Search Results (447)

Although ceramic materials exhibit excellent thermal stability, high melting points, and chemical inertness, their intrinsic brittleness restricts their application across various fields. To address this challenge, ceramic fibers possessing the flexibility and functionality demanded by advanced applications have emerged. This review provides an overview of recent progress in ceramic fibers, emphasizing four major spinning techniques, including melt spinning, electrospinning, solution blow spinning, and wet spinning, along with their underlying fabrication mechanisms and process–structure relationships. The fibrous architectures (including aerogels, textiles, and membranes) demonstrate exceptional performance in thermal protection, extreme environment, wave absorption, thermoelectric energy conversion, and wearable electronic textiles and high-temperature catalysis. Despite these advancements, challenges remain in scalable continuous production, long-term stability under realistic service conditions, multifunctional integration, and cost-effective sustainability. This review provides a roadmap for translating laboratory innovations into practical, large-scale deployment in aerospace, energy, and electronic systems. Full article

Wearable electronic systems require compact and sustainable power sources together with reliable motion-sensing functions. This study presents a gear-driven plantar energy harvester that integrates biomechanical energy conversion with self-sensing locomotion recognition. The device converts low-frequency vertical foot loading into rotary motion through a wedge–lever transmission and amplifies the rotational speed using a multistage gear train with a total transmission ratio of 12. A one-way bearing enables directional power transmission during loading and prevents reverse rotation during recovery. The generated voltage serves both as the electrical output and as the sensing signal for locomotion recognition. Human-subject experiments were conducted under six locomotion modes: walking at 1, 2 and 3 m/s; running; ascending; and descending. Voltage signals were sampled at 2000 Hz and segmented into overlapping sequences. A CNN–LSTM model was used to extract local waveform features and temporal dependencies from the nonstationary signals. The model achieved an overall recognition accuracy of 98.8%, with most errors occurring between ascending and descending. The results demonstrate that a single plantar device can simultaneously harvest biomechanical energy and provide motion-related information, offering a compact solution for integrated energy harvesting and self-sensing in wearable systems. Full article

Real-time multi-sensor human activity recognition (HAR) requires accurate models and a system architecture capable of distributing computation, exchanging compact outputs, and maintaining temporal consistency across asynchronous streams. This paper presents a distributed HAR framework in which five wearable sensors are associated with local embedded nodes that perform acquisition, windowing, preprocessing, and convolutional neural network–long short-term memory (CNN–LSTM) inference. Each node transmits a timestamped six-class softmax vector, and a central node applies approximate synchronization and learned probability-level fusion. The framework was evaluated with ten participants whose data were not used for model development. It achieved 95.868% accuracy and a 95.642% macro-F1-score. During continuous operation, the system sustained 47.949 predictions/s, with a mean post-window end-to-end latency of 33.963 ms and a mean synchronization span of 13.788 ms. Relative to complete-window transmission, the numerical payload decreased by 97.69%, and central-node energy per prediction decreased by 52.2% compared with centralized real-time processing. Under 30% independent probability-message loss, accuracy remained at 94.31%. Full article

Long-term functional impairments, including deficits in upper limb mobility, cancer-related fatigue, and reduced physical performance, are often reported by breast cancer survivors, underlining the need for sustainable and easily accessible rehabilitation techniques. Telemonitoring technologies have shown promising results in extending rehabilitation outside of clinical settings. However, the increasing number of technological solutions make it difficult to outline the optimal approach in the comprehensive management of breast cancer patients. Therefore, this narrative review aimed to provide an overview of contemporary digital and sensor-based telemonitoring solutions for breast cancer rehabilitation. To find pertinent research published in recent years, major scientific databases were searched, with emphasis on clinical applications and technology advancements in oncology rehabilitation. The results identified different digital solutions, including wearable sensors, surface electromyography systems, computer vision techniques, virtual reality environments, three-dimensional motion analysis, and mobile health platforms. These technologies have shown promising results in remote delivery of rehabilitation programs, enhancing patient involvement and adherence, and providing objective and ongoing evaluation of functional performance. However, a lack of standardization and a lack of clinical validation in cancer populations are major limitations. Altogether, telemonitoring is a potential strategy for improving rehabilitation pathways in breast cancer survivors, but further studies are required to develop solid standards and enable widespread adoption in cancer rehabilitation clinical practice. Full article

Construction workers face disproportionate rates of fatal and nonfatal injuries, including falls, heat strain, overexertion, and struck-by incidents, that passive personal protective equipment cannot continuously monitor. Smart garments, which embed sensors and microelectronics directly into textile apparel, offer a deployable infrastructure for continuous worker-level physiological and kinematic monitoring. This PRISMA-guided systematic review synthesizes 76 records (search executed 31 December 2025; coverage 2000–2026), of which 63 are empirically validated and used for benchmarking. Four analytical contributions are made: (i) a tiered corpus classification separating construction-direct, transferable, and contextual evidence; (ii) stratification of all performance claims by validation context; (iii) explicit transferability assessment for records from healthcare, occupational safety, sports, and rehabilitation; and (iv) a deployment-maturity framework evaluating readiness across technical performance, validation maturity, operational durability, human and organizational acceptance, and system integration, extending beyond single-dimension scores such as NASA TRL. Only 7 of 63 empirical records (11%) were field or field-simulated, of which 6 reached TRL 5; most other systems remain at TRL 4. The framework suggests that technical performance is less often the primary limiting dimension than validation maturity, durability, and integration, although this conclusion is constrained by the dominance of laboratory and transferable evidence. Full article

Background: Obesity is a chronic, multifactorial disease that demands personalized and sustainable management approaches. Digital health technologies, such as artificial intelligence, wearable devices, mobile health apps, and virtual reality (VR), may support obesity care by providing enhanced behavioral monitoring, personalized feedback, and patient engagement. Objective: This critical narrative review discusses the current evidence on artificial intelligence, wearable technologies, and VR in the context of precision nutrition and obesity management and their possible clinical applications and limitations. Method: A critical narrative review was conducted using peer-reviewed literature published between 2019 and 2026 and identified through PubMed and Google Scholar. Search terms included combinations of “precision nutrition,” “personalized nutrition,” “obesity,” “weight management,” “metabolic health,” “digital health,” “artificial intelligence,” “machine learning,” “mobile health,” “wearable devices,” and “omics” using Boolean operators. Evidence from randomized controlled trials, systematic reviews, meta-analyses, and key conceptual studies was critically synthesized due to substantial heterogeneity in interventions and outcomes. Result: Wearables and mobile applications can enable continuous self-monitoring of physical activity, dietary intake, sleep, and physiological measures. Artificial intelligence may improve dietary personalization, risk prediction, glycemic control, and adaptive feedback. VR offers an immersive way to tackle behavioral and cognitive mechanisms related to overeating such as cravings, food cue reactivity, and inhibitory control. However, the evidence is heterogeneous, with many studies limited by short follow-up periods, small samples, variable adherence, and insufficient clinical validation. Conclusions: Artificial intelligence, wearable technologies, and VR are promising tools for precision obesity management, but their long-term clinical effectiveness remains uncertain. Future research should prioritize adequately powered trials, longer follow-up, standardized outcomes, transparent algorithms, ethical data governance, and integration with multidisciplinary nutrition and obesity care. Full article

The rapid development of wearable electronics has created a demand for flexible, lightweight, and sustainable power-supply technologies. The persistent temperature difference between the human body and the environment provides a low-grade thermal source for thermoelectric energy harvesting. However, traditional flexible thermoelectric devices still face limited self-supporting capabilities and difficulties in maintaining sufficiently low cold-side temperatures. Here, we designed a passively radiative-cooled thermoelectric film (PRT film) by integrating a PAM/PEDOT:PSS self-supporting thermoelectric composite layer with a polymer metamaterial radiative cooling (PMRC) film. The PAM/PEDOT:PSS layer serves as a self-supporting thermoelectric conversion component for harvesting low-grade heat, while the PMRC film layer acts as a passive cold-side regulator without energy input to lower the cold-side temperature and enhance the temperature gradient. By optimizing the PAM content, the PAM/PEDOT:PSS composite material with 85 wt% PAM achieved the highest power factor of 72.3 μW m⁻¹ K⁻². Under a temperature difference of 39 °C, the optimized PAM/PEDOT:PSS sample provided an open-circuit voltage of 0.47 V, a maximum output power of 1.1 μW, and a power density of 11.2 μW cm⁻². According to the temperature-difference enhancement measured in experiments and the independently obtained load characteristics, the integration of PMRC films is expected to increase the maximum output power from 1.1 to 1.4 μW, with the corresponding power density rising from 11.2 to 14.25 μW cm⁻², representing a 27.2% enhancement. This work demonstrates the feasibility of passive radiative cold-side regulation in enhancing low-level thermoelectric energy harvesting for wearable applications. Full article

Developing sustainable and scalable personal thermal management textiles that simultaneously provide radiative cooling, moisture comfort, and responsible end-of-life management remains challenging. Here, we report a sustainable, scalable, and recyclable bamboo dissolving pulp-derived cellulose/SiO₂ fiber (CSF), fabricated by a wet-spinning process involving the dissolution and regeneration of cellulose and nano-SiO₂. The resultant CSF exhibits a hierarchical interface-pore structure, which enhances solar scattering (up to 94.56% in 0.4–1.0 μm) by Mie scattering of nano-SiO₂ particles and multiple scattering at micro- and nanopore-induced air/cellulose/SiO₂ interfaces. By coupling high mid-infrared emissivity of 94.8% (8–13 μm), the CSF demonstrates average daytime sub-ambient cooling of 9.5 °C under hot and humid summer conditions. More importantly, the CSF presents a multiscale water-transport network that integrates molecular water capture (–OH groups), capillary infiltration (nanoscale interfaces between nano-SiO₂ and cellulose), and liquid spreading and evaporation (interconnected microchannels between fibers), which realizes larger liquid diffusion area and water-vapor transmission rate (7.55 cm² and 175.48 g m⁻² 24 h⁻¹), compared to commercial cotton and polyester. In addition, the CSF demonstrates desirable soil-biodegradation capability, while the feasibility of closed-loop reuse is demonstrated through a single recycling cycle, supporting environmentally friendly wearable cooling textiles. The wet-spinning strategy paves the way for the construction of sustainable, scalable and recyclable fiber for thermal- and moisture-comfort textiles. Full article

Conductive hydrogels combine tissue conformability, conductivity, and biocompatibility, making them an emerging class of materials for soft and wearable bioelectronics applications. This review comprehensively presents the latest developments in studies of conductive hydrogels for wearable sensing applications, including materials chemistry, mass and charge transport physics, and device engineering. Starting with an examination of the main materials used, such as conducting-polymer networks, nanocomposites, and ionic hydrogels, we analyze how molecular network design, hydration state, and conducting-phase organization jointly determine the electromechanical performance and durability of devices. We discuss how microscopic mechanisms related to mechanical behavior, transport phenomena under deformation, and the influence of viscoelasticity on conductivity as a function of hydration degree are linked to the macroscopic response of devices. The review discusses how strain, pressure, temperature, and multimodal sensing are detected as a function of the device architecture and its transduction mechanisms. Aspects related to interface engineering, skin adhesion, long-term reliability, and the development of metrological frameworks necessary for comparison between different studies in the literature are explored. The latest developments in sustainability, biodegradability, and AI-assisted materials design are also presented. By integrating these analyses, the review suggests design principles and performance maps that can be used for the development of next-generation CH-based wearable devices that optimize mechanical softness, multimodal sensing capability, and environmental durability. Full article

Arterial hypertension in adolescents is increasingly a lifestyle-driven disorder, and a growing share of cardiovascular risk in this age group is hidden from conventional office measurement. Intensive nighttime screen use, together with the chronic sleep loss that accompanies it, may disrupt circadian control of the cardiovascular system, an effect we designate, as a proposed integrative concept, digital chronotoxicity. Through melatonin suppression, sustained sympathetic and neuroendocrine activation, oxidative stress, and metabolic dysregulation, this exposure is hypothesized to attenuate the physiological nocturnal fall in blood pressure and to contribute to the non-dipper phenotype, a predictor of early vascular aging that remains invisible to office readings. This narrative review traces the mechanistic pathway from screen exposure to the loss of nocturnal dipping and reframes the problem as a diagnostic one. Twenty-four-hour ambulatory blood pressure monitoring is positioned as the reference standard for detecting the at-risk phenotype and masked hypertension; the cardiac, vascular, renal, autonomic, and neurocognitive biomarkers of early hypertension-mediated organ damage are reviewed; and emerging wearable and artificial-intelligence tools for continuous, preventive screening are examined. An integrated screening pathway is proposed, on the premise that the evaluation of a hypertensive adolescent is incomplete without a digital and sleep history alongside ambulatory monitoring. Reframed in this way, pediatric hypertension becomes a detectable chronobiological disease. Full article

Background: Physical activity plays a vital role in caring for people with multiple sclerosis (MS). Yet, challenges like fatigue, mobility impairment, fall risk, symptom fluctuation, and poor access to rehabilitation often make sustained participation difficult. Adhering to physical activity programmes is tough. Artificial intelligence (AI) may transform wearable, smartphone, clinical, and patient-reported data into interpretable information for monitoring, prediction and individualized support. Objective: Building on the identified need for individualized support, this scoping review mapped original AI-based studies relevant to physical activity, gait, mobility, ambulation, fatigue, fall risk, and real-world functioning in people with MS. Methods: The review followed PRISMA-ScR principles. Search covered records published from 1 January 2016 to 14 May 2026 across biomedical, rehabilitation, technology-oriented, trial registry, and citation-searching sources. Eligible studies included MS or MS-specific data, an explicit AI/ML or model-derived predictive analytics component, and an activity-related clinical domain. Results: The search identified 332 records/reports. After removing 127 duplicates, 205 records were screened, 45 full texts were assessed, and 21 original AI studies met the eligibility criteria. These addressed inertial sensor deep learning, wearable gait speed estimation, postural sway fall risk prediction, connected device prediction of fatigue and health state, smartphone-based ambulation characterization, treadmill or walkway gait classification, chair stand fall status prediction, daily life gait/turning fall prediction, fear-of-falling detection, sensor-derived fatigue prediction, mobile/wearable modelling of MS, and physical activity prediction. Conclusions: AI can extract clinically relevant patterns from gait, wearable, smartphone, connected device, clinical, and patient-reported data in MS. However, evidence does not yet show that AI-supported systems improve physical activity, adherence, fatigue burden, fall risk, quality of life, or functional independence. AI is therefore promising but early, and prospective, externally validated, human-supervised, fatigue-aware, safety-sensitive studies are needed to support clinically meaningful, patient-centred MS rehabilitation and activity planning. Full article

Portable biosensor hardware can now sustain continuous multimodal physiological acquisition at the edge, yet the analytical layer that converts raw signals into deployment-consistent inference remains the main bottleneck for practical embedded systems. This study addresses that bottleneck by presenting the machine-learning layer of the Real-time Cognitive Grid, the analytical companion to the previously reported hardware architecture, which equips a fixed-wiring biosensor assembly with real-time physiological-state classification through an asymmetric edge–cloud workflow. The proposed framework assigns analytical responsibility across tiers: a locked 17-feature schema comprising 5 EMG features, 6 EEG spectral features, 2 cross-modal features, 2 HRV features, 1 EOG feature, and 1 EEG quality indicator governs window-bounded inference on the Arduino Nano RP2040 Connect with an LDA edge artefact requiring approximately 716 B RAM, whereas the cloud tier supports public-dataset pretraining, hardware-aligned refinement, multimodal fusion, deployment comparison, and feature-importance analysis under the same schema contract. To evaluate analytical consistency across physiological diversity, five public repositories covering stress physiology (WESAD), affective EEG (DEAP), inertial activity recognition (PAMAP2), sEMG gesture decoding (EMG Gestures), and motor-imagery EEG (EEGMMIDB) were evaluated under subject-disjoint GroupKFold (k = 5) protocols. To test whether the same contract survives translation to the physical rig, the hardware branch was evaluated under session-disjoint GroupKFold across five bench-acquired sessions. Unimodal performance was strongest in sEMG- and IMU-dominant tasks, whereas multimodal fusion improved macro-F1 by up to 0.141 over the strongest unimodal baseline in WESAD and by 0.109 in PAMAP2. In the hardware branch, the deployed edge LDA artefact reached 0.9435 macro-F1 with 0.9470 accuracy, while the retained cloud Random Forest reached 0.8792 macro-F1 with 0.8799 accuracy; feature-importance analysis further showed that the final 17-feature branch was dominated by EMG descriptors, with EEG spectral terms contributing secondary support and hardware-exclusive variables remaining weak under the present bench regime. These results show that a compact multimodal sensing assembly can be elevated beyond passive signal capture into an intelligent portable biosensor that performs context-aware interpretation with minimal user intervention, supported by a reproducible analytical workflow that remains coherent across heterogeneous benchmark repositories, hardware-specific refinement, and microcontroller-class deployment, thereby establishing cross-session bench feasibility as a structured basis for future multi-subject wearable validation. Full article

Soft actuators made from soft organic materials that can exhibit biomimetic motions, such as artificial muscles, have recently attracted substantial interest for applications in soft robotics, wearable electronics, and haptic interfaces, where flexible, adaptive, and biocompatible actuation is essential. In this context, piezoelectric and electroactive materials have emerged as important platforms for electromechanical transduction; however, conventional piezoelectric materials are predominantly ceramic-based, making them brittle, limiting achievable strain, and often requiring high operating voltages. Ionic liquids (ILs) have emerged as promising alternatives due to their high ionic conductivity, negligible volatility, and wide thermal and electrochemical stability windows. Notably, recent reports of piezoelectric behavior in ionic liquids, representing the first observation of such effects in liquid systems, have opened new opportunities for IL-based soft actuators. These advances highlight the potential of IL-based materials for developing next-generation soft robotic systems with enhanced functionality. Accordingly, there is a growing interest in designing sustainable actuators that integrate self-healing, self-powering, and self-actuating capabilities while maintaining efficient energy use, long-term stability, and user-specific adaptability. This review summarizes recent progress in IL-based soft actuators, including material design, actuation mechanisms, sensing integration, and control strategies, and also discusses current challenges and future research directions in this emerging field. Full article

Portable sensing systems are increasingly important in biological and chemical analyses because they can provide analytical information at the point of decision-making. While traditional laboratory methods remain crucial for reference measurements, regulatory validation, and high-precision quantification, portable systems emphasize rapid response, convenience, cost-effectiveness, robustness, and relevance to decision-making. This paper views portable sensing systems as integrated analytical platforms rather than isolated sensing elements. The paper discusses recognition elements, including enzymes, antibodies, nucleic acid probes, aptamers, molecularly imprinted polymers, nanomaterials, and hybrid recognition interfaces, as well as electrochemical, optical, mass-sensitive, thermal, field-effect, and hybrid sensing technologies. Furthermore, this paper reviews platform designs, including paper-based analytical devices, chip lab systems, smartphone-assisted sensors, wearable and flexible sensors, handheld instruments, and wireless sensor networks. It explores their applications in sample handling, calibration, data processing, and field deployment. Applications of this technology include point-of-care diagnostics, pathogen detection, wearable health monitoring, agriculture, veterinary medicine, environmental monitoring, food safety, industrial process control, forensic analysis, public safety, and occupational exposure assessment. The report focuses on sample acquisition, miniaturized preparation, reagent storage, matrix interference, calibration transfer, signal conditioning, machine learning, cloud platforms, analytical validation, and decision support. Furthermore, it identifies key obstacles to translating academic prototypes into industrial products, including reproducibility, stability, manufacturability, ease of use, cybersecurity, regulatory approval, and market acceptance. Future development requires fully integrated sample-to-result systems, multimodal sensing, artificial intelligence, sustainable single-use materials, self-powered devices, and system-level validation under real-world operating conditions. Full article

Wearable biosensors have swiftly progressed from stiff laboratory prototypes to flexible, skin-like systems capable of ongoing physiological monitoring. Yet, the active and mechanically intense nature of athletic performance reveals the limits of static device designs made solely through traditional 3D printing. This review offers a thorough analysis of the shift from custom 3D-printed platforms to adaptive 4D-printed wearable biosensors that include time-sensitive, stimuli-responsive materials. We carefully investigate how nanomaterial-engineered transducers, including carbon nanomaterials, MXenes, and metallic nanostructures, improve electrochemical sensitivity, signal stability, and mechanical durability in sweat-based and electrophysiological sensing. Additionally, we examine the integration of thermoresponsive polymers, moisture-activated hydrogels, shape-memory materials, and self-healing networks that support autonomous control of skin–sensor contact, microfluidic sweat management, and structural stability under high mechanical strain. Case studies focused on sports monitoring demonstrate how these innovations enable multimodal measurement of mechanical, chemical, and molecular biomarkers in real time. Lastly, we address manufacturing scalability, regulatory issues, and translational challenges necessary to move from proof-of-concept to clinical deployment. By combining nanomaterial-driven electrochemical precision with 4D-printed mechanical adaptability, this review maps a path toward adaptive, self-regulating wearable platforms that sustain analytical accuracy even under extreme physiological demands. Full article