Health Monitoring Applications Research Articles

Janus Membrane-Based Wearable pH Sensor with Sweat Absorption, Gas Permeability, and Self-Adhesiveness.

Wearable biomedical sensors have enabled noninvasive and continuous physiological monitoring for daily health management and early detection of chronic diseases. Among biomedical sensors, wearable pH sensors attracted significant interest, as pH influences most biological reactions. However, conformable pH sensors that have sweat absorption ability, are self-adhesive to the skin, and are gas permeable remain largely unexplored. In this study, we present a pioneering approach to this problem by developing a Janus membrane-based pH sensor with self-adhesiveness on the skin. The sensor is composed of a hydrophobic polyurethane-polydimethylsiloxane porous hundreds nanometer-thick substrate and a hydrophilic poly(vinyl alcohol)-poly(acrylic acid) porous nanofiber layer. This Janus membrane exhibits a thickness of around 10 μm, providing a conformable adhesion to the skin. The simultaneous realization of solution absorption, gas permeability, and self-adhesiveness makes it suitable for long-term continuous monitoring without compromising the comfort of the wearer. The pH sensor was tested successfully for continuous monitoring for 7.5 h, demonstrating its potential for stable analysis of skin health conditions. The Janus membrane-based pH sensor holds significant promise for comprehensive skin health monitoring and wearable biomedical applications.

Three-dimensional electrochemical-magnetic-thermal coupling model for lithium-ion batteries and its application in battery health monitoring and fault diagnosis

Storage batteries with elevated energy density, superior safety and economic costs continues to escalate. Batteries can pose safety hazards due to internal short circuits, open circuits and other malfunctions during usage, hence real-time surveillance and error diagnosis of the battery’s operational state is imperative. In this paper, a three-dimensional model of electrochemical-magnetic field-thermal coupling is formulated with lithium-ion pouch cells as the research focus, and the spatial distribution pattern of the physical field such as magnetic field and temperature when the battery is operational is acquired. Furthermore, this manuscript also investigates the diagnostic methodology for defective batteries with internal short circuits and fissures, that is, the operational state of the battery is evaluated and diagnosed by the distribution of the magnetic field surrounding the battery. To substantiate the method’s practical viability, the present study extends its examination to the 18650-battery pack. We obtained the magnetic field images of the normal operation of the battery pack and the failure state of some batteries and analyzed the relationship between the magnetic field distribution characteristics and the performance of the battery pack, providing a new method for the health monitoring and fault diagnosis of the battery pack. This non-contact method incurs no damage to the battery, concurrently exhibiting elevated sensitivity and extremely rapid response time. Meanwhile, it provides an effective means for non-destructive research on the batteries and can be applied to areas such as battery safety screening and non-destructive testing. This research not only helps to facilitate our understanding of the battery’s operating mechanism, but also provides robust support for safe operation and optimal battery design.

3D printing-based lateral flow visual assay utilizing the dual-excitation property of nitrogen-doped carbon dots for the ratiometric determination and chemometric discrimination of flavonoids.

A ratiometric fluorescence sensor has been established based on dual-excitation carbon dots (D-CDs) for the detection of flavonoids (morin is chosen as the typical detecting model for flavonoids). D-CDs were prepared using microwave radiation with o-phenylenediamine and melamine and exhibit controllable dual-excitation behavior through the regulation of their concentration. Remarkably, the short-wavelength excitation of D-CDs can be quenched by morin owing to the inner filter effect, while the long-wavelength excitation remains insensitive, serving as the reference signal. This contributes to the successful design of an excitation-based ratiometric sensor. Based on the distinct and differentiated variation of excitation intensity, morin can be determined from 0.156 to 110µM with a low detection limit of 0.156µM. In addition, an intelligent and visually lateral flow sensing device is developed for the determination of morin content in real samples with satisfying recoveries, which indicates the potential application for human health monitoring.

Deep Residual Network with a CBAM Mechanism for the Recognition of Symmetric and Asymmetric Human Activity Using Wearable Sensors

Wearable devices are paramount in health monitoring applications since they provide contextual information to identify and recognize human activities. Although sensor-based human activity recognition (HAR) has been thoroughly examined, prior studies have yet to definitively differentiate between symmetric and asymmetric motions. Determining these movement patterns might provide a more profound understanding of assessing physical activity. The main objective of this research is to investigate the use of wearable motion sensors and deep convolutional neural networks in the analysis of symmetric and asymmetric activities. This study provides a new approach for classifying symmetric and asymmetric motions using a deep residual network incorporating channel and spatial convolutional block attention modules (CBAMs). Two publicly accessible benchmark HAR datasets, which consist of inertial measurements obtained from wrist-worn sensors, are used to assess the model’s efficacy. The model we have presented is subjected to thorough examination and demonstrates exceptional accuracy on both datasets. The ablation experiment examination also demonstrates noteworthy contributions from the residual mappings and CBAMs. The significance of recognizing basic movement symmetries in increasing sensor-based activity identification utilizing wearable devices is shown by the enhanced accuracy and F1-score, especially in asymmetric activities. The technique under consideration can provide activity monitoring with enhanced accuracy and detail, offering prospective advantages in diverse domains like customized healthcare, fitness tracking, and rehabilitation progress evaluation.

A metasurface for suppressing mode conversion and achieving directional reflection of shear horizontal wave

The lowest-order shear horizontal (SH0) guided wave in a plate is crucial for nondestructive testing and structural health monitoring due to its nondispersive nature. However, mode conversion at the interface for the SH0 wave complicates the received information and increases the difficulty of signal analysis. In this work, an elastic metasurface is proposed to suppress mode conversion at the interface and to reflect the SH0 wave directionally. The metasurface comprises a series of rectangular elements with a specific phase gradient covering the 2π phase range. The effectiveness of the proposed metasurface is confirmed through finite element simulations and experimental validation. The results indicate that the metasurface can significantly reduce the amplitude of the reflected lowest-order symmetric Lamb wave when compared to the reflection without the metasurface, while simultaneously allowing the reflected SH0 wave to propagate at the designed angle. Furthermore, the metasurface maintains its functionality for SH0 wave with incident angles varying from 0° to 25°. This study provides a viable approach for suppressing mode conversion and modulating the reflection behavior of SH0 wave, which is expected to find applications in nondestructive testing and structural health monitoring.

Double-sided microstructured flexible iontronic pressure sensor with wide linear sensing range

Iontronic pressure sensors have garnered significant attention for their potential in wearable electronic devices. While simple microstructures can enhance sensor sensitivity, the majority of them predominantly amplify sensitivity at lower pressure ranges and fail to enhance sensitivity at higher pressure ranges, leading to nonlinearity. In the absence of linear sensitivity in a pressure sensor, users are unable to derive precise information from its output, necessitating further signal processing. Hence, crafting a linearity flexible pressure sensor through a straightforward approach remains a formidable task. Herein, a double-sided microstructured flexible iontronic pressure sensor is presented with wide linear sensing range. The ionic gel is made by 1-Ethyl-3-methylimidazolium bis(tri-fluoromethylsulfonyl)imide (EMIM:TFSI) into the matrix of poly(vinylidene fluoride-co-hexafluoropropylene) (PVDF-HFP), which acts as active layer, featuring irregular microstructures (IMS) and pyramid microstructures (PMS) on both sides. Unlike previous complex methods, IMS and uniform PMS are easily and achieved through pattern transfer from a sandpaper mold and micro-pyramid template. The iontronic pressure sensor exhibits exceptional signal linearity with R2 values of 0.9975 and 0.9985, in the wide pressure range from 100 to 760kPa and 760 kPa to 1000 kPa, respectively. This outstanding linearity and wide sensing range stem from a delicate balance between microstructure compression and mechanical alignment at the ionic gel interface. This study provides valuable insights into achieving linear responses by strategically designing microstructures in flexible pressure sensors, with potential applications in intelligent robots and health monitoring.

Fundamental frequency formulation and modeling of masonry slender structures: A comparative study of machine learning and regression techniques

This paper presents a novel method for determining the fundamental frequency and modeling slender masonry structures, which is particularly relevant in the context of structural dynamics. This research evaluates the effectiveness of machine learning techniques, specifically artificial neural networks (ANN), and regression methods, including multiple linear regression (MLR) and multiple nonlinear regression (MNLR), in predicting the fundamental frequency of structures, considering both their geometrical and mechanical properties. The objective is to provide a comprehensive comparison of these methods, emphasizing their benefits, limitations, and potential applications in structural health monitoring. The ANN model demonstrated superior performance to linear and non linear regression methods. The findings could significantly impact both theoretical understanding and practical applications in this field. Moreover, this work paves the way for future research, potentially leading to the creation of more accurate and efficient predictive models for slender masonry structures.This methodology ensures a thorough understanding of the model’s behavior and its responsiveness to variations in input parameters.

Bio-inspired flexible optical micro/nano fibers sensor with nanohemisphere array microstructure for ultra-sensitive detection of skin signals

We propose the utilization of a bio-inspired nanohemispherical array of microstructure flexible optical micro/nanofibers sensor (NHSAMNFS) designed for highly sensitive detection of epidermal signals and cardiovascular health assessment. NHSAMNFS builds a flexible wearable self-mixing optical sensor by assembling MNF in a polydimethylsiloxane (PDMS) film with a surface modified nanohemispheres (400 nm diameter) array, where the micro/nanofiber (MNF) sensor is used to sense physiological signals. PDMS with a surface modified nano-hemispherical array is used to eliminate misalignment and increase the sensitivity of sensing pressure. Due to the high flexibility of PDMS and its surface modified nano-hemispherical array microstructure, the sensing element can reliably transmit pressure stimuli into interpretable optical signals with high fidelity, allowing instantaneous pressure perception. The sensor, measuring 20×20mm in size, boasts a sensitivity of 0.102mV/kPa, with a rise time of 3.7 ms and a fall time of 6.26 ms. By tracking fluctuations in the reflected light power of MNF within PDMS, real-time monitoring of the fingertip pulse wave is achieved. Extracting cardiovascular vital signs from this pulse signal enables the evaluation of cardiovascular health status, yielding results consistent with human physiological characteristics. This non-invasive, continuous, and precise cardiovascular health monitoring, facilitated by wearable devices, holds significant promise for applications in human health monitoring, as well as in the prevention and diagnosis of cardiovascular diseases.

Notch corrosion fatigue behaviour and simplified strain energy density-based notch fatigue life assessment of Grade-A shipbuilding steel

Corrosion fatigue is a key challenge in lifecycle management of ship hull structures. Although sea wave fatigue stresses are linear-elastic, highly-damaging elastic–plastic conditions occur at geometrical discontinuities or notches. Estimation of notch stress-strains to characterize notch fatigue behaviour conventionally requires large computational efforts. Also, constant strain energy density (SED), as a driving force, is far more effective in realistic applications versus constant stress and strain fatigue life approaches. Identified research gaps were the development of a simplified SED approach for fatigue life assessment of hole-notched structures. Fatigue studies were conducted on hole-notched specimens of Grade-A shipbuilding steel in air and corrosion mediums. Strong synergistic interactions of corrosion and fatigue damage mechanisms were studied by scanning electron microscope (SEM) fractography. Notch corrosion fatigue life was reduced by 70% from notch air fatigue life due to environmental effect. Two new parameters are proposed to improve the efficiency of notch fatigue life assessment, namely “fatigue notch strain energy density concentration factor (K_SED) and “fatigue notch toughness”. This improved fatigue life assessment method was demonstrated as equally reliable but much simpler to implement, eliminating the complex computational analysis. This improved method has promising applications in real-time structural health monitoring (SHM) of ship hull structures.

Efficiency comparison of MCMC and Transport Map Bayesian posterior estimation for structural health monitoring

In this paper, an alternative to solving Bayesian inverse problems for structural health monitoring based on a variational formulation with so-called transport maps is examined. The Bayesian inverse formulation is a widely used tool in structural health monitoring applications. While Markov Chain Monte Carlo (MCMC) methods are often implemented in these settings, they come with the problem of using many model evaluations, which in turn can become quite costly. We focus here on recent developments in the field of transport theory, where the problem is formulated as finding a deterministic, invertible mapping between some easy to evaluate reference density and the posterior. The resulting variational formulation can be solved with integration and optimization methods. We develop a general formulation for the application of transport maps to vibration-based structural health monitoring. Further, we study influences of different integration approaches on the efficiency and accuracy of the transport map approach and compare it to the Transitional MCMC algorithm, a widely used method for structural identification. Both methods are applied to a lower-dimensional dynamic model with uni- and multi-modal properties, as well as to a higher-dimensional neural network surrogate system of an airplane structure. We find that transport maps have a significant increase in accuracy and efficiency, when used in the right circumstances.

Innovative welding integration of acousto-ultrasonic composite transducers onto thermoplastic composite structures

Acousto-ultrasonic composite transducers (AUCTs), comprising piezoceramic materials in a reinforced polymeric matrix, show promise for structural health monitoring in composite structures. Challenges arise when integrating AUCTs onto highly loaded thermoplastic composites, especially low-surface-energy materials like polyaryletherketone composites. To address this, the study explores the viability of attaching AUCTs to low-melting polyaryletherketone carbon fiber-reinforced thermoplastic composite structures using ultrasonic welding. This welding technique forms a joint where the interface material fuses with the AUCT embedment and the structure matrix, providing a reliable and automatable process. The investigation includes a comparative analysis of an ultrasonic welded joint with an external energy director and a reference AUCT system integrated using a vacuum bagging oven procedure. Results highlight the potential of AUCT configurations integrated by ultrasonic welding as an alternative solution, acknowledging challenges that persist for further development and increased reliability in structural health monitoring applications.

Recent advances of ECG monitoring and webserver health monitoring applications: A review

In response to recent improvements in mobile technology and cloud computing-based real-time monitoring systems, numerous cloud-based healthcare services and devices have been developed. In the cloud system, it is feasible to collect and distribute medical data immediately from anywhere to medical specialists, and patients can receive network-based feedback. This paper reviews the most recent developments and trends in wearable technology and biomedical equipment (mostly ECG), as well as the development of a cloud-based solution for mobile device and web browser users. The system contains ECG enhancement, ECG quality grading, and ECG parameter extraction methods. A use case was utilized to showcase the system, in which a web server processed ECG data from a mobile phone in real-time. The system has been proved to be functional, accurate, and efficient. We review the primary principles underlying the operation of the ECG and its fundamental operation, as well as the recording and transmission paradigm for ECG signals. Detailed processes were also provided for the system components, their configuration, and the employed technologies. In conclusion, we examine the complete study and the project's future scope.

Performance Assessment for the Validation of Wireless Communication Engines in an Innovative Wearable Monitoring Platform.

In today's health-monitoring applications, there is a growing demand for wireless and wearable acquisition platforms capable of simultaneously gathering multiple bio-signals from multiple body areas. These systems require well-structured software architectures, both to keep different wireless sensing nodes synchronized each other and to flush collected data towards an external gateway. This paper presents a quantitative analysis aimed at validating both the wireless synchronization task (implemented with a custom protocol) and the data transmission task (implemented with the BLE protocol) in a prototype wearable monitoring platform. We evaluated seven frequencies for exchanging synchronization packets (10 Hz, 20 Hz, 30 Hz, 40 Hz, 50 Hz, 60 Hz, 70 Hz) as well as two different BLE configurations (with and without the implementation of a dynamic adaptation of the BLE Connection Interval parameter). Additionally, we tested BLE data transmission performance in five different use case scenarios. As a result, we achieved the optimal performance in the synchronization task (1.18 ticks as median synchronization delay with a Min-Max range of 1.60 ticks and an Interquartile range (IQR) of 0.42 ticks) when exploiting a synchronization frequency of 40 Hz and the dynamic adaptation of the Connection Interval. Moreover, BLE data transmission proved to be significantly more efficient with shorter distances between the communicating nodes, growing worse by 30.5% beyond 8 m. In summary, this study suggests the best-performing network configurations to enhance the synchronization task of the prototype platform under analysis, as well as quantitative details on the best placement of data collectors.

Label-free SPR fiber microfluidic sensor with PDA-MA cross-linked lipase for specific detection of tributyrin

As a precursor to butyric acid and also a short-chain triglyceride, tributyrin plays an important role in repairing small intestinal villi, promoting the absorption and utilization of nutrients, and monitoring human health. The traditional carboxylation functionalization method of optical fiber sensors takes a long time and is complicated to operate. Therefore, we need a sensor with a functional period, high sensitivity, and the ability to quickly detect samples to achieve specific detection of tributyrin. In this paper, based on the chemical cross-linking method of polydopamine-maleic acid (PDA-MA), a novel fiber-optic microfluidic surface plasmon resonance (SPR) sensor was proposed to realize the detection of tributyrin. Compared with the traditional functionalization method, the fixation method of PDA-MA chemical cross-linking method not only shortens the functionalization time, but also increases the sensitivity of the sensor. Experimental results show that the sensitivity of the microfluidic sensor coated with PDA reaches 3392.7 nm/RIU, which is 1.75 times that of the uncoated sensor. The sensor has a sensitivity of 8.22 nm/mM in the range of 0.5–5 mM and a response time of only 8 min at the concentration of 1 mM. In addition, the sample detection error of the sensor is within 12 % in the range of 0.5–5 mM. The proposed sensor offers the advantages of low sample consumption (100 μL), high sensitivity, good selectivity, good repeatability, anti-interference ability, and label-free measurement. The sensor holds promising applications in food safety and health monitoring due to its superior performance.

Batch fabrication of ultrathin flexible pressure sensors enabled by full printed technique

Flexible pressure sensors have gained significant attention in recent years owing to their versatile applications in health monitoring, human-machine interfaces, and artificial robotics. Geometric microengineering of the active layer, including micropatterned structures, porous layers, and multilayered packed structures has been investigated to improve the sensing performance. However, accompanying challenges such as increased thickness and difficulties in achieving mass production still persist. This work presents a full printed technique for fabricating high-performance piezoresistive pressure sensors. Thermal-treated graphene oxide (TGO) colloid was selected as the printing ink. Combined with an in-situ chemical foaming strategy, structural microengineering of the piezoresistive layer was achieved. The obtained hierarchical protruding micro-domes endow the piezoresistive pressure sensors with a wide sensing range of 550 kPa and ultrathin features (18 μm). In addition, the full-printed manufacturing method enables the batch-producible fabrication of pressure sensors. These sensors can be employed for human motion detection and pressure mapping applications.

Recent progress of optical tactile sensors: A review

Over the past decade, flexible electronics has become a cutting-edge technology owing to its broad applications in robotics, health monitoring, and human-machine interface. The recent development of flexible electrical tactile sensor triggers the research of optical tactile sensors due to their potentials in high-sensitivity, high-precision, low power consumption, and anti-electromagnetic interference. Here, this review mainly discusses the advances and applications of silica fiber, polymer fiber, hydrogel, and micro/nanofiber based tactile sensors, specifically, focusing on the latest research results and on the necessary technologies to enable the health monitoring, robotics, and human-machine interface applications. Furthermore, future outlooks in developing skin-like optical tactile sensors are also presented.

Bamboo structures: Innovative methods and applications for structural health monitoring and dissemination

Bamboo is gaining more and more attention in the field of building materials thanks to its unique characteristics, such as resistance, lightness, sustainability, and flexibility. However, the widespread adoption of bamboo as a building material presents some challenges, especially in relation to the durability of the material. In fact, being a natural material, it requires protection from atmospheric agents, such as rain and sun, as well as from attacks by insects and animals. Ensuring the longevity of bamboo involves implementing tailored treatments, precise precautions, and regular structural inspections to detect any potential degradation over time. In this context, the article focused on the monitoring of a structure made of bamboo and on the testing of an innovative system to identify the structural critical issues present, in the context of a FISR project which envisaged a cultural exchange between China and Italy. Starting from the survey of the structure carried out with a drone, a 3D model was then built using a commercial software. For the monitoring phase of the structure, an innovative system was used that exploits the YOLO v5s6 algorithm to identify structural critical issues, supported by a Virtual/Augmented/Mixed reality app developed by the authors. This app is useful not only during the monitoring phase, but also in the subsequent phase of disseminating information relating to the potential of the use of bamboo in the field of structural engineering. The tested and proposed methodologies have proven to be particularly useful and high-performance, especially regarding the monitoring of bamboo structures, in relation to the resolution of problems linked to their possible fragility and limitations in certain application areas. The contribution provided in this paper by Geomatics methodologies is evident to better highlight the potential of this eco-friendly material for its desirable ever-increasing use in the construction sector by becoming aware of its potential.

Ethical and legal aspects of cybersecurity in health

Background: With the emergence of eHealth and mHealth, the use of mental health apps has increased significantly as an accessible and convenient approach as an adjunct to promoting well-being and mental health. There are several apps available that can assist with mental health monitoring and management, each with specific features to meet different needs. The intersection of mental health and cyber technology presents a number of critical legal and ethical issues. As mental health monitoring apps and devices become more integrated into clinical practice, cybersecurity takes on paramount importance. Objective: To address the ethical and legal aspects of health cybersecurity related to applications in mental health monitoring and management. Methods: We carried out a thematic synthesis of the best scientific evidence. Results: These tools have the potential to significantly improve access to and quality of care for users with mental health conditions, but they also raise substantial concerns about privacy and informed consent. Cybersecurity in mental health is not only a matter of technology, but also of human rights. The protection of sensitive mental health information is critical, and legal and ethical measures to safeguard this information must be implemented in a robust and transparent manner. Conclusion: the use of information technologies and mobile devices is now part of the clinical reality and its future perspectives. It is important to mention that while these apps can be helpful for self-care and mental well-being management, they are not a substitute for the advice and support of a qualified mental health professional (psychologist or psychiatrist). As we move into the digital age, it is imperative that mental health monitoring and management apps are developed and used responsibly, ensuring the safety, dignity, and well-being of users.

Improved hierarchical Bayesian modeling framework with arbitrary polynomial chaos for probabilistic model updating

Bayesian finite element model updating techniques have found widespread application in the structural health monitoring. The conventional Bayesian modeling framework (CBMF) identifies the posterior distribution of structural parameters using data from a single experiment. However, structural parameters exhibit variability due to changes in environmental or experimental conditions, an aspect overlooked by CBMF. In recent years, the hierarchical Bayesian modeling framework (HBMF) has gradually attracted attention. This framework incorporates an additional layer of hyperparameters and utilizes multiple sets of experimental data to describe the variability in structural parameters caused by changes in experimental conditions. Unfortunately, the increase in hyperparameters and data size leads the HBMF to face the dilemma of exceedingly high computational costs. To address this issue, this study proposes an improved hierarchical Bayesian modeling framework (IHBMF). To enhance the computational efficiency of IHBMF, the importance sampling method is employed to simplify the computation of the likelihood function for hyperparameters. Subsequently, a surrogate model based on arbitrary polynomial chaos expansion is introduced to further reduce the computational cost of IHBMF. The framework's accuracy and efficiency were validated through analysis of a simply supported beam and a steel pedestrian bridge. The results demonstrate that IHBMF not only delivers a more accurate quantification of structural parameter uncertainty than CBMF but also exhibits higher computational efficiency compared to HBMF, showcasing its superior capability in structural health monitoring applications.

Flexible piezoelectric sensor based on PAN/MXene/PDA@ZnO composite film for human health and motion detection with fast response and highly sensitive

Polyacrylonitrile (PAN) nanofiber-based flexible piezoelectric sensors are recognized for their applicability in wearable electronic devices, personal health monitoring, and motion detection. This research explores the impact of dual fillers, MXene and polydopamine-modified zinc oxide (PDA@ZnO), on the characteristics of PAN-based piezoelectric composite nanofiber films, including planar zigzag conformation content, dielectric, ferroelectric, piezoelectric, and mechanical properties. By integrating PDA@ZnO as a filler, PAN/MXene/PDA@ZnO-5 (PMPO) piezoelectric sensors were developed, showcasing enhanced piezoelectric sensing capabilities. These sensors achieved a sensitivity of 28.56 V/N across a broad linear range, with superior mechanical stability and durability across 3000 loading–unloading cycles, and exhibited swift response and recovery times of 49 ms and 40 ms, respectively. Furthermore, their unique fiber structure and flexibility, when interfaced with the human body, enable the detection of minute physiological movements and the conversion of varied motions into distinct voltage outputs. Thus, these sensors offer considerable promise for applications in human health monitoring and motion posture correction.