Search Results (1,113)

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

Flexible acceleration sensors demonstrate revolutionary potential in healthcare, structural vibration monitoring, and consumer electronics owing to their unique conformal adhesion capability and mechanical adaptability. However, current academic research presents two distinct paradigms for realizing flexibility: one is the hybridly flexible sensor, which incorporates traditional micro-electro-mechanical System (MEMS) acceleration sensor chips with flexible packaging/substrates; the other is the intrinsically flexible sensor, whose sensing unit and substrate are entirely composed of flexible materials enabled by microstructural design. This review first analyzes the fundamental differences and design challenges between these two flexible architectures. It then systematically elucidates five core sensing mechanisms—capacitive, piezoresistive, triboelectric, piezoelectric, and electromagnetic—comparing their working principles, material systems, structural designs, and performance metrics. Among these, piezoelectric and triboelectric types exhibit distinctive advantages in self-powering capability, whereas resistive and capacitive approaches offer greater ease of integration. Furthermore, the applications of intrinsically flexible acceleration sensors in structural health monitoring, wearable devices, automotive safety, and other fields are discussed, with particular emphasis on their unique strengths in real-time vibration monitoring. Finally, the review summarizes existing challenges, such as the trade-off between sensitivity and flexibility, and provides theoretical insights to guide future innovations in intrinsically flexible acceleration sensor technology. Full article

Owing to the increasing demand for wearable electronics, flexible energy storage devices, such as supercapacitors, have gained interest in the electronic industry. In this context, asymmetric configurations have emerged as a promising strategy for the development of wider potential window supercapacitors. On the other hand, MOF-derived synthesis of transition metal oxides is known to result in porous materials, which exhibit better electrochemical performance. In this work, a MOF-derived Fe₂O₃ coating on carbon fiber woven substrate is proposed as a negative supercapacitor electrode for asymmetric flexible devices. Moreover, the MOF calcination time was evaluated in order to ensure the best electrochemical performance possible, achieving for the sample calcined for 2 h a specific capacitance of 18.8 F/g at a current density of 200 mA/g and an excellent rate capability. In addition, not only was this promising material obtained, but an asymmetric flexible supercapacitor based on two MOF-derived TMO coatings on carbon fiber woven electrodes was manufactured and characterized as a proof of concept. This supercapacitor displayed a specific capacitance of 229 mF/cm², an energy density of 0.067 mWh/cm² and a power density of 0.11 mW/cm² at 0.15 mA/cm². Moreover, the flexible supercapacitor retained 94.1% of its capacitance even after being bent to 90°. Full article

Respiratory rate (RR) is a critical indicator of physiological status, yet unobtrusive and continuous RR monitoring remains challenging, particularly in wearable applications that require soft, lightweight, and low-power sensing systems. This paper presents an integrated approach that combines a textile-embedded embroidered strain-gauge sensor with Tiny Machine Learning (TinyML) to enable real-time, on-device RR estimation. The sensing platform consists of a textile-integrated meander-pattern strain gauge and a fabric-mounted analog readout circuit, which together capture thoracic expansion during breathing. Two lightweight neural network models—a convolutional neural network (CNN) operating on raw respiratory waveforms and a dense neural network (DNN) operating on wavelet features—were developed and trained using a public strain-sensor dataset and a custom dataset collected with the textile system (TexHype dataset). Both models were optimized through 8-bit quantization and deployed to an STM32L4 microcontroller, where end-to-end on-device preprocessing, filtering, segmentation, normalization, and inference were performed. The CNN achieved the highest accuracy, with a mean absolute error (MAE) of 1.23 breaths per minute (BPM) on the TexHype dataset, but exhibited substantial inference latency (5.8–6.2 s) due to its computational complexity. In contrast, the wavelet-based DNN demonstrated lower accuracy (MAE 2.21 BPM) but achieved real-time performance with inference times of 18–96 ms, and a power overhead ($\Delta P=P_{\mathrm{active}}-P_{\mathrm{idle}}$) of approximately 3.3 mW during inference. Cross-dataset testing revealed limited generalization between different strain-sensor platforms. The findings highlight key trade-offs between accuracy, latency, and energy efficiency, and illustrate the potential of combining stretchable electronics with embedded intelligence to enable next-generation wearable respiratory monitoring systems. Full article

Introduction: Childhood and adolescent obesity is a growing global health challenge associated with early metabolic and cardiovascular complications. This study aims to compare questionnaire-assessed physical activity patterns and lifestyle characteristics among children and adolescents with obesity and normal-weight peers and to explore their associations with clinical measurements and cardiometabolic risk. Assessing resting metabolic rate (RMR) by indirect calorimetry may provide additional insight into metabolic status beyond conventional anthropometric indicators. Methods: This prospective cross-sectional study included 58 children and adolescents aged 5–18 years with obesity and 30 normal-weight controls evaluated in Sfânta Maria Emergency Children’s Hospital Iași, Romania. Clinical data included anthropometric measurements and available biochemical parameters. RMR was assessed through indirect calorimetry (Fitmate Pro, Cosmed, Rome, Italy). Parents completed a structured lifestyle questionnaire adapted from validated international instruments, collecting information on physical activity, sedentary behavior, and wearable device use. Data analysis was conducted using SPSS 22.0, applying descriptive statistics and Pearson correlation analysis. Results: Children with obesity reported higher body mass index (BMI) (30.48 ± 5.31 kg/m²), higher RMR values, lower physical activity levels and greater sedentary time than controls. RMR correlated positively with BMI, central adiposity, blood pressure, waist-to-height, hepatic steatosis and exercise tolerance. Although electronic devices for monitoring physical activity were more frequently used in the obesity group, this was not associated with higher activity levels. Conclusions: Children and adolescents with obesity exhibited a clustered cardiometabolic risk profile and reduced physical activity. RMR measured by indirect calorimetry may contribute to a more comprehensive metabolic assessment in pediatric obesity. Full article

This bibliometric study examines the role of ubiquitous computing and intelligent systems in the treatment of mental and neurological disorders. Ubiquitous computing integrates computational intelligence into everyday environments, enabling seamless monitoring and support of patients. Intelligent systems, including wearable devices, environmental sensors, and mobile health applications, collect real-time data on behavior, physiology, and environmental factors. These systems support early detection of symptom changes, adherence to treatment, and crisis prediction through context-aware analysis. Artificial intelligence (AI) processes the collected data to generate personalized therapeutic feedback and notify healthcare providers when intervention is needed. In mental health care, intelligent environments can monitor mood, sleep, and social interaction patterns, providing valuable objective information about mental health status. In the case of neurological conditions such as Parkinson’s disease or epilepsy, intelligent systems facilitate movement tracking, seizure detection, and cognitive assessment outside of the clinical setting. Integration with electronic health records and telemedicine platforms ensures coordinated and responsive care. Ethical design, privacy protection, and patient consent remain key to successful implementation. In this way, ubiquitous computing is transforming care models by increasing autonomy, precision, and continuity in the treatment of complex neurodegenerative diseases, including those related to neurodegeneration in aging. Full article

Advances in implantable and wearable cardiac monitoring technologies have led to widespread detection of brief, often asymptomatic atrial high-rate episodes, frequently labelled as device-detected atrial fibrillation (AF). While detection has increased substantially, the clinical interpretation of these findings remains uncertain. Observational studies demonstrate associations between AF burden and stroke risk but reveal marked inter-individual heterogeneity and no consistent temporal threshold below which risk is eliminated. Recent randomised controlled trials show that anticoagulation guided solely by arrhythmia duration confers limited net clinical benefit, with modest reductions in ischaemic stroke offset by increased bleeding. These findings challenge the biological and clinical validity of rigid time-based thresholds for intervention. Increasing evidence suggests that AF may act primarily as a marker of underlying atrial disease rather than the sole mechanistic cause of thromboembolism. This article provides an evidence-informed perspective on the interpretation of device-detected AF in contemporary clinical practice and argues for a shift away from duration-based triggers toward a longitudinal, risk-adapted approach that integrates AF trajectory, atrial substrate, and clinical context. Emerging tools such as artificial intelligence-enhanced electrocardiography may help identify occult atrial pathology but must augment rather than replace clinical judgement. Proportionate, individualised care should supersede reflexive treatment strategies in the management of device-detected AF. Full article

Objective: The effects of fatigue on swimming propulsion are unclear. This study examined upper-limb propulsive force and bilateral coordination during constant-speed front crawl performed until exhaustion. Methods: Twelve competitive swimmers completed a visually paced front-crawl trial performed at a constant speed (95% of maximal speed) until volitional exhaustion. Upper-limb propulsion (pressure-derived) was quantified using wearable differential-pressure mini-paddles synchronized with high-speed video. Propulsive force and impulse were analyzed at ten standardized time points (10–100% of test duration), distinguishing the early (entry–catch–pull) phase and the push phase of the stroke cycle. Results: Total overall propulsive impulse (time-integral of propulsive force) and mean propulsive force decreased significantly as early as 30–40% of test duration, with the largest reductions occurring during the push phase. Interestingly, push-phase impulse declined earlier in the non-dominant left arm (from 20% of test duration) compared to the dominant right arm (from 40%), whereas force generated during the early phase did not change. Peak propulsive force decreased at later stages, while intra-cycle timing indices (peak timing and force centroid) and inter-limb asymmetry remained unchanged. Stroke frequency increased from mid-test onward and was strongly negatively associated with stroke efficiency (r = −0.79). Stroke efficiency correlated positively with push-phase impulse and peak force. Conclusions: During constant-speed front crawl performed to exhaustion, propulsion progressively declines, primarily through reduced force and impulse during the push phase rather than changes in the early (entry–catch–pull) phase or temporal and asymmetry-related variables. Increased stroke frequency initially compensates for declining propulsion but ultimately fails to maintain the imposed swimming velocity. Full article

Blind and visually impaired individuals face persistent challenges when navigating unfamiliar environments, where unseen obstacles compromise their safety and independence. Although many electronic travel aids have been proposed, most remain impractical for daily use—they often rely on bulky or costly hardware, require external processing, or provide unintuitive feedback. This work presents a wearable stereo-vision-based vibrotactile system for real-time obstacle detection and navigation assistance. The device combines an off-the-shelf stereo camera integrated with a simultaneous localization and mapping framework to perceive spatial geometry and detect obstacles in the user’s path. Two stereo-matching methods were implemented to estimate depth: a block-based algorithm optimized for low-latency performance and a semi-global approach providing denser depth maps. Detected obstacles are translated into distinct vibration patterns delivered through four skin-contact body-mounted actuators encoding both direction and distance. The system was evaluated with blindfolded sighted, visually impaired, and blind participants. Both stereo approaches supported reliable real-time guidance and high obstacle-avoidance rates, demonstrating robust performance on affordable, wearable hardware. These findings confirm the feasibility of real-time tactile guidance using commercially available components, marking a concrete step toward accessible navigation support that enhances safety and autonomy for blind and visually impaired individuals. Full article

Printed electronics have emerged as a versatile manufacturing platform for next-generation biosensors, enabling on-demand and low-cost fabrication of functional devices on flexible, stretchable, and unconventional substrates. One major challenge in this field lies in the sintering of printed features, as conventional high-temperature processing is incompatible with polymeric substrates and thermally sensitive biological components. Low-temperature sintering inks, typically processed below 200 °C or even at room temperature, have become a critical enabling technology for bio-integrated electronics. This review provides an overview of the current state-of-the-art and key challenges associated with low-temperature sintering inks for printed bioelectronics. We discuss inks based on metal nanoparticles, metal–organic decomposition precursors, metal oxides, chalcogenides, and hybrid material systems. The emphasis is on how ink chemistry, ligand selection, and precursor structure govern rheology, stability, and sintering behavior. In addition, key low-temperature sintering and curing strategies, including thermal, photonic, laser, plasma, microwave, and chemical sintering, are compared in terms of energy delivery, densification mechanisms, and substrate compatibility. Finally, we outline emerging directions towards low temperature and room-temperature sintering inks, and sustainable biobased ink formulations, and discuss their applications for wearable, implantable, and soft biosensing platforms. Full article

Artificial intelligence (AI) is rapidly reshaping cardiovascular (CV) medicine, driving a paradigm shift toward truly personalized and data-driven care. This comprehensive review examines the conceptual foundations, clinical applications, and future implications of AI across the CV continuum, spanning prevention, diagnosis, risk stratification, and therapy. Core AI methodologies (including machine learning, deep learning, natural language processing, and computer vision) are discussed in the context of cardiology’s uniquely data-rich environment, encompassing imaging, electrocardiography, electronic health records, wearable devices, and multi-omics data. This systematic review highlights major clinical domains where AI has demonstrated a substantial impact, including CV imaging, ECG interpretation, hypertension and heart failure management, coronary artery disease, acute coronary syndromes, interventional cardiology, and cardiac surgery. AI-driven predictive analytics enable early detection of subclinical disease, improved prognostication, and individualized prevention strategies, while wearable technologies and remote monitoring platforms facilitate continuous, real-world patient surveillance. Emerging applications in pharmacotherapy, drug repurposing, and genomics further reinforce AI’s role in advancing precision cardiology. Equally emphasized are the ethical, legal, and social challenges accompanying AI adoption, such as algorithmic bias, data privacy, cybersecurity, interpretability, and regulatory oversight. Our review underscores the necessity of rigorous clinical validation, transparent model design, and seamless integration into clinical workflows to ensure safety, equity, and physician trust. Ultimately, AI is best positioned as an augmentative tool that complements (but does not replace!) clinical expertise. By fostering hybrid intelligence that integrates human judgment with computational power, AI has the potential to redefine CV care delivery, improve outcomes, and support a more proactive, patient-centered healthcare model. Full article

Flexible X-ray detectors have emerged as a promising technology for portable medical imaging and wearable electronics, yet their manufacturing remains constrained by the competing requirements of device performance, mechanical conformability, and production scalability. Conventional solution-based deposition methods fail to yield high-quality perovskite thick films with uniform morphology, while vacuum evaporation techniques are limited by exorbitant operational costs and low throughput. Herein, we report an optimized solution-processed vacuum evaporation strategy that enables the fabrication of high-quality perovskite films (~1 μm thick) on flexible polyethylene naphthalate (PEN) substrates at a low processing temperature of 100 °C. By incorporating tailored additives into the precursor solution and precisely modulating the vapor-phase conversion kinetics, we achieved significant improvements in film density, crystallinity, and morphological uniformity. Systematic investigations were conducted to elucidate the structure–property relationships across three material systems: pure methylammonium lead iodide (MAPbI₃), halogen-doped methylammonium lead iodide-bromide (MAPb(IBr)₃), and synergistic cation-halogen engineered cesium-methylammonium lead iodide-bromide (CsMAPb(IBr)₃). The optimized flexible PIN-type X-ray detector based on CsMAPb(IBr)₃ exhibited exceptional performance metrics, including a dark current density as low as 5.2 nA cm⁻² and an X-ray sensitivity of up to 1.43 × 10⁴ μC·Gy_air⁻¹·cm⁻². Remarkably, the device retained over 95% of its initial performance after 400 bending cycles with a bending radius of 6 mm, demonstrating outstanding mechanical robustness and operational durability. This work establishes a viable, cost-effective technical route for the scalable production of high-performance flexible X-ray detectors, addressing critical challenges in the advancement of next-generation portable imaging technologies. Full article

Inherited cardiomyopathies associated with high-risk genotypes, are characterized by a disproportionate risk of malignant ventricular arrhythmias and sudden cardiac death, often independent of left ventricular systolic dysfunction or advanced structural remodeling. Traditional surveillance strategies based on intermittent electrocardiography and phenotype-driven risk assessment are insufficient to capture the dynamic and often silent progression of electrical instability in these populations. This narrative review evaluates the emerging role of artificial intelligence (AI)-enabled sensor technologies in remote arrhythmic monitoring of genetically defined cardiomyopathy cohorts. Wearable ECG devices, implantable cardiac monitors, multisensor cardiac implantable electronic device algorithms, pulmonary artery pressure sensors, and contact-free systems enable continuous acquisition of electrophysiological and hemodynamic data, generating digital biomarkers that may reflect early arrhythmic vulnerability and subclinical decompensation. AI-driven analytics enhance signal processing, automated event detection, and remote data triage, with the potential to reduce clinical workload while preserving diagnostic sensitivity. However, current evidence predominantly derives from heterogeneous heart failure or general arrhythmia populations, and prospective validation in genotype-specific cohorts remains limited. Key challenges include algorithm generalizability, signal quality in ambulatory environments, data governance, interpretability of AI models, and integration into structured remote-care pathways. The convergence of genotype-informed risk stratification and multimodal AI-enabled sensing represents a promising strategy to transition from reactive device-based protection to proactive, precision-guided arrhythmic prevention. Dedicated genotype-focused studies and standardized digital endpoints are required to support safe and effective implementation in inherited cardiomyopathies. Full article

To meet the requirements of flexibility and high performance for energy storage devices in flexible wearable electronic equipment, the MnO₂/acetylene black composite flexible cathodes is fabricated via 3D printing technology and the aqueous manganese-based zinc-ion flexible batteries are assembled. Based on bending and torsion mechanical tests, and the electrochemical tests, the optimal 3D printing electrode structure was determined. The micromorphology of the electrode after mechanical tests shows that when the printed lines of the upper and lower layers form a 30° angle, the electrode sheet exhibits the least damage. Electrochemical tests indicated that it had an ohmic resistance of 2.052 Ω, an interfacial charge transfer resistance of 141.1 Ω, a specific capacity of 103 mAh/g at 50 mA/g, and a specific capacity of 65 mAh/g at 500 mA/g. Compared with traditional coated electrodes, the 3D-printed electrode showed significantly improved diffusion coefficient, conductivity, and cycle stability. The assembled 3D-printed flexible battery could stably power a 1.5 V LED bulb under flat, bent, and twisted states. It provides a feasible solution for the development of high-performance flexible energy storage devices. Full article

The increasing density of wireless and wearable electronic devices necessitates the development of lightweight, flexible, and absorption-dominated electromagnetic interference (EMI) shielding materials. In this study, electrospun poly(vinylidene fluoride) (PVDF) composite mats reinforced with carbon nanotubes (CNTs) and graphene nanosheets at low filler loadings (1–3 wt.%) were fabricated and systematically investigated for X-band (8.0–12.5 GHz) EMI shielding performance. Raman, FTIR, and thermal analyses confirm enhanced electroactive β-phase formation and improved thermal stability upon nanofiller incorporation. The formation of interconnected conductive networks within the electrospun fibrous architecture leads to a significant increase in electrical conductivity from 10⁻⁷ S·cm⁻¹ for pure PVDF to 10⁻² S·cm⁻¹ and 10⁻¹ S·cm⁻¹ for CNT/PVDF and Graphene/PVDF composites, respectively, at 3 wt.% loading. Consequently, the total EMI shielding effectiveness (SE_T) increases from 2.5 dB for pure PVDF to 40 dB for CNT/PVDF and 42 dB for graphene/PVDF composites at 3 wt.%. The shielding effectiveness arising from absorption (SE_A) dominates the overall EMI shielding performance, contributing more than 85% of the total shielding effectiveness (SE_T), which clearly indicates an absorption-controlled shielding mechanism. The combination of high absorption-dominated EMI shielding, low filler content, and mechanical flexibility highlights these electrospun CNT/PVDF and graphene/PVDF composites as promising candidates for next-generation flexible, wearable, and biomedical EMI shielding applications. Full article