Search Results (673)

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

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

Flexible and wearable electronics require soft sensing materials that balance mechanical compliance, stable signal transduction, and durability for human–machine interfaces (HMIs). To address the limitations of single-filler systems, we propose a poly(vinyl alcohol) (PVA)/aramid nanofiber (ANF)/MXene organogel (PAM) as a multifunctional soft platform. This design integrates a PVA physically crosslinked network with ANF for mechanical reinforcement and MXene for electrical functionality. The optimized PAM composite exhibits outstanding mechanical properties, including a fracture stress of 2931 kPa, a fracture strain of 676%, and a fracture toughness of 9.04 MJ m⁻³. Importantly, PAM serves as a single material platform configurable into three sensing modalities. The resistive strain sensor achieves a gauge factor of 3.1 over 10–100% strain and enables the reliable recognition of human joint movements and gestures. The capacitive pressure sensor delivers a sensitivity of 0.298 kPa⁻¹, rapid response/recovery times of 30/10 ms, and is integrated with a wireless module to control a smart car. Furthermore, the PAM-based triboelectric nanogenerator (TENG) delivers excellent electrical outputs (V_oc = 123 V, I_sc = 0.52 μA, Q_sc = 58 nC) and functions as a self-powered smart handwriting pad, achieving a machine-learning-based recognition accuracy of 97.6%. This work demonstrates the immense potential of the PAM organogel for advanced, self-powered HMIs. Full article

Microelectromechanical systems are being increasingly deployed in nuclear industry robotics, where their great sensitivity and mechanically stable silicon structures enable reliable sensing in radiation-exposed environments. An ultra-thin silicon strain gauge without an oxide substrate layer designed for robotic electronic skin is evaluated under Co-60 γ irradiation, representative of nuclear decommissioning conditions. The sensor performance is evaluated based on electrical measurements conducted before and after irradiation, focusing on cumulative radiation-induced effects. The results show that silicon strain gauge signal maintains a high linearity (R² > 0.99) under strain. Across an accumulated dose range up to approximately 15 Gy, only minor variations are observed, including a resistance increase within 1.3% and a reduction in gauge factor within 5% for most specimens. The radiation-induced resistance increases and sensitivity degradation results in a maximum strain estimation error of approximately 22.5 με (≈3.5%) within the tested operating range below 700 με. Full article

Blade Tip Timing (BTT) provides a superior long-term blade monitoring solution compared to strain gauges, yet its adoption in lower value turbomachinery remains constrained by sensor costs. This study explores Inductive Proximity (IP) probes as a cost-effective alternative to eddy current probes. Experimental results demonstrate that the IP probes achieve comparable accuracy in natural frequency estimation while being 40 times more affordable than traditional active eddy current probes. Additionally, IP probes maintain robustness, ensuring reliable vibration parameter extraction via open-source BTT software (version 0.17.0). These findings significantly enhance the accessibility of BTT, enabling budget-conscious turbomachine manufacturers to implement high-precision blade monitoring solutions across a wider range of applications. 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

Owing to its exceptional mechanical and electrical properties, graphene is regarded as an ideal sensing material for piezoresistive pressure sensors. However, vacancy defects inevitably introduced during graphene preparation and transfer significantly alter its electrical characteristics and piezoresistive performance. Based on first-principles calculations, this work systematically investigates the influence of mono-, di-, and tri-vacancy defects on the electrical and piezoresistive properties of graphene. The results indicate that di- and tri-vacancy defects reconstruct into 5-8-5 and 5-10-5 configurations during relaxation. Mono-, di-, and tri-vacancy defects effectively open bandgaps in graphene, yielding values of 0.62, 0.48, and 0.72 eV, respectively. The mono-vacancy introduces localized defect states near the Fermi level, the di-vacancy shifts the Dirac point from K to M, and the tri-vacancy moves it along the K-Γ path, eventually placing it between K and Γ. The application of strain not only widens the bandgap of defective graphene but also induces the movement of defect energy levels toward the band edges in the mono-vacancy system. All three defect types enhance the piezoresistive effect, with the tri-vacancy defect showing the most pronounced enhancement—boosting the gauge factor by a factor of 5.58. These findings provide a theoretical foundation for optimizing graphene-based pressure sensors. Full article

A dual-mode electrical–optical nanocomposite hydrogel is developed by integrating carboxyl-modified upconversion nanoparticles (UCNPs-COOH) and quaternized chitosan (CQAS) into a polyacrylamide (PAAm) covalent network. The hydrogel exhibits high optical transparency (>90% in the visible region), excellent mechanical properties (fracture strain of 1742%, tensile strength of 0.85 MPa, toughness of 6.57 MJ/m³), and robust adhesion to various substrates. The synergistic covalent–noncovalent hybrid network enables efficient energy dissipation, while CQAS-enhanced dispersion of UCNPs significantly improves upconversion luminescence intensity and stability, as evidenced by prolonged fluorescence lifetime from 0.564 ms to 0.691 ms at 539 nm. Leveraging distinct electrical and optical signal transduction pathways, the hydrogel functions as a highly sensitive resistive strain sensor with multistage gauge factors up to 13.85 and excellent cyclic stability over 1200 loading–unloading cycles at 100% strain for human motion monitoring. It also serves as a ratiometric optical pH sensor over a broad range (pH 1–13) based on phenolphthalein-sensitized upconversion luminescence, with excellent repeatability. By integrating real-time resistance responses with optical readouts within a single soft material, this work demonstrates a reliable dual-mode sensing strategy for simultaneous mechanical and chemical monitoring, holding promise for wearable electronics, smart healthcare, and environment-responsive sensing systems. Full article

Quantifying the health of civil infrastructure using sensor data remains challenging, as degradation-related signals are typically weak and obscured by dominant environmental and operational effects. In structural health monitoring (SHM), this often results in sensor measurements that are highly periodic or intermittent, while long-term degradation manifests only as subtle drift. This study addresses the problem of extracting meaningful proxies for structural health from such data. We propose monotonicity as a guiding principle, operationalized through absolute Spearman’s rank correlation between sensor values and time. Two complementary methods are introduced. First, subgroup discovery is employed to identify structurally coherent groups of sensors that exhibit significantly elevated monotonicity, enabling the construction of robust health proxies through aggregation. Second, we present Latent Monotonic Feature Discovery (LMFD), a data-driven method inspired by equation discovery, which searches for arithmetic combinations of sensors that yield monotonic behaviour even when individual sensors are predominantly non-monotonic. The methods are evaluated on a two-year monitoring dataset from a Dutch concrete highway bridge comprising strain gauges, geophones, and temperature sensors. Results show that meaningful monotonic proxies can be derived both from naturally monotonic sensor subgroups and from composite features constructed from periodic signals. The proposed approach provides indirect yet interpretable indicators of structural health and offers a principled way to uncover latent degradation trends in long-term SHM data. Full article

Measurement While Drilling (MWD) tools are widely employed in deep and ultra-deep well drilling. In the high-temperature and high-pressure (HTHP) environments characteristic of these wells, structural deformation induced by thermal expansion interferes with the bit parameter recorder’s sensor readings, thereby degrading the measurement accuracy of weight on bit (WOB) and working torque (WT). To address this issue, this paper proposes a temperature compensation method based on thermo-mechanical coupling simulation. This method systematically establishes the quantitative relationships between multiple loads—including WT, WOB, temperature, and make-up torque—and the strain at critical locations of the bit parameter recorder through finite element analysis (FEA). Furthermore, surface calibration experiments have verified a strong linear correlation between the strain gauge voltage signals and the simulated strain. Building upon this foundation, an inversion-based compensation algorithm is developed. This algorithm effectively isolates the interference caused by thermally induced deformation and inversely deduces the true WOB and torque values by utilizing downhole-measured sensor voltage and temperature data. The research results demonstrate that the proposed temperature compensation method significantly improves the measurement accuracy of the bit parameter recorder under harsh, high-temperature operating conditions. The relative errors for both WOB and torque measurements are controlled to within 5%, providing a reliable solution for precise parameter measurement in high-temperature deep wells. Full article

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application. Full article

In this study, a high-performance MoS₂-based strain-gauge pressure was sensor fabricated entirely below 80 °C, enabling direct integration onto flexible polyethylene terephthalate (PET) substrates. The sensor comprised a three-layer MoS₂ channel (~2 nm) patterned via dry transfer and O₂/Ar plasma etching, interfaced with Cr/Au electrodes. This wafer-scale and cost-effective fabrication route preserves the crystallinity of the film and prevents substrate degradation. The sensor achieved a gauge factor of ~104 under compression, representing a fifty-fold improvement over conventional metal foil gauges (~2), with a linear response across both compressive and tensile regimes. Mechanical robustness was confirmed through repeated bending and tape adhesion tests, with no degradation in electrical performance. When configured as a Wheatstone bridge, this device exhibits normalized sensitivity suitable for real-time monitoring, with response and recovery times below 200 ms. These results establish O₂/Ar-plasma-patterned MoS₂ architectures as a scalable, cost-effective platform for next-generation flexible sensors, outperforming metal-foil technology in applications including seat-occupancy detection, wearable physiological monitoring, and tactile interfaces for soft robotics. Full article

The variable-pitch connecting rod of a helicopter bears axial tensile and compressive loads during operation. The traditional load monitoring method using strain gauge is easily affected by external conditions. Therefore, a giant magnetostrictive (GM) tension and compression force sensor with permanent magnet bias is proposed and optimized. Because the bias magnetic field plays a decisive role in the performance of the sensor, this paper has carried out in-depth research on this. Firstly, the mathematical model of the magnetic circuit is established, and the various magnetic circuits of the sensor are simulated and analyzed. Secondly, the magnetic flux uniformity of the GMM rod is used as the evaluation index, and the relative permeability of the magnetic material and the structure are systematically studied. The influence of parameters on the magnetic flux of the magnetic circuit, and finally the optimal parameter combination of the magnetic circuit is determined by orthogonal test. The results show that when the magnetic circuit without the magnetic side wall is used, the magnetic material can better guide the magnetic flux through the GMM rod; the magnetic flux uniformity of the optimized GMM force sensor is increased by 7.44%, the magnetic flux density is increased by 13.9 mT and the Hall output voltage increases linearly by 1.125% in the same proportion. This provides an important reference for improving the utilization rate of GMM rods and also improves the safety of flight operation and reduces maintenance costs. Full article

Objectives: This study aimed to assess stroke coordination and biomechanics in elite U23 male kayakers under valid on-water conditions (instrumented K1 kayak on a competition lake) across race-relevant stroke frequencies (60, 80, and 100 strokes·min⁻¹). Methods: To achieve our aims, twelve male athletes (age 21.00 ± 0.47 years) completed 500 m trials at three randomized paddle frequencies (60, 80, 100 strokes·min⁻¹) with 10 min of passive recovery in-between. Data were collected with inertial measurement units, and a customized seat/footrest with integrated strain-gauge sensors. Results: Principal Component Analysis identified four key components: Mechanical Work, Mechanical Energy, Stroke Variability (PCI, Phase Coordination Index), and boat acceleration, accounting for 76% of total variance. Linear mixed-effects models (within-subject LME; Participant random intercept; Satterthwaite df) revealed that Mechanical Work (χ² = 17.10, p < 0.001) and Mechanical Energy (χ² = 53.10, p < 0.001) increased significantly with stroke frequency. Phase Coordination Index showed a significant increase at 60 and 100 strokes·min⁻¹ (χ² = 16.78, p < 0.001; t = 4.78, p < 0.001), while boat acceleration was not significantly affected (χ² = 4.95, p = 0.08). The PCI correlated negatively with Mechanical Work (r = −0.37, p = 0.022) and positively with boat acceleration (r = 0.39, p = 0.010). Effect sizes were moderate to large (ηp² = 0.18–0.36; corresponding 95% confidence intervals are reported in the main text). For the primary mechanical indicator (Paddle Factor), the mixed-effects model yielded a marginal R² = 0.57, reflecting the proportion of variance explained by cadence. Conclusions: Approximately 80 strokes·min⁻¹ may represent a condition in which coordination metrics appear comparatively favorable. These findings are exploratory and hypothesis-generating, not prescriptive. No causal inference can be drawn, and any training application attempts should await replication in larger, longitudinal and randomized studies. Full article

Electrical strain gauges are essential for monitoring wind turbine tower loads and fatigue, but accurate load measurements from these sensors require calibration over time to correct the zero-drift found in long-term measured signals. Calibration is often performed using nacelle rotation events for cable untwisting, where the tower mechanical load is known; however, non-uniform solar heating during these events can introduce thermal stresses that are misinterpreted as drift, causing systematic errors. This study evaluates six preprocessing methods for correcting zero-drift and thermal stresses in strain gauges, using measurements from two tower cross-sections—one with temperature sensors and one without. Performance is quantified using the scatter of the 10 min mean bending moments in the fore–aft and side-to-side directions and the cumulative fatigue damage over the monitoring periods. Results show that modelling the thermal stresses using a linear regression model with temperature measurements as inputs yields the most physically consistent load curves. If temperature measurements are unavailable, the effects of thermal stresses can be partly mitigated by restricting calibration to nighttime events or using solar-position variables in a regression model (instead of temperatures). As expected, the choice of preprocessing method significantly impacts load curves, but its influence on fatigue damage estimates is limited. Full article