Search Results (309)

Accurate non-contact human height estimation is vital for large-scale growth monitoring in schools but remains challenging for monocular RGB sensors due to scale ambiguity and keypoint jitter. This study proposes a robust temporal SegPose framework for high-precision height measurement in unconstrained outdoor playground environments. We develop a multi-task deep learning model using a MobileNetV4 backbone and a novel Height-Aware Boundary Refinement (HABR) module, which utilizes nose-spatial priors to refine cranial vertex localization. To resolve scale issues, a three-way orthogonal calibration system is established using existing playground marking lines and goalposts to dynamically estimate ground plane metric factors. A linear Kalman filter is integrated to smooth keypoint trajectories, suppressing gait-induced oscillations and reducing high-frequency jitter by 64.92%. Validated on a dataset of 95 volunteers (53 males, 42 females) at distances of 6–12 m, the proposed system achieves a mean absolute error (MAE) of 1.42 cm and a mean absolute percentage error (MAPE) of 0.84%, significantly outperforming recent Transformer-based state-of-the-art methods. The framework operates at 42.7 FPS, ensuring real-time performance while adhering to a privacy-preserving protocol that decouples biometric records from individual identities. These results demonstrate that our framework effectively overcomes boundary ambiguity and distance-dependent resolution loss, providing a reliable, efficient, and ethical solution for automated physical health assessments in educational settings. Full article

Population aging has increased the need for continuous, non-invasive, and context-aware monitoring systems capable of supporting autonomy, safety, and early intervention in daily living environments. Multimodal biometric sensing offers an important technical basis for this purpose, as it combines physiological, motion-related, and environmental signals to provide a more complete view of older adults’ functional and health-related conditions. However, many existing solutions remain fragmented, device-dependent, and insufficiently connected to core instrumentation requirements, including signal quality, sensor calibration, temporal synchronization, latency, energy consumption, interoperability, reliability, and data privacy. This article proposes an Edge-AI instrumentation framework for multimodal biometric sensing in active aging environments, supported by a structured analysis of recent literature on wearable, ambient, and context-aware sensing systems. The framework integrates wearable, ambient, and context-aware sensors with local processing capabilities to support signal acquisition, preprocessing, quality control, feature extraction, anomaly detection, and decision support close to the data source. By placing Edge AI within the instrumentation pipeline, the proposed framework identifies design requirements that may help reduce response time, limit unnecessary transmission of sensitive biometric data, and improve feasibility in home-based and assisted-living contexts. These expected benefits, however, require empirical testing through future prototype implementation and real-world evaluation. The article also defines a validation-oriented perspective for sensor-based active aging systems, covering technical, operational, and human-centered dimensions such as measurement accuracy, signal robustness, usability, privacy preservation, interoperability, reproducibility, energy efficiency, and system scalability. The proposed framework is intended to support the design, comparison, and validation of more reliable, interpretable, and reproducible sensor-based monitoring systems, while offering a structured basis for prototype development and future real-world evaluation in active aging environments. Full article

Electroencephalogram (EEG)-based biometric sensing provides a promising pathway for secure and user-specific human authentication, but practical deployment remains limited by cross-session non-stationarity and potentially optimistic evaluation protocols caused by window- or event-level leakage. This study developed a cross-session EEG biometric sensing framework using interpretable spectral features and target-session calibration, with trial-level non-overlapping data partitioning to prevent overlap between training, calibration, and blind-test sets. Multi-channel EEG events were segmented into non-overlapping 2 s windows and aggregated into event-level samples to ensure strict isolation among training, validation, calibration, and blind testing subsets. Power spectral density, differential entropy, and log-variance features were evaluated using support vector machine, linear discriminant analysis, logistic regression, random forest, and a compact convolutional neural network designed for EEG decoding (EEGNet). Both closed-set identification and biometric verification were assessed using Rank-N accuracy, cumulative match characteristic curves, macro-F1, equal error rate, area under the curve, and true acceptance rate (TAR) at fixed false acceptance rate (FAR) levels. In the in-house cross-day dataset, limited target-session calibration improved Rank-1 accuracy from 44.82% to 98.70% for support vector machine (SVM) and from 19.93% to 99.94% for linear discriminant analysis (LDA). SVM with differential entropy achieved an equal error rate (EER) of 1.04 ± 0.60%, area under the receiver operating characteristic curve (AUC) of 0.9985 ± 0.0008, and TAR@FAR = 0.1% of 98.44 ± 1.06%. External validation on the public multi-session motor imagery dataset confirmed the calibration benefit. These results demonstrate strong closed-set identification and score-level verification performance under a cohort-wide, calibration-assisted cross-session experimental protocol. Full article

Human gait datasets play a central role in the development and evaluation of computer vision models. However, the current dataset landscape remains highly heterogeneous, with inconsistent reporting of acquisition conditions, user variability, and sensing configurations, which limits reproducibility and hinders principled cross-dataset comparability. In this work, we propose a covariate-centered, modality-agnostic taxonomy for gait datasets, explicitly structuring variability across scene-level, user-level, and sensor-level factors. The proposed framework enables consistent characterization of datasets through a standardized set of covariates (A–R), bridging differences across application domains and sensing modalities. Following a systematic review protocol aligned with PRISMA 2020, we analyze 47 publicly available image- and depth-based human gait datasets spanning healthcare, biometric, and attribute-recognition application domains. Using the proposed taxonomy, we derive a quantitative analysis of covariate coverage, revealing systematic biases in current dataset design. Full article

In healthcare facilities, patient wayfinding challenges—especially in complex, multi-unit or campus-scale healthcare environments—extend the patient journey and increase front-desk workload. This work investigates the integration of a Digital Twin-orchestrated, IoT-enabled architecture that links digital scheduling to in-clinic guidance through standardized tokens. The proposed approach relies on (i) an administrative mapping layer that binds unique QR identifiers to cabinets, specialties, clinicians, and human-readable location labels, (ii) an appointment confirmation workflow that issues a confirmation code and delivers an e-mail package including a QR token and an RFC 5545-compliant (Internet Calendaring and Scheduling Core Object Specification) attachment, and (iii) a kiosk-like model (embedded, single-board computer with camera-based QR scanning) that resolves tokens via REST endpoints and presents deterministic guidance using a finite-state-machine workflow with explicit fallback from appointment resolution to cabinet-level QR mapping. An optional biometric module is included for recurrent visits via a database linkage layer, enforced by a single-owner rule for the serial sensor interface to prevent concurrency faults. Scenario-based validation confirms end-to-end operability and robustness, with time–motion quantification scheduled for future on-site evaluation. Full article

Smart wearable biomedical sensors/devices provide the ability for continuous monitoring of physiological parameters in occupational settings. Industrial Internet of Things (IIoT)’s integration in wearable biomedical technology presents additional cybersecurity risks and privacy concerns as attack surfaces expand and sensitive biometric data is processed. This scoping review included studies published between 2020 and 2026, screened according to PRISMA-ScR guidelines, which identified 39 articles. The findings indicate that most systems rely on commercial wearables for monitoring fatigue, stress, and environmental conditions, often with limited consideration of the cybersecurity aspects. Common risks include insecure wireless communication, data interception, firmware and supply chain attacks, and vulnerabilities related to IT/OT integration. To address these challenges, this study proposes a multi-layer threat taxonomy covering sensing, communication, processing, AI/analytics, and organizational layers. The results highlight the need for end-to-end cyber-resilient architectures and privacy-by-design approaches in occupational wearable monitoring systems. Full article

The exponential growth of Internet of Things (IoT) ecosystems is driving a paradigm shift from centralized cloud computing towards decentralized architectures to mitigate latency and bandwidth constraints. While edge computing addresses some of these challenges, data transmission to local gateways still raises critical security and privacy concerns. This study explores the Compute Continuum by pushing intelligence to the extreme edge using TinyML. We propose a secure, privacy-preserving multimodal biometric authentication system designed for resource-constrained embedded devices. Our solution implements a hierarchical processing chain: an ultra-lightweight person-detection filter acts as an intelligent wake-up mechanism, followed by robust facial and voice authentication modules. Operating as a strict hierarchical pipeline, the system achieves a combined False Acceptance Rate (FAR) of just 0.12%. Experimental results on an ESP32 microcontroller demonstrate exceptional energy efficiency, requiring only 0.15 J per inference cycle. This allows the system to operate autonomously for over 39 h of continuous inference on a standard 600 mAh battery, proving the viability of standalone, privacy-by-design biometric sensors in intelligent IoT environments. Full article

Deepfakes threaten sensor-based authentication systems, including biometric sensors, surveillance cameras, and IoT edge devices. Unimodal detectors remain vulnerable to modality-specific attacks. We propose a multimodal deepfake detection framework optimized for resource-constrained edge devices, featuring a novel cross-modal attention fusion mechanism with adaptive gating. The architecture combines enhanced Res2Net for audio, temporal 3D CNN with SE attention for video, and bidirectional cross-modal attention with quality-based gates. On our benchmark (5472 audio + 1842 video samples), the fusion model achieves 96.7% accuracy, 96.6% F1-score, 0.988 AUC-ROC, and 3.3% EER. Adversarial testing shows 92.3% accuracy under the Fast Gradient Sign Method (FGSM) attack. The model has a 30.3 MB footprint and runs at 20 FPS on edge hardware. Modality contribution analysis reveals adaptive weighting (72% audio for TTS forgery, 78% video for lip-synced attacks). Cross-dataset evaluation on FakeAVCeleb achieves 92.3% overall accuracy, confirming generalization. Full article

The timely detection of psycho-emotional risks has become increasingly important due to the rapid growth of social media platforms. This study examines user-generated text as a potential source of early indicators of psychological vulnerability. The proposed NLP-based framework incorporates behavioral features to improve the interpretation of users’ psycho-emotional states. In addition to text classification, the study considers structured behavioral indicators to support psycho-emotional risk analysis. Particular attention is given to interpretability. SHAP-based techniques are applied to reveal the contribution of individual features and to provide a clearer explanation of model predictions. The evaluation was conducted on publicly available datasets containing textual data and aggregated behavioral/physiological indicators. No raw physiological streams, wearable sensor data, or biometric recordings were used. The two datasets were employed in complementary experimental settings and were not aligned at the individual-sample level; accordingly, the broader analytical perspective explored in this study should not be interpreted as a single end-to-end or fully aligned multimodal learning framework. The proposed BERT-based model with SHAP interpretability achieved an accuracy of 96.3%, an F1-score of 0.96, and a ROC–AUC score of 0.98, showing consistent improvement over baseline models, including Random Forests and Support Vector Machines. Full article

Field-programmable gate arrays (FPGAs) are increasingly used to bring biometric recognition from cloud- or GPU-centric deployments to resource-constrained edge devices where latency, power, and privacy are critical. This paper surveys recent (2021–2025) FPGA and FPGA-SoC implementations across five widely deployed modalities: face, fingerprint, iris, speaker (voiceprint), and finger vein. For each modality, we summarize representative implementations and the performance figures commonly reported in the literature (e.g., accuracy or EER, latency/throughput, resource usage, and power), highlighting the algorithm–hardware co-design choices that enable real-time operation. Across modalities, successful designs repeatedly employ streaming/dataflow architectures, aggressive quantization and fixed-point arithmetic, reuse-aware buffering, and heterogeneous CPU–FPGA partitioning, often supported by high-level synthesis and vendor deep learning IP. Beyond throughput, we discuss how FPGAs facilitate privacy-preserving on-device processing and can integrate template protection and presentation attack detection within the same fabric. Finally, we identify open challenges related to scalability to larger models, memory-bandwidth constraints, and design productivity, and outline research directions enabled by emerging adaptive FPGA architectures and more automated toolflows. Overall, the surveyed evidence indicates that FPGAs are a compelling platform for deterministic, energy-efficient, and secure biometric inference at the sensor edge. Full article

Wearable sensors and remote sensing technologies are rapidly increasing opportunities to measure grazing animal behavior, energetics, and performance in extensive rangeland systems. However, despite significant advances in device capabilities, the livestock sector lacks an ecological framework that connects sensor data to the metabolic processes driving animal growth and efficiency. In this paper, we apply the movement ecology paradigm to grazing beef cattle as a demonstration of how metabolic theory, animal behavior, and landscape heterogeneity interact to influence energy budgets. We first describe the mechanistic relationships among basal metabolism, thermoregulation, activity, and forage intake, highlighting how movement patterns reflect underlying metabolic states. Next, we review key variables measurable through modern sensors, including GPS, accelerometers, rumen temperature boluses, and remote sensing of forage quantity and quality and explain how these data can be integrated into an information system to estimate energy expenditure, resource selection, and physiological stress. Finally, we show how combining movement, behavioral, and landscape data can yield meaningful indicators of performance and health, paving the way for precision livestock management grounded in ecological principles. Integrating metabolic and movement ecology with emerging technologies offers a strong framework for enhancing efficiency, welfare, and sustainability in grazing beef systems. Full article

Biometric sensing systems enable accurate identity recognition using unique physiological traits. These systems can be unimodal (single trait) or multimodal (multiple traits, such as iris and fingerprint). Biometric templates, digital representations of these traits, enhance security over traditional methods but are vulnerable to attacks. Unlike passwords, compromised templates cannot be replaced, necessitating robust protection. Various security schemes exist, including cancellable biometrics, biometric cryptosystems, sensing technology, and biometrics in the encrypted domain. Cancellable biometrics apply transformations, such as biometric salting, to obscure the original data. Biometric cryptosystems integrate cryptographic techniques, including key generation and key binding, to enhance security. Biometrics in the encrypted domain, such as homomorphic encryption, ensures data remains encrypted during storage and computation. This survey focuses on the fuzzy vault method, a key-binding biometric cryptosystem. It analyses its applications, security performance, and associated challenges across different domains. By analysing advancements in fuzzy vault mechanisms, this paper provides insights into enhancing sensor-based biometric security. The study aims to serve as a reference for researchers exploring secure and efficient biometric authentication methods, ensuring robust protection against unauthorised access while maintaining the integrity and usability of biometric data in real-world applications. Full article

Biometrics technology is undergoing a paradigm shift from static single-modal authentication to continuous multimodal sensing, combined with higher-performing algorithms powered by new deep learning techniques. This editorial reviews cutting-edge advancements and trends in the field of biometrics in four dimensions—novel sensors, modalities, algorithms, and equipment—as well as summarizes the contributions to this Special Issue, “New Trends in Biometric Sensing and Information Processing” by grouping them into the corresponding aspects of breakthroughs in this field. Full article

Three-Dimensional (3D) face reconstruction from monocular Red-Green-Blue (RGB) imagery remains a fundamental yet ill-posed challenge in computer vision, with applications in biometrics, augmented reality/virtual reality (AR/VR), and intelligent visual sensing systems. While deep learning has significantly improved reconstruction fidelity and realism, existing surveys primarily focus on network architectures in isolation, often overlooking how sensing conditions, data acquisition protocols, and geometric calibration influence reconstruction reliability and evaluation outcomes. This paper presents a sensor-aware, end-to-end review of deep learning-based 3D face reconstruction and introduces a unified modular framework that connects sensing hardware, data acquisition, calibration, representation learning, and geometric refinement within a coherent pipeline. The reconstruction process is organized into four stages: sensor-driven acquisition and calibration, landmark estimation and feature extraction, 3D representation and parameter regression, and iterative refinement via differentiable rendering. Within this framework, we examine how sensor characteristics, calibration accuracy, representation models, and supervision strategies affect reconstruction accuracy, perceptual quality, robustness, and computational efficiency. We further synthesize the reported results across widely used benchmarks using both geometric and perceptual metrics, highlighting trade-offs between reconstruction fidelity and deployment constraints. By integrating sensing-aware analysis with architectural evaluation, this survey provides practical insights for developing scalable and reliable 3D face reconstruction systems under real-world conditions. Full article

Background/Objectives: Wearable technologies become increasingly important in surveillance of biometric parameters in pregnant women; however, early-stage usability data on wearable form factors specifically designed for pregnant women remain limited. This study evaluated the usability and acceptance of three non-functional wearable garment prototypes intended for future breathing exercise guidance and sleep-related applications. The prototypes incorporated sensor dummies that were technically capable of operation but intentionally deactivated for this usability pilot study. Methods: Eighteen pregnant women (second and third trimester) and twelve non-pregnant women tested three prototypes (Bra, Strap, Maternity Belt (hereafter Belt)) for 24 h. Usability was assessed using structured, participant-completed questionnaires addressing fit, material properties, comfort, and wear-related issues immediately after fitting (T0) and after 24 h of wear (T24). Analyses were descriptive and exploratory. Results: Among pregnant women, the Bra prototype showed consistently favorable usability ratings across multiple domains, particularly after extended wear, whereas the Belt demonstrated declining ratings related to fit and comfort over time. The Strap showed intermediate usability with specific strengths related to pressure and friction. In non-pregnant women, usability ratings were largely comparable between the Bra and Strap, with no clear preference pattern. No systematic differences were observed between pregnant and non-pregnant groups. Conclusions: This exploratory usability study suggests that garment form factor plays a critical role in acceptability during pregnancy. The Bra prototype demonstrated the most favorable usability profile among pregnant women, while the Belt revealed design limitations that warrant further modification. These findings provide formative guidance for the development of functional maternal wearables, with future studies integrating objective testing and validated measures to optimize performance and evaluate adherence in larger cohorts. Full article