Sensors For Health Monitoring Research Articles

A low-power AE b-value sensor for structural health monitoring

An increase in the quantity of aging infrastructure has become a major social issue. It is thus necessary to properly maintain and manage aging infrastructure, which incurs increasingly large maintenance costs. Therefore, there is a demand for maintenance and management using low-cost and wide-application technologies such as Internet of Things (IoT). We propose a unique, ultra-low-power acoustic emission b-value sensor for structural health monitoring. Acoustic emission (AE) is an elastic wave generated by crack generation or growth inside a material. The characteristics of AE, such as waveform, source frequency, source location, and velocity, can indicate damage affecting material strength. When focusing on the source frequency, the b-value, which is the slope of AE amplitude-frequency distribution derived from the source frequency, is important and widely used as an indicator of material integrity. The proposed sensor was developed to detect this b-value trend more conveniently than ever and to achieve continuous structural or machinery health monitoring. We pursued two key points: ultra-low power consumption and long-distance wireless transmission, which enabled the AE sensor to be applied as an IoT sensor. The wake-on-AE algorithm, which wakes the sensor when AE waves reach the sensor, enables AE sensing to consume a very low current of 172μA on two dry batteries. Generally, the waveform of AE is very short in duration, such as microseconds to milliseconds. If the sensor did not wake up and take measurements in such a short time, the AE wave would pass, and the sensor would fail to measure AE. Therefore, we developed a unique analog- digital co-circuit technology that uses a peak-hold circuit to extract AE features with less sampling. In addition, sensor edges reduce data rates through statistical analysis and transmit data using low-power wide-area networks. In this paper, we present validation results obtained with the sensor on bearings. We performed validation experiments with faulty and normal bearings, generating AEs with different b-values. The developed sensor, operated with two dry batteries, and the AE measurement systems for verification simultaneously acquired the AE generated by the bearing rotation. As a result, we successfully confirmed that the developed sensor provides almost the same results as conventional AE systems in b-value measurement with ultra-low power consumption.

Imaging of the vascular distribution of the outer ear using optical coherence tomography angiography for highly accurate positioning of a hearable sensor.

Novel hearable technology is securely and comfortably positioned within the ear canal minimizing inaccuracies caused by accessory movements during activities. Despite extensive research on hearable technologies within the outer ear, there is a lack of research in the field of vascular imaging and quantitative analysis in the outer ear in vivo, which is one of the crucial factors to select the appropriate sensor position. Therefore, in this paper, we introduced optical coherence tomography angiography (OCTA)-based qualitative and quantitative analyses to visualize the inner vasculature of the outer ear to acquire vascular maps for microvascular assessments in vivo. By generating maximum amplitude projection images from three-dimensional blood vascular volume, we identified variations of blood vessel signal caused by the different biological characteristics and curvature of the ear among individuals. The performance of micro-vascular mapping using the proposed method was validated through the comparison and analysis of individual vascular parameters using extracted 20 vascular-related variables. In addition, we extracted pulsatile blood flow signals, demonstrating its potential to provide photoplethysmographic signals and ear blood maps simultaneously. Therefore, our proposed OCTA-based method for ear vascular mapping successfully provides quantitative information about ear vasculature, which is potentially used for determining the position of system-on-chip sensors for health monitoring in hearable devices.

Wearable Volatile Organic Compound Sensors for Plant Health Monitoring

Wearable Volatile Organic Compound Sensors for Plant Health Monitoring

Exploring the Potential of Promising Sensor Technologies for Concrete Structural Health Monitoring.

Structural health monitoring (SHM) is crucial for maintaining concrete infrastructure. The data collected by these sensors are processed and analyzed using various analysis tools under different loadings and exposure to external conditions. Sensor-based investigation on concrete has been carried out for technologies used for designing structural health monitoring sensors. A Sensor-Infused Structural Analysis such as interfacial bond-slip model, corroded steel bar, fiber-optic sensors, carbon black and polypropylene fiber, concrete cracks, concrete carbonation, strain transfer model, and vibrational-based monitor. The compressive strength (CS) and split tensile strength (STS) values of the analyzed material fall within a range from 26 to 36 MPa and from 2 to 3 MPa, respectively. The material being studied has a range of flexural strength (FS) and density values that fall between 4.5 and 7 MPa and between 2250 and 2550 kg/m3. The average squared difference between the predicted and actual compressive strength values was found to be 4.405. With cement ratios of 0.3, 0.4, and 0.5, the shear strength value ranged from 4.4 to 5.6 MPa. The maximum shear strength was observed for a water-cement ratio of 0.4, with 5.5 MPa, followed by a water-cement ratio of 0.3, with 5 MPa. Optimizing the water-cement ratio achieves robust concrete (at 0.50), while a lower ratio may hinder strength (at 0.30). PZT sensors and stress-wave measurements aid in the precise structural monitoring, enhanced by steel fibers and carbon black, for improved sensitivity and mechanical properties. These findings incorporate a wide range of applications, including crack detection; strain and deformation analysis; and monitoring of temperature, moisture, and corrosion. This review pioneers sensor technology for concrete monitoring (Goal 9), urban safety (Goal 11), climate resilience (Goal 13), coastal preservation (Goal 14), and habitat protection (Goal 15) of the United Nations' Sustainable Development Goals.

Advancements in Polymer-Assisted Layer-by-Layer Fabrication of Wearable Sensors for Health Monitoring.

Recent advancements in polymer-assisted layer-by-layer (LbL) fabrication have revolutionized the development of wearable sensors for health monitoring. LbL self-assembly has emerged as a powerful and versatile technique for creating conformal, flexible, and multi-functional films on various substrates, making it particularly suitable for fabricating wearable sensors. The incorporation of polymers, both natural and synthetic, has played a crucial role in enhancing the performance, stability, and biocompatibility of these sensors. This review provides a comprehensive overview of the principles of LbL self-assembly, the role of polymers in sensor fabrication, and the various types of LbL-fabricated wearable sensors for physical, chemical, and biological sensing. The applications of these sensors in continuous health monitoring, disease diagnosis, and management are discussed in detail, highlighting their potential to revolutionize personalized healthcare. Despite significant progress, challenges related to long-term stability, biocompatibility, data acquisition, and large-scale manufacturing are still to be addressed, providing insights into future research directions. With continued advancements in polymer-assisted LbL fabrication and related fields, wearable sensors are poised to improve the quality of life for individuals worldwide.

Cellulose hydrogels with high response sensitivity and mechanical adaptability for flexible strain sensor and triboelectric nanogenerator

Cellulose hydrogels are widely regarded as green and environmentally friendly flexible electronic materials. Herein, a novel multiple cross-linked cellulose hydrogel (MCC/SA20/MCC-g-P(AA-co-AM-AMPS)) with high sensitivity (gauge factor = 440, 670 %, 60 ms) and good mechanical adaptability was designed and prepared. The prepolymer (MCC-g-P(AA-co-AM-AMPS)) was introduced into the microcrystalline cellulose/sodium alginate (MCC/SA) hydrogel network to form a multiple cross-linked network structure, which effectively enhanced the tension and elasticity of the cross-linked network, and improved the mechanical adaptability and sensitivity of the hydrogel. Additionally, it was assembled as a strain sensor that can accurately identify and monitor various motion states of the human body. It was also assembled as a friction nanogenerator (TENG) that continuously generates a stable open circuit voltage (7.2 V) for self-powering small electronic devices. This study provides an effective and feasible strategy for cellulose hydrogels in flexible sensors for sports health monitoring, sustainable green energy harvesting, and self-powering applications for small electronic devices.

Soldier Health and Position Tracking System

The Soldier Health and Position Tracking System (SHRYS) is an integrated technological solution designed to address the multifaceted challenges associated with monitoring and ensuring the well-being of military personnel in dynamic and unpredictable operational environments. This system amalgamates state-of-the-art wearable devices, GPS technology, and health monitoring sensors to provide comprehensive real-time insights into soldiers' physical conditions and locations. The wearable devices continuously collect vital health metrics such as heart rate, body temperature, and activity levels, fostering a proactive approach to healthcare management. Simultaneously, the GPS technology enables precise and continuous tracking of soldiers' positions on the battlefield. The collected data is then transmitted to a centralized monitoring system, where advanced analytics and machine learning algorithms assess soldiers' health trends, detect anomalies, and facilitate timely medical interventions. The SHPTS aims to enhance the overall situational awareness of military commanders, optimize resource allocation, and ultimately ensure the optimal health and safety of soldiers deployed in challenging and high-risk scenarios. This innovative system stands at the forefront of leveraging technology to safeguard the well-being and operational effectiveness of military personnel.

On-demand fabrication of piezoelectric sensors for in-space structural health monitoring

Inflatable structures, promising for future deep space exploration missions, are vulnerable to damage from micrometeoroid and orbital debris impacts. Polyvinylidene fluoride-trifluoroethylene (PVDF-trFE) is a flexible, biocompatible, and chemical-resistant material capable of detecting impact forces due to its piezoelectric properties. This study used a state-of-the-art material extrusion system that has been validated for in-space manufacturing, to facilitate fast-prototyping of consistent and uniform PVDF-trFE films. By systematically investigating ink synthesis, printer settings, and post-processing conditions, this research established a comprehensive understanding of the process-structure-property relationship of printed PVDF-trFE. Consequently, this study consistently achieved the printing of PVDF-trFE films with a thickness of around 40 µm, accompanied by an impressive piezoelectric coefficient of up to 25 pC N−1. Additionally, an all-printed dynamic force sensor, featuring a sensitivity of 1.18 V N−1, was produced by mix printing commercial electrically-conductive silver inks with the customized PVDF-trFE inks. This pioneering on-demand fabrication technique for PVDF-trFE films empowers future astronauts to design and manufacture piezoelectric sensors while in space, thereby significantly enhancing the affordability and sustainability of deep space exploration missions.

Wearable Self‐Powered Smart Sensors Enabled by 2D Flower‐Like Ni–Co Layered Double Hydroxides for Copper Monitoring in Sweat

AbstractThe growing power demands of wearable electronic devices have stimulated the development of self‐powered systems in noninvasive healthcare. Herein, ultrathin homogeneous 2D flower‐like nickel–cobalt layered double hydroxide (Ni–Co LDH) nanocomposites are prepared by a one‐step hydrothermal method to integrate a self‐powered system for wearable sweat copper ions (Cu2+) monitoring. The Ni–Co LDH nanocomposites with a lamellar structure and numerous active sites, exhibit sensitive selectivity and low detection limits for Cu2+. In addition, the optimized Ni–Co LDH provides favorable capacitive characteristics as supercapacitors for self‐powering sensors, ensuring continuous sensing energy sources. By connecting wirelessly to a smart device, a self‐powered system is constructed to achieve wearable sensing of Cu2+ in the practical application of sweat. This simple yet effective strategy provides new opportunities for the development of self‐powering health monitoring sensors, as well as smartsensing more heavy metals.

Research Progress and Application of Multimodal Flexible Sensors for Electronic Skin.

In recent years, wearable electronic skin has garnered significant attention due to its broad range of applications in various fields, including personal health monitoring, human motion perception, human-computer interaction, and flexible display. The flexible multimodal sensor, as the core component of electronic skin, can mimic the multistimulus sensing ability of human skin, which is highly significant for the development of the next generation of electronic devices. This paper provides a summary of the latest advancements in multimodal sensors that possess two or more response capabilities (such as force, temperature, humidity, etc.) simultaneously. It explores the relationship between materials and multiple sensing capabilities, focusing on both active materials that are the same and different. The paper also discusses the preparation methods, device structures, and sensing properties of these sensors. Furthermore, it introduces the applications of multimodal sensors in human motion and health monitoring, as well as intelligent robots. Finally, the current limitations and future challenges of multimodal sensors will be presented.

Flexible Broadband Organic Photodetectors with Ternary Planar-Mixed Heterojunction Semiconductors and Solution-Processed Polymeric Electrode.

Flexible organic photodetectors (OPDs) hold immense promise in health monitoring sensors, flexible imaging sensors, and portable optical communication. Nevertheless, the actualization of high-performance flexible electronics has been hindered by rigid electrodes such as metals or metal oxides. In this work, we constructed a flexible broadband organic photodetector using a solution-processed polymeric electrode, which exhibits flexibility surpassing that of conventional indium tin oxide (ITO) electrodes. Additionally, we employed a planar-mixed heterojunction (PMHJ) through a sequential deposition method and introduced PC71BM as the third constituent into the PM6/Y6 binary active layer, resulting in enhanced photodetection performance and a broadend spectral range. The optimized OPDs demonstrated remarkable detectivity (D*) exceeding 1012 Jones in brodband from 300 to 900 nm, with a champion D* of 6.31 × 1012 Jones at 790 nm. Furthermore, after undergoing 500 cycles of bending, the D* retained approximately 78% of its original performance, highlighting the outstanding mechanical stability. This work presents a promising pathway toward the development of flexible broadband OPDs using a straightforward method, offering enhanced compatibility in diverse application scenarios and propelling the frontier of flexible optoelectronic research.

Optimisation and Calibration of Bayesian Neural Network for Probabilistic Prediction of Biogas Performance in an Anaerobic Lagoon.

This study aims to enhance diagnostic capabilities for optimising the performance of the anaerobic sewage treatment lagoon at Melbourne Water's Western Treatment Plant (WTP) through a novel machine learning (ML)-based monitoring strategy. This strategy employs ML to make accurate probabilistic predictions of biogas performance by leveraging diverse real-life operational and inspection sensor and other measurement data for asset management, decision making, and structural health monitoring (SHM). The paper commences with data analysis and preprocessing of complex irregular datasets to facilitate efficient learning in an artificial neural network. Subsequently, a Bayesian mixture density neural network model incorporating an attention-based mechanism in bidirectional long short-term memory (BiLSTM) was developed. This probabilistic approach uses a distribution output layer based on the Gaussian mixture model and Monte Carlo (MC) dropout technique in estimating data and model uncertainties, respectively. Furthermore, systematic hyperparameter optimisation revealed that the optimised model achieved a negative log-likelihood (NLL) of 0.074, significantly outperforming other configurations. It achieved an accuracy approximately 9 times greater than the average model performance (NLL = 0.753) and 22 times greater than the worst performing model (NLL = 1.677). Key factors influencing the model's accuracy, such as the input window size and the number of hidden units in the BiLSTM layer, were identified, while the number of neurons in the fully connected layer was found to have no significant impact on accuracy. Moreover, model calibration using the expected calibration error was performed to correct the model's predictive uncertainty. The findings suggest that the inherent data significantly contribute to the overall uncertainty of the model, highlighting the need for more high-quality data to enhance learning. This study lays the groundwork for applying ML in transforming high-value assets into intelligent structures and has broader implications for ML in asset management, SHM applications, and renewable energy sectors.

Nanotechnology-based Sensors for Real-time Monitoring and Assessment of Soil Health and Quality: A Review

Soil health and quality are critical factors in maintaining sustainable agriculture, ecosystem stability, and global food security. Conventional methods for assessing soil properties are often time-consuming, labor-intensive, and lack real-time monitoring capabilities. Nanotechnology has emerged as a promising approach to develop advanced sensors for rapid, in-situ, and continuous monitoring of soil health parameters. This comprehensive review discusses the recent advancements in nanotechnology-based sensors for soil health assessment, their working principles, applications, challenges, and future prospects. We highlight the potential of various nanomaterials, such as carbon nanotubes, graphene, metal oxide nanoparticles, and quantum dots, in fabricating highly sensitive, selective, and robust soil sensors. The integration of these nanosensors with wireless communication technologies and data analytics enables real-time monitoring and precision agriculture practices. Furthermore, we discuss the environmental and ecological implications of deploying nanosensors in soil and the need for standardized protocols and regulations. This review provides valuable insights into the current state-of-the-art and future directions of nanotechnology-based sensors for soil health monitoring, promoting sustainable agriculture and environmental management.

Membrane-based optical fiber Bragg grating pressure sensor for health monitoring of pile foundations

A fiber Bragg grating (FBG) pressure sensor is proposed, designed, and fabricated for lateral earth pressure sensing, in which the FBG sensor is mounted on a 3D printed trestle structure combined with a membrane. The applied pressure can cause a deformation on the membrane, and then this deformation applied on the trestle structure causes tensile strain on the FBG. The proposed sensor is functionalized as a high-sensitive pressure transducer capable of converting the pressure into strain on the FBG. Here, the performance of the proposed sensor is numerically and experimentally investigated. The results show that the pressure sensitivity at 30°C is 10.62 pm/kPa within a range of 0–0.6 MPa. Due to the thermal expansion of the structure, the pressure sensitivity coefficient decreases with the increase of temperature; however, the cross effect between the temperature and strain on the sensing sensitivity is investigated and can be eliminated. The fabricated sensor has advantages of high sensitivity, good stability, and high pressure resolution, so it has potential in the field of structural health monitoring.

Laser-Induced Graphene-Based Sensors in Health Monitoring: Progress, Sensing Mechanisms, and Applications.

The rising global population and improved living standards have led to an alarming increase in non-communicable diseases, notably cardiovascular and chronic respiratory diseases, posing a severe threat to human health. Wearable sensing devices, utilizing micro-sensing technology for real-time monitoring, have emerged as promising tools for disease prevention. Among various sensing platforms, graphene-based sensors have shown exceptional performance in the field of micro-sensing. Laser-induced graphene (LIG) technology, a cost-effective and facile method for graphene preparation, has gained particular attention. By converting polymer films directly into patterned graphene materials at ambient temperature and pressure, LIG offers a convenient and environmentally friendly alternative to traditional methods, opening up innovative possibilities for electronic device fabrication. Integrating LIG-based sensors into health monitoring systems holds the potential to revolutionize health management. To commemorate the tenth anniversary of the discovery of LIG, this work provides a comprehensive overview of LIG's evolution and the progress of LIG-based sensors. Delving into the diverse sensing mechanisms of LIG-based sensors, recent research advances in the domain of health monitoring are explored. Furthermore, the opportunities and challenges associated with LIG-based sensors in health monitoring are briefly discussed.

Magnetic Micro and Nano Sensors for Continuous Health Monitoring

Magnetic micro and nano sensors can be used in a broad variety of applications, e.g., for navigation, automotives, smartphones and also for health monitoring. Based on physical effects such as the well-known magnetic induction, the Hall effect, tunnel magnetoresistance and giant magnetoresistance, they can be used to measure positions, flow, pressure and other physical properties. In biomedicine and healthcare, these miniaturized sensors can be either integrated into garments and other wearables, be directed through the body by passive capsules or active micro-robots or be implanted, which usually necessitates bio-functionalization and avoiding cell-toxic materials. This review describes the physical effects that can be applied in these sensors and discusses the most recent micro and nano sensors developed for healthcare applications.

Crack Detection of Reinforced Concrete Structure Using Smart Skin.

The availability of carbon nanotube (CNT)-based polymer composites allows the development of surface-attached self-sensing crack sensors for the structural health monitoring of reinforced concrete (RC) structures. These sensors are fabricated by integrating CNTs as conductive fillers into polymer matrices such as polyurethane (PU) and can be applied by coating on RC structures before the composite hardens. The principle of crack detection is based on the electrical change characteristics of the CNT-based polymer composites when subjected to a tensile load. In this study, the electrical conductivity and electro-mechanical/environmental characterization of smart skin fabricated with various CNT concentrations were investigated. This was performed to derive the tensile strain sensitivity of the smart skin according to different CNT contents and to verify their environmental impact. The optimal CNT concentration for the crack detection sensor was determined to be 5 wt% CNT. The smart skin was applied to an RC structure to validate its effectiveness as a crack detection sensor. It successfully detected and monitored crack formation and growth in the structure. During repeated cycles of crack width variations, the smart skin also demonstrated excellent reproducibility and electrical stability in response to the progressive occurrence of cracks, thereby reinforcing the reliability of the crack detection sensor. Overall, the presented results describe the crack detection characteristics of smart skin and demonstrate its potential as a structural health monitoring (SHM) sensor.

Stiffness identification of reinforced concrete beams using rotation rate sensors

Monitoring flexural stiffness variations of cracked reinforced concrete (r/c) structures is one of the most challenging problems of the dynamics of these structures. In this article, the application of rotation rate sensors for structural health monitoring of reinforced concrete beams is proposed as an alternative SHM method to, e.g. those utilising more expensive and laborious fiber optics. This subject was investigated with progressively damaged 6 m long reinforced concrete beams. During each damage state, the cracks were located and described in detail. Experimental modal analyses were carried out. Translational and rotational vibration modes were acquired using translational accelerometers and rotation rate sensors. The stiffness distributions of the damaged reinforced concrete beams were identified from the modal properties using model-updating techniques. It is demonstrated that stiffness distributions calculated using rotational modes are more accurate than those obtained from the familiar translational natural modes of vibrations. Further improvement of stiffness identification of r/c beams from their vibrations was obtained by combining the directly measured rotational modes and their approximations from translational modes by the central difference method.

An Integrated High-Gain Wide–Dynamic Range Photoplethysmography Sensor for Cardiac Health Monitoring

An Integrated High-Gain Wide–Dynamic Range Photoplethysmography Sensor for Cardiac Health Monitoring

Machine Learning Applications in Optical Fiber Sensing: A Research Agenda.

The constant monitoring and control of various health, infrastructure, and natural factors have led to the design and development of technological devices in a wide range of fields. This has resulted in the creation of different types of sensors that can be used to monitor and control different environments, such as fire, water, temperature, and movement, among others. These sensors detect anomalies in the input data to the system, allowing alerts to be generated for early risk detection. The advancement of artificial intelligence has led to improved sensor systems and networks, resulting in devices with better performance and more precise results by incorporating various features. The aim of this work is to conduct a bibliometric analysis using the PRISMA 2020 set to identify research trends in the development of machine learning applications in fiber optic sensors. This methodology facilitates the analysis of a dataset comprised of documents obtained from Scopus and Web of Science databases. It enables the evaluation of both the quantity and quality of publications in the study area based on specific criteria, such as trends, key concepts, and advances in concepts over time. The study found that deep learning techniques and fiber Bragg gratings have been extensively researched in infrastructure, with a focus on using fiber optic sensors for structural health monitoring in future research. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. One of the main limitations is the lack of research on the use of novel materials, such as graphite, for designing fiber optic sensors. This presents an opportunity for future studies.