Search Results (10,545)

Voice disorders severely limit verbal communication, creating a need for intuitive assistive technologies. To meet this need, we present epidermal strain sensors that capture strain signals during silent speech and hand gesture. A thin electrospun nanofiber layer integrated onto commercial polyurethane films guides uniform, controlled microcrack formation in screen-printed carbon conductive paths, achieving a gauge factor up to 243 over 0–40% strain. Signals from the seven-channel strain sensor array are recognized by a hybrid neural network that combines convolutional and Transformer architectures, reaching over 98% accuracy. The recognized outputs are rendered in virtual reality (VR), enabling intuitive, real-time communication. Moreover, the approach simplifies fabrication by enabling crack-based strain sensing with only a thin electrospun surface layer on commercial polyurethane films, eliminating the need for thick freestanding electrospun substrates. This cost-effective approach addresses limitations of conventional electrospun substrates by minimizing the thickness of the electrospun layer, thereby shortening the electrospinning time. Overall, the work demonstrates a method for translating natural non-verbal expressions into speech and text in VR, with promising applications in healthcare and assistive communication. 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

Transparent clinical decision-making remains a critical barrier to deploying deep learning in medical diagnosis. Post hoc explanation methods approximate model behaviour after training but cannot guarantee that explanations faithfully reflect the underlying reasoning. This study proposes a Self-Explaining Neural Network (SENN) for Parkinson’s Disease (PD) screening via Ground Reaction Force (GRF) gait analysis, enforcing intrinsic interpretability through learnable basis concepts and input-dependent relevance scores computed jointly with the prediction. The architecture combines a four-block residual CNN backbone with stochastic depth regularisation, a 16-concept encoder with diversity and stability constraints, and temperature-scaled probability calibration for reliable clinical operating points. Evaluated on the PhysioNet Gait in Parkinson’s Disease dataset (306 subjects, 16 GRF sensors per foot), SENN achieves a subject-level ROC-AUC of 0.916 [95% CI: 0.867–0.964], sensitivity of 0.913 [0.862–0.963], specificity of 0.671 [0.485–0.858], and Average Precision of 0.942 [0.918–0.967], reported across five independent random seeds. Comparative evaluation against four deep learning baselines—CNN-Residual, BiLSTM, CNN-LSTM, and CNN-Attention—confirms that the interpretability constraints impose no statistically significant reduction in discriminative performance, with all pairwise ROC-AUC confidence intervals overlapping. Concept-level analysis reveals that the three most discriminative concepts correspond to disrupted midfoot loading patterns, increased step-length variability, and bilateral cadence asymmetry—all established biomechanical hallmarks of parkinsonian gait—providing clinically grounded, patient-specific explanations without post hoc approximation. These findings demonstrate that rigorous intrinsic interpretability and competitive predictive accuracy are simultaneously achievable in deep gait analysis, supporting the clinical adoption of transparent diagnostic AI. Full article

Providing reliable sensory feedback is one of the most challenging aspects of transfemoral prosthetics, motivating the development of intuitive vibrotactile interfaces capable of conveying information about limb position in real-time. The aim of this study was to develop a vibrotactile feedback prototype and examine which interstimulus intervals (ISIs) and vibration waveforms might best enhance recognition of sequential tactile patterns. The results will be used to inform the development of a prototype to be tested on participants with transfemoral amputation where prosthetic feedback is provided. A forearm-mounted six-actuator feedback system, encoding eight lower-limb configurations, was used in two experiments with healthy adults. Experiment 1 assessed recognition accuracy across ISIs from 10 to 110 ms, while Experiment 2 compared sinusoidal and square waveforms under matched conditions. Recognition accuracy was high across all tested conditions, with no significant effects of ISI (p = 0.79) or waveform type (p = 0.17). These results indicate that participants were able to interpret spatially distributed vibrotactile patterns even under rapid temporal sequencing and with differing signal shapes. The system therefore offers design flexibility for real-time prosthetic feedback, suggesting that fast update rates may be achievable without a statistically detectable reduction in perceptual clarity within the tested conditions. These findings provide practical guidance for developing robust, user-friendly sensory substitution systems intended to increase proprioceptive awareness in transfemoral prosthesis users. 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

Printed biosensors have attracted increasing attention as platforms for rapid, low-cost, and portable diagnostics because they can be fabricated on flexible or rigid substrates using scalable printing techniques. Their performance is strongly influenced by both the printing process and the materials employed, since factors such as ink rheology, particle dispersion, interfacial behavior, and post-processing conditions directly affect device architecture, sensing performance, and manufacturing reliability. This review summarizes recent advances in printed biosensors from the combined perspectives of printing technologies and functional materials. Commonly employed printing techniques, including inkjet, screen, aerosol jet, and roll-to-roll gravure printing, are discussed with emphasis on their processing characteristics and material requirements. The review also examines key material platforms used in printed biosensors, including carbon-based nanomaterials, metal oxides, metal nanoparticles, conductive polymers, dielectric materials, and hybrid composites, highlighting their roles in electrical conductivity, catalytic activity, biomolecule immobilization, mechanical flexibility, and overall analytical performance. Finally, current challenges and emerging research directions are outlined with respect to ink stability, post-processing strategies, sensor reliability, manufacturability, and practical translation. Overall, this review emphasizes that the development of high-performance printed biosensors depends on the synergistic integration of rational material design with optimized printing strategies. 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

Mine rescue operations are frequently conducted in hazardous underground environments characterized by damaged infrastructure, unstable communications, heat stress, and hypoxia risk, all of which threaten the safety of rescue personnel. To address these challenges, this study proposes a prototype-oriented mine-rescue monitoring framework that combines a Wi-Fi/optical-fiber communication architecture with flexible wearable sensing modules for physiological monitoring. The communication design employs Wi-Fi for local wireless data aggregation and optical fiber for reliable long-distance backhaul to the surface command side. For wearable monitoring, two flexible sensing modules were developed: a temperature sensor based on a polyaniline/graphene–polyvinyl butyral composite film and a PPG-oriented flexible optoelectronic module based on an ITO/Ag/ITO multilayer transparent electrode structure. Experimental results show that the temperature sensor exhibits a clear temperature-dependent resistance response within the tested range, while the optoelectronic module demonstrates low sheet resistance and acceptable electrical continuity under repeated bending. These results provide preliminary support for combining hybrid underground communication architecture with flexible wearable sensing components in mine-rescue scenarios. However, the present work remains at the stage of architecture design and component-level validation, and full end-to-end system verification under simulated or field rescue conditions will be the focus of future studies. 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

Aerogels represent an extraordinary class of materials characterized by remarkable properties, including an exceptionally high porosity (approximately 99.8%), minimal weight, extraordinarily low density, low thermal conductivity, a diminished dielectric constant, and a reduced refractive index. These attributes arise from their extensive micro-meter-sized pores. In recent years, there has been a notable surge of interest in carbon or carbon nanotube (CNT) based aerogels due to their compelling potential across various applications, encompassing sensors, energy systems, and catalysis, among others. In the context of our ongoing investigation, we have successfully synthesized lightweight aerogels by incorporating copper and carbon nanotubes (Cu-CNT) through electrospinning. Intriguingly, these aerogels exhibit an electrical conductivity of approximately 0.5 × 103 S/cm, positioning them within the realm of semiconductors. Concurrently, their density measures approximately 1.669 g/c.c (similar to CNTs), underscoring their notably low mass. These semi-conductive aerogels, uniquely characterized by their lightweight nature and expansive surface area (approximately 442 m²/g), manifest considerable potential across a spectrum of applications. This includes catalytic processes, energy storage mechanisms, bio-sensing technologies, thermoelectric systems, and the burgeoning domains of micro and wearable electronics. The distinctive combination of properties within these aerogels augments their suitability for these diverse applications, offering the prospect of innovative and impactful advancements in various scientific and technological arenas. 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