Search Results (5)

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

Sweat, which contains a rich array of biomarkers, serves as a vital biological fluid for non-invasive biosensing. Wearable sweat sensors have garnered significant interest owing to their portability and capacity for continuous monitoring. However, there are safety concerns regarding the direct contact of sweat sensors with the skin during the detection process. The chemical substances in the sensor patches may cause contamination of the epidermis when in contact with the skin, leading to skin allergic reactions. Sample collection and biosafety isolation are critical issues in wearable sweat detection. To address this, we develop a cactus-inspired biomimetic Janus membrane capable of unidirectionally transporting and concentrating sweat toward a designated detection zone. Through unidirectional transport from the hydrophobic layer to the hydrophilic layer of the Janus membrane, sweat droplets are enriched at the designated detection point of the conical hydrophilic pattern via Laplace pressure. The bionic osmosis-enrichment sensing patch effectively inhibits direct contact between indicators and skin, eliminating potential epidermal contamination. This achieved the effect of in situ perspiration collection under the premise of biosafety isolation. To rapidly and accurately analyze sweat biomarkers, we employ a deep learning (DL)-assisted fluorescence sensor for efficient and precise detection of biomarker concentrations. A dataset of 4500 fluorescence images are constructed and used to evaluate two DL and seven machine learning (ML) algorithms. The convolutional neural network (CNN) model could easily and accurately classify and quantitatively analyze the total concentration of the amino acid mixture, Ca²⁺ and Cl⁻, with 100% classification accuracy. The consistency between the detection results of actual sweat by the DL-assisted fluorescence method and fluorescence spectroscopy was 91.4–96.0%. This approach demonstrates high reliability in sweat collection and analysis, offering a practical tool for clinical health monitoring, early disease intervention, and diagnosis. Full article

This study integrates hollow microneedle arrays (HMNA) with a novel jellyfish-shaped electrochemical sensor for the detection of key biomarkers, including uric acid (UA), glucose, and pH, in artificial interstitial fluid. The jellyfish-shaped sensor displayed linear responses in detecting UA and glucose via differential pulse voltammetry (DPV) and chronoamperometry, respectively. Notably, the open circuit potential (OCP) of the system showed a linear variation with pH changes, validating its pH-sensing capability. The sensor system demonstrates exceptional electrochemical responsiveness within the physiological concentration ranges of these biomarkers in simulated epidermis sensing applications. The detection linear ranges of UA, glucose, and pH were 0~0.8 mM, 0~7 mM, and 4.0~8.0, respectively. These findings highlight the potential of the HMNA-integrated jellyfish-shaped sensors in real-world epidermal applications for comprehensive disease diagnosis and health monitoring. Full article

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner. Full article

Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods. Full article