Health Monitoring Applications Research Articles

A quadrilateral inverse plate element for real-time shape-sensing and structural health monitoring of thin plate structures

The inverse finite element method (iFEM) emerged as a powerful tool in shape-sensing and structural health monitoring (SHM) applications with distinct advantages over existing methodologies. In this study, a quadrilateral inverse-plate element is formulated via a sub-parametric approach using bi-linear and non-conforming cubic Hermite basis functions for engineering structures, which can be modeled as thin plates. Numerical validation involves dense and assumed sparse sensor arrangements for in-plane, out-of-plane, and mixed general loading conditions. iFEM analysis reveals efficient monotonic convergence to analytical and high-fidelity finite element reference solutions. After successful numerical validation, defect detection analysis is performed considering minute geometric discontinuities and structural stiffness reduction because of latent subsurface defects under tensile and transverse loading conditions. The inverse formulation successfully resolves the presence of simulated defects under a sparse sensor arrangement. The proposed inverse-plate element is accurate in the full-field reconstruction of shape-sensing profiles and reliable in defect identification and quantification in thin plate structures.

Ultrahighly Sensitive Flexible Pressure Sensors with Dual-Mode Response Based on Monolayer Films of Calcium Niobate Nanosheets.

Flexible pressure sensors present enormous potential for applications in health monitoring, human-machine interfacing, and electronic skins (e-skin). However, at the cost of flexibility, the design of flexible pressure sensors has been facing the trade off between high sensitivity and wide sensing range. Herein, we propose a sandwiched structure composed of monolayer films of calcium niobate nanosheets to endow the device with both ultrahigh sensitivity and a wide sensing range. Tunable contact between the two electrodes of the pressure sensor through the gaps in the insulative monolayer film and precise thickness modulation of the monolayer films at the nanoscale result in an ultrahigh sensitivity and wide sensing range of the sensors. By virtue of these two traits, the pressure sensor distinguishes itself with unprecedented performances of ultrahigh sensitivity (6.43 × 104 kPa-1), a wide sensing range (1.94-60.00 kPa), a fast response time (<165 ms), and reliable repeatability. In addition, the sensor has a sensing mechanism transition from capacitive mode to piezoresistive mode from low pressure to high pressure. The sensors demonstrates the ability for motion detection of the human body. This work sheds light on the development of highly sensitive flexible pressure sensors.

AI-Enhanced Edge Device for Real-Time Snoring Detection

This paper presents the development of a cutting-edge, non-invasive edge device designed to monitor snoring and provide timely, moderate haptic feedback to users. Utilizing the Qualcomm Snapdragon 8cx Gen 3 processor, the device offers robust computing power and AI capabilities for real-time processing, making it a versatile tool for health monitoring applications. The system integrates a high-fidelity MEMS microphone array capable of capturing nuanced audio signals and a TDK piezoelectric haptic actuator, which delivers precise alerts through customized vibrations. The research explores the potential of this advanced hardware in detecting and managing obstructive sleep apnea (OSA), a condition often underdiagnosed due to a lack of patient awareness. By leveraging state-of-the-art digital signal processing and deep learning techniques, the device aims to enhance user awareness and intervention in sleep-related disorders, offering a promising new avenue for improving patient outcomes and quality of life.

MZI-based high intensity responsive sandwiched multi-layer fiber optic humidity sensor

This paper presents a multi-layer optical fiber humidity sensor based on dual-beam Mach-Zehnder interference (MZI) for optical intensity sensitivity. The sensor is fabricated using a large-scale axial offset fusion technique between a multimode fiber (MMF) and an offset hole two-core fiber (OHTC), making it suitable for agricultural and health monitoring applications. The 27 μm axial offset enables tilted light incidence, enhancing environmental sensitivity. With a single-cycle Graphene oxide (GO) coating of approximately 22 nm, the MMF-OHTC structure achieves high humidity sensitivity (0.39 dB/%RH) over the 25 %-80 % RH range with excellent linearity (R2≈0.99). The sensor exhibits stable, rapid response (2.52 s/0.46 s) and low temperature cross-sensitivity (0.06 %RH/°C). When combined with a hollow-core fiber Fabry-Perot interferometer (HCF-FPI), the system allows zero cross-talk simultaneous humidity and temperature measurements, expanding the potential applications of multi-layered structures in multifunctional detection systems.

V2CTx MXene-derived porphyrin-based MOF modified with ZIF-67 as an ultra-stretchable and deformable double-network hydrogel for room temperature detection of dimethylamine

Stretchable and self-adhesive chemical sensors are highly appealing for wearable applications in health and environmental monitoring. Here, for the first time, we report V2CTx MXene-derived Porphyrin-based MOF (V-PMOF) modified with Zeolite imidazole framework-67 (ZIF) for the room temperature detection of dimethylamine. V-PMOF is synthesized from V2CTx MXene using tetrakis(4-carboxyphenyl)porphyrin (TCPP) as a ligand via a solvothermal route. Thereafter, an optimized wt% ratio (1:1) of ZIF-67 and V-PMOF crosslinked by PVA/polypyrrole (Ppy) is put for lyophilisation which yields the V-PMOF@ZIF-67 double-network (DN) hydrogel. Detailed morphological assessment reveals dodecahedron shaped ZIF-67 particles are evenly distributed on the V-PMOF sheets which are encapsulated in the polymer matrix. This three-dimensional polymeric structure of hydrogel largely enhances the active sites on the surface with abundant oxygen vacancies. The sensor exhibits a dynamic range of 1 ppm to 13 ppm with a low limit of detection (LOD = 3 s/σ) of 902 ppb towards dimethylamine detection. Further, quick response/recovery times (3 s/7s) are observed along with high robustness tosignificant mechanical deformation, including twist (270°), large-range bending, and upto 500 % strain without affecting the sensing performance. The remarkable sensitivity is accredited to the abundance of O2 functional groups in polymer chains and the formation of schottky heterojunction between V-PMOF and ZIF-67, facilitating charge transfer from the heterostructure to dimethylamine during sensing. In addition, the self-adhesive hydrogel based sensor shows outstanding selectivity against other typical volatile organic compounds (VOCs) such acetone, ethanol, ammonia, trimethylamine, aniline, and methanol. In overall, this work provides insight into designing extremely flexible sensitive dimethylamine gas sensor as innovative material.

Hydro-steel structure digital twins: Application in structural health monitoring and maintenance of large-scale reservoir

In the context of frequent accidents during hydro-steel structures (HSS) operations due to harsh environments and extended service conditions, a novel approach is proposed to reduce the frequency of structural failure incidents and ensure safe and reliable operation. The approach begins with introducing a comprehensive DT modeling framework. Subsequently, detailed DT modeling and DT-based SHM methods are developed. Finally, a platform with perception, interaction, analysis, and decision-making for intelligent health monitoring and maintenance of HSS is constructed and validated in China’s large-scale reservoir project, Luhun Reservoir. The platform includes functions of condition monitoring, fault feature recognition, health status assessment, and maintenance strategies optimization. The integration of DT technology has led to significant improvements in health monitoring and maintenance quality, which includes data collection, model optimization, comprehensive evaluation, and decision-making. This approach has also demonstrated its effectiveness by reducing the operation and maintenance response time and enhancing the overall efficiency and reliability.

Fabrication of surface functionalized QCM sensor for BTX detection at ambient conditions

Developing optimized gas sensors for detecting low concentrations of Benzene, Toluene, and Xylene (BTX) in ambient conditions is advantageous for health and environment monitoring applications. This study reports on the feasibility of using Quartz Crystals Microbalance (QCM) coated by Tungsten Oxide (WO3) to detect BTX compounds. A WO3 layer was deposited on the QCM surface using DC magnetron sputtering. Optical Profilometry (OP), Scanning Electron Microscopy (SEM), and X-ray Diffraction (XRD) were employed to analyze sensor surface morphology and structure. Adsorption properties were studied through the Langmuir adsorption isotherm. Thermal stability was evaluated by thermogravimetric analysis. The response to BTX vapours was investigated using a laboratory-designed gas-sensing setup in ambient conditions. The optimized film thickness was 200.81 ± 0.39 nm for 540 s of sputtering. XRD pattern confirms the amorphous nature, and SEM images of WO3 show the granular microstructure with 200 nm diameter. The developed sensor shows the highest sensitivity for xylene (5.10 ± 0.08 Hz/ppm), followed by toluene (5.69 ± 0.22 Hz/ppm) and benzene (3.36 ± 0.24 Hz/ppm). The limit of detection for BTX was 1.48 ppm, 0.79 ppm, and 0.33 ppm, respectively. The sensor exhibited faster response times of 30 s, 32 s, and 43 s for BTX, respectively. Repeatability of 99 % and reproducibility of 98 % were observed for BTX vapours. The fabricated WO3-coated QCM sensor can detect BTX vapour at room temperature with high sensitivity and response time. Thus, it can play a significant role in detecting BTX in the workplace for health and environment monitoring systems.

Papermaking-inspired sustainable triboelectric sensors for intelligent detecting system

Digitalization not only drives the intelligent transformation of industries while fostering cultural diversity and innovation. Cutting-edge triboelectric sensing technology inspired by traditional Chinese papermaking fabricates a bamboo paper-based triboelectric nanogenerator (BP-TENG). The addition of high-purity bacterial cellulose improves the structural integrity of the resulting paper. The proposed BP-TENG exhibits excellent electrical performance, generating an output voltage of 88 V and a maximum output power of 0.9 mW at a low-frequency motion of 2 Hz. Moreover, the BP-TENG is combined with machine learning algorithms to build a multi-scene intelligent detecting system, including real-time handwriting-to-speech conversion, personalized jump prediction and smart home automation systems. The machine learning algorithm can achieve a recognition accuracy of up to 99.8 %. This multi-scene intelligent detecting system boasts superior accuracy, environmental sustainability and affordability, ideal for communication security, sports health monitoring and smart building applications.

Advancements in triboelectric nanogenerator applications for health monitoring

Advancements in triboelectric nanogenerator applications for health monitoring

Wearable and Multiplexed Biosensors based on Oxide Field-Effect Transistors.

Wearable sensors designed for continuous, non-invasive monitoring of physicochemical signals are important for portable healthcare. Oxide field-effect transistor (FET)-type biosensors provide high sensitivity and scalability. However, they face challenges in mechanical flexibility, multiplexed sensing of different modules, and the absence of integrated on-site signal processing and wireless transmission functionalities for wearable sensing. In this work, a fully integrated wearable oxide FET-based biosensor array is developed to facilitate the multiplexed and simultaneous measurement of ion concentrations (H+, Na+, K+) and temperature. The FET-sensor array is achieved by utilizing a solution-processed ultrathin (≈6nm thick) In2O3 active channel layer, exhibiting high compatibility with standard semiconductor technology, good mechanical flexibility, high uniformity, and low operational voltage of 0.005V. This work provides an effective method to enable oxide FET-based biosensors for the fusion of multiplexed physicochemical information and wearable health monitoring applications.

Effect of epoxy resin addition on the acoustic impedance, microstructure, dielectric and piezoelectric properties of 1–3 connectivity lead-free barium zirconate titanate ceramic cement-based composites

In this work, 1–3 connectivity barium zirconate titanate ceramic cement-based composites were fabricated using Portland cement and epoxy resin as the matrix. Barium zirconate titanate (BZT) of 40–60 % by volume was used while epoxy was used with cement at 0–7% by volume. Dielectric and piezoelectric properties, and other properties such as acoustic impedance, density and microstructure were investigated. It was found that epoxy resin can be used in combination with BZT to achieve a suitable acoustic impedance value (9–11 × 106 kg/m·s2) matching that of concrete for structural health monitoring application. Thus, when epoxy resin was used at 7 %, BZT volume can be increased to 60 % where the highest d33 value of 93 pC/N was found and remain within the acoustic matching range. In addition, when epoxy resin was increased, both piezoelectric charge coefficient (d33) and piezoelectric voltage coefficient (g33) were also found to increase. This is likely due to the lower porosity thus denser matrix when epoxy resin was used in addition to cement.

Reverse iontophoresis sensing electrode for joint detection of pH, NH4+, and lactic acid

With the rapid development of integrated electronic circuit technology, biological fluid-electricity integrated detection systems have gradually become a research hotspot. Researchers have integrated biological fluid electrical detection methods into circuits and developed small, low-power, portable detection devices equipped with sensing electrodes, which can achieve continuous monitoring of human health parameters. In recent years, the integration of reverse iontophoresis technology that can be used to extract body fluids with electrochemical sensors has opened up the possibility of flexible, portable biochemical sensing with excellent detection sensitivity. Herein, we present a method for extracting biofluids based on reverse iontophoresis technology, coupled with electrochemical sensing techniques for the simultaneous detection of various biomarkers in body fluids. The multi-channel sensing electrode was modified layer by layer using nitrogen-doped graphene (N-Gr), ion-selective membrane, or lactate oxidase for rapid and sensitive detection of pH (3–8), ammonium ion (NH4+) (0.1 mM–50 mM), and lactic acid (1 mM–50 mM). Subsequently, an integrated system for electrical stimulation extraction and sensing analysis based on reverse iontophoresis was established and experimentally tested. Experimental results demonstrated that the multi-channel joint detection sensing electrode has excellent sensing linearity, specificity, repeatability, and long-term stability. Finally, a smartphone-based WeChat applet was developed, which can realize parameter setting, function selection, and result display of sensor detection. In this study, reverse iontophoresis technology was used to collect body fluids, and the important biomarkers pH, NH4+, and lactic acid in the body fluids were detected. Overall, this research presents an integrated detection system and a multi-channel sensing scheme for the detection of important biochemical markers in bodily fluids, thereby providing potential value for health monitoring applications.

Efficient and widely tunable mid-infrared sources using GaAs and AlGaAs integrated platforms for second-order frequency conversion

Integrated coherent mid-infrared (mid-IR) sources are crucial for spectroscopy and quantum frequency conversion (QFC) to facilitate scalable fiber-based application of single photons. Direct mid-IR emission with broad tunability poses fundamental challenges from the gain media and mirror components. This paper presents a characterization of a second-order nonlinear platform. It showcases a mid-IR parametric coherent source with a continuous tuning range exceeding 230 nm centered around 2425 nm, achieved through difference-frequency generation (DFG). The nonlinear coefficient d14 of gallium arsenide (GaAs) and aluminum gallium arsenide (AlGaAs) on insulator is experimentally determined via second-harmonic generation (SHG) in waveguides of various lengths, and the tolerance of the process is investigated. These materials are explored for their high conversion efficiency, utilizing monolithic epitaxial quantum dots and integrated waveguides for QFC. The results demonstrate efficient and tunable mid-IR emission suitable for compact, scalable quantum emitters, with applications in environmental and health monitoring.

Development of a Highly Sensitive and Stretchable Charge-Transfer Fiber Strain Sensor for Wearable Applications.

Wearable electronics have significantly advanced the development of highly stretchable strain sensors, which are essential for applications such as health monitoring, human-machine interfaces, and energy harvesting. Fiber-based sensors and polymeric materials are promising due to their flexibility and tunable properties, although balancing sensitivity and stretchability remains a challenge. This study introduces a novel composite strain sensor that combines poly(3-hexylthiophene) and tetrafluoro-tetracyanoquinodimethane to form a charge-transfer complex (CTC) with carbon nanotubes (CNTs) on a styrene-butadiene-styrene substrate. The CTC improves conductivity through effective charge transfer, while CNTs provide mechanical reinforcement and maintain conductive paths, preventing cracks under large strains. Purposefully introduced wrinkles in the structure enhance the detection of small strains. The sensor demonstrated a broad strain-sensing range from 0.01 to 200%, exhibiting high sensitivity to both minor and major deformations. Mechanical tests confirmed strong stress-strain performance, and electrical tests indicated significant conductivity improvements with CNT integration. These results highlight the potential of the sensor for applications in health monitoring, human-machine interfaces, and energy harvesting, effectively mimicking the tactile sensing abilities of human skin.

Flexible intelligent fabric composed of pinecone-like microstructure Co-cMOFs/CoZnAl-LDH composite nanomaterial for various types of sensor signals to guide personalized health activities

Multifunctional interfacing is a key aspect of flexible sensor development that facilitates enhanced interaction with the natural environment. However, challenges faced by contemporary multifunctional flexible sensors are structural complexity and high cost. Herein, inspired by the specific structure of pinecone in nature and the microstructured neural networks randomly distributed within human skin, we fabricated Co-cMOFs/CoZnAl-LDH composite nanomaterials with pinecone-like microstructure. Through the mutual verification of finite element simulation and experimental data, the mechanism of high sensitivity of the sensor is dynamically revealed from the structural point of view. The prepared pressure sensors exhibit a detection range (0–120 kPa), high sensitivity (2.35 × 109 kPa−1) and fast response. The photoelectric sensor shows enhanced photoelectric energy conversion, which shows great potential in the detection of sunlight intensity (20–100 mW cm−2). Simultaneously, building a hybrid pressure, acoustic and optical sensing platform and validating its superior performance in human movement and health monitoring applications. Furthermore, a strategy of light intensity feedback system to guide personalized health activities is put forward, so that doctors can guide patients/elderly health activities remotely.

Refractive Index Resolved Imaging Enabled by Terahertz Time-Domain Spectroscopy Ellipsometry

AbstractMaterial characterization in the terahertz range is an interesting topic of research due to its great applications in material science, health monitoring, and security applications. Advances in terahertz generation, detection, and data acquisition have contributed to improved bandwidth, signal power, and signal-to-noise ratio. This enables advanced material characterization methods such as ellipsometry, which has been little explored in the terahertz frequency range, yet. Here, we introduce a comparison between material characterization with terahertz time-domain spectroscopy in transmission geometry and ellipsometry reflection geometry. Terahertz ellipsometry images were taken, showing spatially resolved refractive index estimation in the far field and higher image quality compared to single-polarization imaging.

An Innovative Deep Neural Network Model for Precise Calorie Burn Prediction from Physical Activity Data

Accurate prediction of calories burned during physical activities is crucial for various applications in health monitoring, fitness tracking, and personalized nutrition. Traditional methods often lack the precision needed for individualized estimates, which has increased interest in advanced machine learning approaches. This research introduces a deep learning model designed to predict calories burned with enhanced accuracy by capturing complex, non-linear relationships in the data. The model employs a multilayer perceptron neural network, Leaky ReLU activations, dropout regularization, and the Adam optimizer to improve generalizability and prevent overfitting. The evaluation of training and validation loss over epochs demonstrated the model's robustness and capacity to generalize effectively to novel data. The model's performance was evaluated using various metrics, achieving superior results with a remarkable Mean Absolute Error (MAE) of 0.27% and an accuracy of 99.73%, outperforming other models discussed in the literature. These findings indicate that deep learning offers significant potential for improving calorie prediction models, providing more reliable fitness and health management tools.

Path Planning Method and Control of Mobile Robot with Uncertain Dynamics Based on Improved Artificial Potential Field and Its Application in Health Monitoring

To enhance the navigation and control efficiency of mobile robots in the field of health monitoring, a novel path planning and control strategy for mobile robots with uncertain dynamics based on improved artificial potential fields is proposed in this paper. Specifically, we propose an attractive potential field rotation method to overcome the limitation that traditional artificial potential fields tend to fall into local minima. Then, we define a new class of attractive potential fields to address the goals non-reachable with obstacles nearby (GNRON) and collisions caused by excessive attractive force at long distances from the target point. Furthermore, a control law is proposed for the mobile robot with uncertain dynamics, and the stability of the closed-loop system is rigorously proven using the Lyapunov method. Finally, the feasibility and effectiveness of the proposed method are verified by simulations and experiments.

Autonomous Sensing Architected Materials

AbstractIntegrating autonomous sensing materials into future applications necessitates developing advanced multiscale multiphysics predictive models. This study introduces an experimentally informed predictive framework for autonomous sensing architected materials, combining theoretical and computational methodologies. By incorporating stress‐dependent electrical resistivity through anisotropic piezoresistive constitutive effects, alongside considering material, geometric, and contact nonlinearities, the proposed multiscale model captures the architecture‐dependent piezoresistive responses of lattice composites produced via additive manufacturing of polyetherimide (PEI)/carbon nanotube (CNT) nanoengineered feedstock. The PEI/CNT composite exhibits exceptional strength (105 MPa), stiffness (3368 MPa), and strain sensitivity (gauge factor ≈13), translating into remarkable piezoresistive characteristics for the PEI/CNT lattice composites, surpassing existing works (gauge factor ≈3 to 11). This multiscale finite element model accurately predicts both macroscopic piezoresistive responses and the influence of architectural and topological variations on electric current paths, validated via infrared thermography analysis. Additionally, an Ashby chart for the gauge factor of PEI/CNT lattice composites suggests their prediction through a scaling law similar to mechanical properties, underscoring the tunable strain and damage sensitivity of these materials. The combined experimental, theoretical, and numerical findings offer critical insights into optimizing piezoresistive composites through architected design, with profound implications for smart orthopedics, structural health monitoring, sensors, batteries, and other multifunctional applications.

Stretchable Sweat Lactate Sensor with Dual-Signal Read-Outs.

Innovations in wearable sweat sensors hold great promise to provide deeper insights into molecular level health information non-invasively. Lactate, a key metabolite present in sweat, holds immense significance in assessing physiological conditions and performance in sports physiology and health sensing. This paper presents the development and characterization of stretchable electrodes with ultrahigh active surface area of 648 % for lactate sensing. The as-printed stretchable electrodes were functionalized with an electron transfer layer comprising Toluidine Blue O and multi-walled carbon nanotubes (MWCNTs), and an enzymatic layer consisting of lactate dehydrogenase with β-Nicotinamide adenine dinucleotide as the cofactor for lactate selectivity. This sensor achieves a dual-signal read-out in which both electrochemical and fluorescence signals were obtained during lactate detection, demonstrating promising sensor performance in terms of sensitivity and reliability. We demonstrate the robustness of the dual-signal sensor under simulated conditions of physical deformation and shifted excitation. Under these compromised conditions, the performance of the stretchable electrodes remained largely unaffected, showcasing their potential for robust and adaptable sensing platforms in wearable health monitoring applications.