incorporated to recalibrate channel-wise feature responses. The cross-entropy loss function is employed to optimize the network parameters during training. Experiments on a spindle reliability test bench demonstrate that MFAFENet achieves 93.37% average test accuracy, outperforming other comparative methods. Ablation and comparative studies confirm the effectiveness of each module and the clear advantage of adaptive fusion over fixed-weight multi-scale methods. Multi-sensor fusion further improves accuracy by 7.23% over the best single-sensor setup. The proposed method establishes an effective end-to-end mapping between vibration signals and rotational errors, providing a promising solution for high-precision spindle condition monitoring. Full article

Triboelectric nanogenerators (TENGs) have emerged as versatile self-powered platforms for wearable and implantable biomedical sensing, offering an alternative to battery-dependent electronic devices. By converting biomechanical energy from physiological motion into electrical signals, TENGs enable simultaneous energy harvesting and active sensing within flexible, lightweight, and biocompatible architectures. This review summarizes recent advances from 2020 to 2025 in triboelectric nanogenerator (TENG)-based cardiovascular monitoring. The discussion focuses on material systems, device configurations, sensing mechanisms, and applications including pulse detection and cuffless blood pressure estimation. Representative studies are compared to highlight emerging trends in wearable and self-powered sensing technologies. However, differences in experimental conditions, anatomical sites, calibration methods, and signal-processing approaches limit direct comparison of reported performance. In addition, challenges such as subject-specific calibration, motion artifacts, and limited clinical validation remain. Overall, this review highlights current progress and outlines key challenges for future development and translation of TENG-based cardiovascular monitoring systems. Full article

Climate change exerts growing pressure on the built environment, intensifying urban heat stress, altering microclimatic conditions, and increasing energy demand and health risks. Urban areas, characterized by dense construction and extensive soil sealing, are particularly susceptible to thermal anomalies such as Urban Heat Islands (UHIs), making thermal assessment a crucial element in adaptation and mitigation strategies. This research provides an updated and critical review of methodologies for the thermal evaluation of the built environment, with a focus on remote sensing as an emerging and integrative measurement paradigm. The study presents a comprehensive framework of detection systems, including satellite and aerial remote sensing, ground-based monitoring, and hybrid approaches, complemented by analytical and modeling techniques that combine physical and data-driven methods. A comparative assessment of open-access satellite sensors is carried out, analyzing spatial, spectral, and temporal resolutions and their relevance to urban-scale applications. The integration of remote sensing data with artificial intelligence, machine learning, and cloud-based processing is highlighted as a key advancement for improving interpretative, predictive, and decision-support capabilities. The findings indicate that such integration represents a new frontier for multiscale thermal analysis, supporting resilient urban planning, enhanced energy efficiency, and effective climate change mitigation policies. Full article

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

Emotions are fundamental to the human experience and are increasingly analyzed in applications such as marketing, healthcare, and human–computer interaction. Many recent approaches to human emotion recognition rely on deep learning, which typically demands large labeled datasets and substantial computational resources and often suffers from limited interpretability. Applying classical machine-learning methods to sensor time series is more lightweight but may struggle to reach high accuracy, especially when the temporal structure is not explicitly modelled. This paper introduces three subinterval voting-based ensemble models designed for user-specific emotion classification from multimodal time-series data acquired by smartwatch inertial sensors and heart-rate measurements. Each model partitions a time window into subwindows and performs window-level voting, thereby exploiting the temporal consistency of emotional responses while remaining compatible with standard classifiers such as logistic regression and Random Forests (with or without hyperparameter tuning). The models are evaluated on a public smartwatch emotion benchmark dataset under both binary (happy vs. sad) and three-class (happy, sad, neutral) settings. The relative accuracy improvement over the corresponding baseline reported in prior work ranges from 4.68% to 26.05%, with a mean gain of 12.34%. For the three-class tasks, improvements range from 11.17% to 37.10%, with a mean gain of 21.63%. Within the evaluated experimental setting, these results show that the proposed subinterval ensembles consistently enhance performance while remaining model-agnostic and compatible with standard user-specific classification pipelines in sensor-based emotion recognition. Full article

High-strength bolts are critical structural components that are highly susceptible to corrosion in complex environments, posing significant threats to structural safety and reliability. Although acoustic emission (AE) technology has been widely applied in structural health monitoring, existing studies mainly focus on damage mode identification or source localization, while the identification of corrosion evolution stages based on AE signals remains insufficient. This study develops an intelligent corrosion diagnosis framework for high-strength bolts by integrating multimodal feature fusion and optimized machine learning. AE signals are first collected from the near-end and far-end of bolts using a wireless sensor network and then transformed into time–frequency representations via continuous wavelet transform (CWT). The resulting time–frequency images are fed into a modified ResNet-18 network to extract deep features, while statistical features are simultaneously extracted from the raw signals to preserve global information. These heterogeneous features are subsequently fused to form a comprehensive representation of corrosion characteristics. Furthermore, an artificial protozoa optimizer (APO) is introduced to adaptively optimize the hyperparameters of the XGBoost model. The results demonstrate that AE signals generated by hammering bolts with different corrosion levels can be successfully distinguished. The proposed method achieves high accuracy in corrosion stage classification and outperforms conventional approaches. Even when evaluated on an additional M30 bolt dataset, the proposed method maintains robust performance, demonstrating excellent generalization capability across different bolt sizes. These results demonstrate the practical potential of the proposed method for intelligent bolt corrosion diagnosis. Full article

Kolmogorov–Arnold Networks (KANs) have recently gained increasing attention as an alternative to conventional neural architectures, mainly because they replace fixed activation functions with learnable univariate mappings defined along network edges. This design not only increases modeling flexibility but also makes it easier to interpret how inputs are transformed within the network while maintaining parameter efficiency. KANs are particularly well suited for sensor-driven systems where transparency, robustness, and computational constraints are critical. This study provides a survey of KAN-based approaches for processing sensor data. A literature review conducted from 2024 to 2026 examined the deployment of KAN models in industrial and mechanical sensing, medical and biomedical sensing, and remote sensing and environmental monitoring, utilizing a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)-based methodology. We first revisit the theoretical foundations of KANs and their main architectural variants, including spline-based, polynomial-based, monotonic, and hybrid formulations, to structure the discussion. From a practical standpoint, we then examine how KAN modules are integrated into modern deep learning pipelines, such as convolutional, recurrent, transformer-based, graph-based, and physics-informed architectures. KAN-based models demonstrate comparable predictive performance as conventional machine learning models, while having fewer parameters and more interpretable representations. Several limitations persist, including computational overhead, sensitivity to noisy signals, and resource-constrained device deployment challenges. Real-world sensor systems encounter significant challenges in adopting KAN-based models, including scalability in large-scale sensor networks, integration with hardware architectures, automated model development, resilience to out-of-distribution conditions, and the need for standardized evaluation metrics. Collectively, these observations provide a clearer understanding of the current and potential limitations of KAN-based models, offering practical guidance on the development of interpretable and efficient learning systems for future sensor equipment applications. Full article

Hemorrhagic shock remains a leading cause of preventable death in trauma, yet traditional vital signs may fail to reflect early blood loss before physiological compensatory mechanisms are no longer able to maintain hemodynamic stability. The Compensatory Reserve Measurement (CRM) algorithm offers early detection capability using physiological waveforms but requires testing with emerging wearable sensor technologies for operational deployment. This study tested the Epicore Epidermal Patch for Imperceptible Care (EPIC) wearable healthcare device (WHD) for CRM-based hemodynamic monitoring during progressive central hypovolemia induced by lower-body negative pressure (LBNP) to simulate hemorrhage. Twenty participants underwent progressive LBNP while photoplethysmography (PPG) signals were recorded from EPIC sensors placed at the clavicle and triceps alongside a clinical-grade finger pulse oximeter for reference. Signal quality, heart-rate accuracy, and CRM predictions were evaluated across multiple filtering approaches. The triceps placement achieved signal quality comparable to the pulse oximeter reference when Chebyshev Type II filtering was applied, as well as high heart-rate accuracy. CRM derived from the EPIC sensor placed at the triceps tracked compensatory trends during progressive hypovolemia, but prediction magnitudes were inaccurate compared to calculated CRM values. In contrast, the clavicle placement consistently performed poorly across all measurements, regardless of the signal-processing approach. These findings support the feasibility of soft, flexible wearable sensors for continuous hemorrhage monitoring at the triceps location in operational environments where traditional finger-based pulse oximetry is impractical. Full article

Vegetation carbon stock is a key component of the terrestrial carbon cycle and supports climate-change mitigation and carbon-neutrality strategies. While field inventories provide accurate references, they are constrained by cost and limited scalability, motivating the rapid adoption of remote sensing for large-scale spatial estimation and mapping. However, the literature lacks a consolidated bibliometric and critical synthesis focused on above-ground vegetation carbon stock estimation. Therefore, this review aims to provide a quantitative overview of publication trends, synthesise methodological developments, and identify key research gaps in remote-sensing-based above-ground vegetation carbon stock estimation. A total of 1825 Web of Science records (2015–2024) were retrieved, of which 763 were included for bibliometric mapping using VOSviewer version 1.6.20 and CiteSpace version 6.3.R2, complemented by a critical review of 32 high-quality studies. Results indicate a shift from passive optical and single-index approaches toward active sensing and multi-sensor, multi-platform integration, alongside broad uptake of machine learning and an emerging dominance of deep learning for nonlinear modelling and feature learning. Research attention is expanding beyond forests to non-forest ecosystems, yet challenges persist in spatial resolution, validation data availability, and cross-biome generalizability. This review summarizes methodological trajectories and identifies priorities for robust, transferable above-ground carbon estimation. Full article

Cognitive impairment is a prevalent non-motor manifestation of Parkinson’s disease (PD), yet early detection remains limited by the sensitivity of conventional cognitive assessments. Emerging evidence suggests that motor dysfunction, particularly gait and balance abnormalities, reflects underlying cognitive vulnerability. This study examined motor–cognitive associations and evaluated whether motor-derived features can be used to classify low-MoCA status in PD without direct cognitive testing. Data from 102 individuals with PD were analyzed, incorporating clinical assessments, physical function measures, lifestyle factors, and gait-derived biomarkers. Multiple regression identified Unified Parkinson’s Disease Rating Scale Part III, stride length of the more affected side during 360° turning at preferred speed, and maximum ankle jerk on the less affected side during forward walking as independent predictors of Montreal Cognitive Assessment scores, collectively explaining 34.7% of the variance. Network analysis revealed integrative relationships among global motor severity, gait smoothness, and cognitive performance. Using a compact motor-based feature set, logistic regression achieved a mean accuracy of 65.8% and an AUC of 0.737 in classifying low-MoCA status under cross-validation. These findings demonstrate that motor-derived digital biomarkers capture clinically meaningful information about cognitive status in PD and may serve as adjunctive tools for identifying cognitive vulnerability in clinical settings. Full article