Search Results (5,259)

Sleep disorders, such as insomnia, obstructive sleep apnea (OSA), narcolepsy, REM sleep behavior disorder, and circadian rhythm disturbances, represent a rapidly expanding global health burden that is strongly associated with cardiovascular, metabolic, neurological, and psychiatric diseases. Advancements in wearable sensing technologies and Internet of Medical Things (IoMT) infrastructures have expanded the possibilities for continuous, home-based sleep assessment beyond conventional polysomnography laboratories. These Sleep Health Internet of Things (S-HIoT) systems combine multimodal physiological sensing (EEG, ECG, SpO₂, respiratory effort and actigraphy) with wireless communication and cloud-based analytics for automated sleep-stage classification and disorder detection. Nonetheless, the digitization of sleep medicine brings about significant cybersecurity concerns. The constant transmission of sensitive biomedical information makes S-HIoT networks open to anomalous traffic flows, signal manipulation, replay attacks, spoofing, and data integrity violation. Existing studies mostly focus on analyzing physiological signals and network intrusion detection independently, resulting in a systemic vulnerability of cyber–physical sleep monitoring ecosystems. With the aim of addressing this empirical deficiency, this review integrates emerging advances (2022–2026) in the AI-assisted categorization of sleep phases and IoMT anomaly detector designs on the finer analysis of CNN, LSTM/BiLSTM, Transformer-based systems, and a component part of federated schemes and the lightweight, edge-deployable intruder assessor models available. The aim of this study is to uncover a gap in the literature: integrated architectures to trade off audiences of faithfulness of physiological modeling with communication-layer security. To counter it, we present a single framework to include CNN-based spatial feature extraction, Bidirectional Long Short-Term Memory (BiLSTM)-based temporal models and Random Forest-based ensemble classification using a dual task-learning approach. We propose a multi-objective optimization framework to jointly optimize the performance of sleep-stage prediction and that of network anomaly detection. Performance on publicly available datasets (Sleep-EDF and CICIoMT2024) confirms that hybrid integration can be tailored to achieve high accuracy [99.8% sleep staging; 98.6% anomaly detection] whilst being characterized by low inference latency (<45 ms), which is promising for feasibility in real-time deployment in view of targeting edge devices. This work presents a comprehensive framework for developing secure, intelligent, and clinically robust digital sleep health ecosystems by bridging chronobiological signal modeling with cybersecurity mechanisms. Furthermore, it highlights future research directions, including explainable AI, federated secure learning, adversarial robustness, and energy-aware edge optimization. Full article

Because of their special optical and electrochemical characteristics, superior biocompatibility, adjustable surface chemistry, and inexpensive, scalable synthesis, carbon dots (CDs), including carbon quantum dots and graphene quantum dots, have become powerful and adaptable nanomaterials for advanced pharmaceutical analysis and other toxicants. The sensitive and selective detection of active pharmaceutical substances, degradation products, contaminants, biomarkers, and therapeutic medication levels in complex matrices has shown great promise in recent years with CD-based nanobiosensors. The development of various sensing platforms, such as electrochemical, optical, and dual-mode biosensors, as well as integration into microfluidic, paper-based, and wearable point-of-care (POC) devices, is made possible by their intrinsic fluorescence, effective electron transfer capacity, and ease of functionalization. With an emphasis on sensing mechanisms, biorecognition techniques, and analytical performance, this study critically reviews current developments in CD-based nanobio/chemosensors for pharmaceutical analysis. It includes a thorough discussion of important applications in drug development, stability research, therapeutic drug monitoring, and drug quality control. Along with new developments like green synthesis, AI-assisted signal processing, and smart sensing platforms, current issues with reproducibility, standardization, biocompatibility, and regulatory validation are highlighted. Lastly, prospects for the industrial application and clinical translation of CD-based nanobiosensors are discussed. Full article

Blood pressure is a central marker of cardiovascular risk, but continuous monitoring remains difficult because cuff-based measurements are intermittent and uncomfortable. Photoplethysmography (PPG) is already ubiquitous in wearables and can, in principle, enable cuffless blood pressure estimation from a single optical signal. However, many deep learning approaches that perform well in floating-point are impractical for microcontroller-class devices, where memory budgets, latency, and integer-only arithmetic constrain what can be deployed. A key open question is which neural architectures retain accuracy after full-integer quantization, rather than only under desktop inference. Here, we show an end-to-end, microcontroller-oriented evaluation framework that benchmarks multiple 1D convolutional models for cuffless systolic and diastolic pressure estimation from single-channel PPG, jointly optimizing estimation error, model footprint, and quantization robustness. We find that floating-point accuracy alone is a poor predictor of deployability: some lightweight CNNs exhibit substantial performance drift after INT8 conversion, whereas a compact residual 1D CNN preserves its predictions with near-identical error statistics after integer quantization. We then deploy the selected integer-only model on an STM32N6 microcontroller using an industrial toolchain and confirm that on-device inference maintains low bias and limited error dispersion while meeting real-time constraints for continuous operation. These results highlight architecture-dependent quantization stability as a critical design dimension for sensor-edge intelligence and support the feasibility of fully on-device cuffless blood pressure monitoring without multimodal sensing or cloud processing. Full article

Balance loss in older adults often leads to severe consequences, and wearable exoskeletons may help restore postural stability. This paper presents a novel hip cable-driven exoskeleton designed to support balance recovery. The proposed postural control strategy implemented on the device is based on maintaining balance by reducing the center of mass displacement from its equilibrium condition. Loss of balance is analyzed using multibody human models both with and without the exoskeleton. Simulation results evaluating static and dynamic balance demonstrate the effectiveness of the proposed control strategy and support its feasibility for implementation in a real system. The simulations presented in this study will be compared with experimental results from human subjects in future work. Full article

Layered double hydroxides (LDH) demonstrate significant potential in flexible superca-pacitors due to their high energy storage capability and adjustable architectures. Never-theless, the practical specific capacitance exhibited by current LDH remains below expec-tations, which is attributed to suboptimal electrode performance and limited active sites. Herein, a novel Fe-doped NiFeCo-LDH/NiFeCoO nanosheet composite supported on car-bon cloth was designed and fabricated as a flexible electrode. In this composite, the Ni-FeCo-LDH supplies numerous reactive centers and accelerates electrochemical kinetics, while the NiFeCoO and carbon cloth significantly improve electrical conductivity and cy-cling stability. Moreover, the heterointerface formed between the LDH and the metal oxide phase further facilitates charge transfer. Owing to such synergistic interactions, the pre-pared NiFeCo-LDH/NiFeCoO@CC electrode demonstrates an excellent areal specific ca-pacitance of 3.282 F cm⁻² at a current density of 1 mA cm⁻², while maintaining a high ca-pacity preservation reaching 88.09% following 5000 cycles. Furthermore, the assembled NiFeCo-LDH/NiFeCoO@CC//AC asymmetric supercapacitor delivers an outstanding en-ergy density reaching 0.302 mWh cm⁻² under a power density of 0.776 mW cm⁻², coupled with an excellent capacitance preservation of 85.29% over 5000 cycles. Meanwhile, it can maintain its initial capacitance under varying bending degrees, rendering it widely ap-plicable for future advanced flexible and wearable electronic devices. Full article

The need for reliable preventive medicine tools is growing, especially for diseases with long diagnostic delays, such as endometriosis, which can take several years to diagnose. In this context, cellulose acetate nanofibrous membranes were prepared via electrospinning, to create the absorbent core of a smart wearable in the form of a sanitary pad, intended to support electronic diagnostic devices. A multi-layered structure was opted for, with each layer acting in a specific way according to its position within the pad, regarding mainly absorbency and porosity. The membranes were ultralight and highly absorbent, with single membranes showing an absorbency of 20–70 times their initial weight, and multi-layered membranes 15–30 times. Morphological evaluation of the pad was used as the basis for the optimization of the fabrication parameters, while liquid absorption capacity confirmed the pad’s high absorbency. Additionally, chemical and toxicological assessments indicated in vitro biocompatibility of the pad. The potential of the electrospinning process in the fabrication of menstrual hygiene pads is shown by these results. Future studies should focus on the integration of smart devices within the pad, as well as their functionality and effectiveness. Full article

With the rapid development of electronic devices and wireless communication systems, electromagnetic interference pollution has become a critical concern, driving the urgent demand for high-performance, lightweight, and flexible electromagnetic interference (EMI) shielding materials. To endow fabrics with excellent electromagnetic shielding, a Fe₃O₄/Ag/RGO ternary nanocomposite-coated cotton fabric for electrical conductivity and EMI shielding application was developed. The cotton fabric pretreated with dopamine was coated with graphene oxide (GO), followed by silver nanoparticles (Ag) via a microwave-assisted chemical reduction method, and Ag/reduced graphene oxide (RGO)-coated cotton. Subsequently, nano-ferroferric oxide was deposited on Ag/RGO-coated cotton fabric using a coprecipitation method. The results show that the surface resistance of Fe₃O₄/Ag/RGO-coated cotton fabric arrives at 1.68 Ω/sq, demonstrating excellent electrically conductive performance. Fe₃O₄/Ag/RGO-coated cotton fabric demonstrates outstanding electromagnetic shielding performance, with SE values exceeding 45 dB across the entire 1–18 GHz range. The flexibility and superior electromagnetic shielding performance of Fe₃O₄/Ag/RGO-coated cotton fabric render it a promising candidate for applications in wearable electronics, aerospace, advanced protective systems, and military protective clothing. Full article

The demand for flexible and wearable electronics has intensified the need for conformable, high-performance, and self-sustaining power sources. Flexible supercapacitors (FSCs) and flexible batteries (e.g., lithium-ion and lithium–sulfur) are promising owing to their high-power density, long cycle life, and mechanical flexibility. A transformative solution lies in integrating these storage devices with mechanical energy harvesters, particularly triboelectric nanogenerators (TENGs), to create autonomous self-charging power systems (SCPSs). TENGs exhibit high output, versatile operational modes, material flexibility, and efficient energy harvesting from body movements. This review provides an overview of the recent advances in flexible energy storage technologies, encompassing carbon-based materials, MXenes, polymers, metal oxides, metal–organic frameworks (MOFs), and their hybrid architectures. It discusses the synergistic integration of these storage devices with TENGs to realize multifunctional SCPSs. It also highlights the fundamental design principles of flexible devices, the critical interplay of materials and architecture, and the journey towards monolithic system integration. The review also underscores the importance of managing harvesters’ pulsed output for efficient storage. Finally, a critical analysis of the challenges, including the energy density–flexibility compromise, environmental stability, and safety, is presented, alongside a forward-looking perspective on commercialization pathways for these technologies to power the next generation of autonomous wearable and sustainable electronic systems. Full article

As smart eyewear becomes increasingly widespread, the need for hands-free input interfaces is growing. Although eye-based input is a promising approach, many everyday interactions do not necessarily require the high-precision gaze-point estimation used in mainstream camera-based systems; instead, what is often needed is the recognition of coarse eyeball direction. In this study, we propose a method for recognizing coarse eyeball direction using infrared distance sensors mounted on eyewear. The proposed method leverages deformation patterns in the eyelid and surrounding skin associated with changes in eyeball direction. The evaluation results show that the proposed method achieved macro-F1 scores of 0.9 or higher in the best-performing conditions for the five- and nine-direction settings. These results demonstrate the feasibility of recognizing coarse eyeball direction from eyelid-skin deformation using infrared distance sensors on eyewear. Rather than replacing high-precision gaze-point estimation, the proposed method can be positioned as a low-cost, non-contact, and low-dimensional sensing approach for command-type eye-based input on eyewear devices. Full article

Carotid artery disease, including atherosclerotic stenosis and non-atherosclerotic abnormalities, substantially increases ischemic stroke risk and motivates accessible tools for early screening. Current diagnostic pathways rely on clinic-based imaging and skilled operators, creating barriers to frequent monitoring and scalable deployment. We present a non-invasive diagnostic approach using a wearable MEMS accelerometer patch to capture mechano-acoustic vibrations generated by carotid blood flow at the neck. The miniature device integrates a hermetically sealed wideband accelerometer with out-of-plane sensitivity and micro-g resolution to detect subtle flow-induced vibrations. We validated the approach in a carotid flow phantom and a clinical study of 74 patients. Time–frequency representations were computed using the continuous wavelet transform (CWT), from which interpretable spectral and scalogram-derived candidate biomarkers were extracted. Six non-redundant features were then selected for multivariate classification, distinguishing pathology, defined as 50% or greater stenosis or a non-atherosclerotic abnormality, from non-pathology, defined as less than 50% stenosis. Finally, model interpretability was assessed using SHapley Additive exPlanations (SHAP) to quantify the contribution of each biomarker to predicted disease probability. These findings resulted in an AUROC of 0.97 and AUPR of 0.947, with 81.7% sensitivity and 93.6% specificity at the prespecified threshold (precision 85.4%, F1 83.5%, accuracy 89.8%), highlighting the potential of wearable seismic sensing combined with interpretable machine learning for fast screening and longitudinal monitoring of the right and left carotid arteries. Full article

Structural colors generated by interference, diffraction, or light scattering offer vivid visual effects without dyes or electronic components, making them promising for flexible optical sensing. This work reports a simple stretch–plasma–release (S-P-R) strategy to fabricate wrinkled photonic elastomers (WPEs). The flexible periodic structures exhibit mechanically responsive structural colors, as tensile strain alters the grating period, generating optical signals that can be visualized and quantified by spectroscopy. The wrinkle period is tunable in the range of 0.4–3.42 μm by adjusting plasma power, exposure time, pre-stretch ratio, and film thickness. A dumbbell-shaped substrate design reduces edge-induced stress concentration. It shows improved wrinkle uniformity, with the coefficient of variation reduced from 6.64% to 2.74%, and experimental colors agreeing well with modified Bragg condition predictions. The reflection peak shows a significant shift from 356 nm to 658 nm with varying viewing angles. Patterned plasma treatment enables the selective generation of wrinkled structures, producing bright color patterns. The structural color can be fully erased at a critical strain of 20% and recovered upon release, remaining stable over multiple loading–unloading cycles. With excellent mechanical compliance and optical tunability, these materials are well-suited for integration with hydrogel-based systems and show promise for wearable devices, security marking, and anti-counterfeiting applications. Full article

Flexible heat exchangers with intricate three-dimensional (3D) geometries exhibit superior mechanical and thermal performance compared with traditional two-dimensional (2D) designs. Their ability to offer greater design freedom and unique functionalities makes them particularly attractive for wearable medical devices. This study investigates flexible heat exchanger technologies in three main directions: (i) miniaturisation, (ii) integration of physical and mathematical models, and (iii) enhanced adaptability through heterogeneous design integration. Through a combination of literature review, mathematical modelling, and experimental analysis, the thermal efficiency of several configurations is compared, including basic thermoplastic polyurethane (TPU) tubes and 3D bio-inspired TPU tubes with aluminium-finned structures. The findings establish a foundation for the development of next-generation flexible wearable medical cooling devices with improved thermal management capabilities and practical applicability in industrial design. Furthermore, the outcomes of this research will directly support the development of improved wearable cooling devices within a UK-based medical device SME, Paxman Scalp Coolers, facilitating the translation of advanced heat exchanger designs into clinically relevant and commercially viable solutions. Full article

Ankle–foot orthoses (AFOs) are widely used in the rehabilitation of patients with neurological or musculoskeletal disorders. However, treatment outcomes may be influenced by incorrect use of the device or by inappropriate orthosis selection. Since many types of AFOs are available, differing in materials, stiffness, and geometry, an objective evaluation tool can support clinical decision-making. This work presents the design, development, and characterization of an instrumented AFO able to quantify relevant gait parameters in an objective way. The proposed device integrates three measurement modalities in a compact wearable structure. Two longitudinal strain gauges estimate ankle plantar- and dorsiflexion angles. Two force-sensitive elements detect foot–ground contact and allow identification of stance and swing phases of the gait cycle. A single inertial measurement unit (IMU) is used to measure lateral shank inclination. The strain-gauge-based angle estimation was validated against a gold-standard motion capture system, achieving a root mean square error of approximately 1.6 degrees and showing higher accuracy than the IMU for plantar/dorsiflexion measurement, while maintaining a simple electronic architecture. The force sensors were validated using a force platform and demonstrated reliable detection of loading and unloading events. Monitoring lateral inclination through the single IMU provides additional information related to balance and potential fall risk. Data are transmitted via Bluetooth Low Energy (BLE) to a custom Python-based application for real-time visualization and recording. Overall, the results validate the electronic instrumentation and demonstrate reliable system performance, indicating that the proposed instrumented AFO represents a promising platform for objective gait assessment and future clinical applications. Full article

Conventional gel electrodes are the gold standard for surface electromyography (sEMG), yet their bulkiness, stiffness, and limited gel lifetime prevents seamless day-long integration with wearable robots. We integrated ultrathin skin-conformal temporary tattoo electrodes with a powered unilateral hip exoskeleton and compared signal quality during treadmill walking against gel. In this pilot study, five healthy participants completed three consecutive walking blocks at fixed speed: (1) using gel electrodes; (2) using tattoo electrodes to compare signal quality; and (3) using the same tattoo electrodes (not repositioned) after eight hours of wear to simulate a full day of typical device use and to evaluate potential degradation in signal quality over time. Electrodes were positioned on muscles not covered by the exoskeleton interface (tibialis anterior and gastrocnemius medialis), as well as on muscles located beneath the exoskeleton cuff, which were potentially subject to motion artifacts due to the application of external forces by the exoskeleton (rectus femoris and biceps femoris, BF). Across all muscles, for both gel and tattoo electrodes, the root mean square error (RMSE) between normalized sEMG envelopes and biological activation profile was 0.069 ± 0.048, and Pearson’s correlation coefficient (ρ) was 0.844 ± 0.091. Re-testing the same tattoo electrode pair after eight hours confirmed day-long stability without the need for recalibration. Statistical analysis revealed no significant differences in signal quality, also when applying assistive forces, between the two electrode types and across all muscles (RMSE, all p ≥ 0.3125; ρ, all p ≥ 0.1250), as well as no degradation after eight hours (RMSE and ρ: all p ≥ 0.0626, uncorrected). Finally, in a proof-of-concept session, BF activity measured with tattoo electrodes was found reliable to drive hip-extension assistance in real time. Collectively, these results show that tattoo electrodes deliver signal quality comparable to gel electrodes while offering a low-profile skin-conformal interface and day-long usability, making them a promising option for enhancing EMG-based control in wearable robots. Full article

Flexible electrolyte-gated synaptic field-effect transistors (EGFETs) have emerged as a promising platform for neuromorphic visual systems, owing to their low-voltage operation, diverse synaptic plasticity, and exceptional mechanical flexibility. In particular, gel-based electrolytes, including hydrogels and ion gels, play a pivotal role as functional gate dielectrics, enabling efficient ion transport and strong ion–electron coupling through electric double-layer (EDL) formation. By leveraging these unique properties at the semiconductor/gel interface, EGFETs can effectively emulate essential biological synaptic behaviors, including short-term and long-term plasticity under optical stimulation. The inherent compatibility of EGFETs with a broad range of semiconductor channels, gel electrolytes, and flexible substrates enables the development of wearable and conformable neuromorphic platforms that seamlessly integrate sensing, memory, and signal processing within a single device architecture. Recent advances in gel material engineering, such as polymer network design, ionic modulation, and nanofiller incorporation, have significantly improved ion transport dynamics, interfacial stability, and device performance. Despite remaining challenges related to ion migration stability, multi-physical field coupling, and large-area device uniformity, these developments have substantially advanced the practical potential of gel-based systems. This review provides a comprehensive overview of the operating mechanisms, gel-based material systems, synaptic functionalities, mechanical reliability, and future prospects of flexible electrolyte-gated visual synapses, highlighting their considerable potential for next-generation intelligent perception and artificial vision technologies. Full article