Advanced Health Monitoring Research Articles

Engineered Conductive Metal-organic Frameworks for Electrochemical Detection of Urinary Biomarkers.

The increasing prevalence of health concerns has sparked a demand for advanced health monitoring tools, which is a significant challenge for researchers. Analysis of biofluid samples and biomarker monitoring is vital for comprehensive health assessment. In this context, metal-organic frameworks (MOFs) have emerged as one of the leading materials for developing cutting-edge artificial sensors. These unique MOFs have been developed from metallic and organic linkers and consist of tunable pores and dynamic surface areas, attracting various guest molecules to participate in developing advanced electrochemical devices. This study is a gateway to a world where MOF designs transcend mere inventions, morphing into electric marvels that revolutionize urine sample monitoring. From the inception and design to the fabrication of MOF sensors tailored for electrochemical applications, this study prides itself on elucidating the factors affecting MOF-based electrical devices and sensors. Moreover, the focus is placed on conductivity, electrode properties, and recent state-of-the-art urine sample analysis, highlighting the pivotal role biomarkers play in honing the resilience and performance of the sensors. Finally, this study is specific to electrochemical sensors used for urine analysis. It will attract researchers to develop new portable tools for health monitoring by analyzing urine samples.

Bioinspired Ultra‐Stretchable and Highly Sensitive Structural Color Electronic Skins

AbstractOrganisms possess remarkably adaptive ability to complex environments. For example, chameleons can alter their skin color to adapt to varying environments, which has inspired significant advances in bioinspired soft electronic skins (E‐skins), and their wide applications in wearable sensors, intelligent robots, and health monitoring. However, current bioinspired E‐skins face challenges in ultra‐stretchability, high sensitivity, and long‐term stability owing to the intrinsic limitations associated with their mismatched interface between soft matrix and hard conductive fillers, hindering their practical applications. Here, it is reported that bioinspired structural color electronic skins (SC E‐skins) that consist of liquid metal particles (LMPs), periodical ordered colloidal crystal arrays, and ultra‐stretchable hydrogel, imparting synergistic and durable electrical–optical sensing capabilities. Such SC E‐skins demonstrate outstanding performances including superior flexibility (elongation at break > 1100%), high sensitivity (gauge factor = 3.26), fast synergetic electric–optical response time (≈100 ms), outstanding durability (over 1500 cycles), and high accuracy (R2 > 99.5%). These bioinspired SC E‐skins with excellent capability of converting mechanical signals into synergetic electrical–optical outputs hold great promise for smart wearable devices, affording a new horizon in developing advanced health monitoring technologies.

E-Skin and Its Advanced Applications in Ubiquitous Health Monitoring.

E-skin is a bionic device with flexible and intelligent sensing ability that can mimic the touch, temperature, pressure, and other sensing functions of human skin. Because of its flexibility, breathability, biocompatibility, and other characteristics, it is widely used in health management, personalized medicine, disease prevention, and other pan-health fields. With the proposal of new sensing principles, the development of advanced functional materials, the development of microfabrication technology, and the integration of artificial intelligence and algorithms, e-skin has developed rapidly. This paper focuses on the characteristics, fundamentals, new principles, key technologies, and their specific applications in health management, exercise monitoring, emotion and heart monitoring, etc. that advanced e-skin needs to have in the healthcare field. In addition, its significance in infant and child care, elderly care, and assistive devices for the disabled is analyzed. Finally, the current challenges and future directions of the field are discussed. It is expected that this review will generate great interest and inspiration for the development and improvement of novel e-skins and advanced health monitoring systems.

Comparative analysis of data-driven electric vehicle battery health models across different operating conditions

The work covers the development of a data-driven algorithm and computes the performance of learning models for lithium-ion battery state of health (SOH) estimation. A wide range of environmental and temperature conditions (15 °C, 25 °C, and 35 °C) at different charging and discharging rates of 1C and 2C are used for electric vehicle battery health estimation. The result of the tested data of cell ‘a’ is validated with a different set of cell ‘b’ on identical test parameters, and the results are tabulated and compared. At 25 °C, the mean absolute errors for the regression algorithms decision tree (DT), k-nearest neighbor (KNN), and random forest (RF) are 3.78641E-03, 3.62524E-03, and 6.16931E-03. The mean absolute percent error for regression algorithms DT, KNN, and RF is 1.48921E-03, 1.40631E-03, and 2.40260E-03. The root mean square error for regression algorithms DT, KNN, and RF is 1.26813E-02, 9.73320E-03, and 1.17238E-02, and the mean squared error for regression algorithms DT, KNN, and RF is 1.60816E-04, 9.47351E-05, and 1.37448E-04. The results show that the KNN and DT methods accurately estimate the SOH under diversified operating conditions in comparison with RF methods and can foster advanced battery health monitoring systems.

NiO nanoparticles-based gas sensors: A novel pulse-driven approach for enhanced and efficient hydrogen detection

In recent decades, enhancing response in metal oxide semiconductor-based gas sensors has emerged as a crucial parameter for practical applications. In this study, we present an innovative approach aimed at augmenting sensor response through pulsed heating, deviating from conventional strategies focused on sensitive nanomaterials design. By extending the trade-off between surface charge exchange and physisorption of analyte molecules, we significantly amplify the electrical responses from 12.8 (under isothermal conditions) to 32.1 when exposed to 1000 ppm H2 using a generic NiO sensor. This approach introduces a novel avenue for enhancing the electrical response of resistive-type sensors, offering potential advancements in portable devices for advanced health and environmental monitoring.

Viscoelastic Adhesive, Super-Conformable, and Semi-Flowable Liquid Metal Eutectogels for High-Fidelity Electrophysiological Monitoring.

Integrating gels with human skin through wearables provides unprecedented opportunities for health monitoring technology and artificial intelligence. However, most conductive hydrogels, organogels, and ionogels lack essential environmental stability, biocompatibility, and adhesion for reliable epidermal sensing. In this study, we have developed a liquid metal eutectogel simultaneously possessing superior viscoelasticity, semiflowability, and mechanical rigidity for low interfacial skin impedance, high skin adhesion, and durability. Liquid metal particles (LMPs) are employed to generate free radicals and gallium ions to accelerate the polymerization of acrylic acid monomers in a deep eutectic solvent (DES), obtaining highly viscoelastic polymer networks via physical cross-linking. In particular, graphene oxide (GO) is utilized to encapsulate the LMPs through a sonication-assisted electrostatic assembly to stabilize the LMPs in DES, which also enhances the mechanical toughness and regulates the rheological properties of the eutectogels. Our optimized semi-flowable eutectogel exhibits viscous fluid behavior at low shear rates, facilitating a highly conformable interface with hairy skin. Simultaneously, it demonstrates viscoelastic behavior at high shear rates, allowing for easy peel-off. These distinctive attributes enable the successful applications of on-skin adhesive strain sensing and high-fidelity human electrophysiological (EP) monitoring, showcasing the versatility of these ionically conductive liquid metal eutectogels in advanced personal health monitoring.

The Wireless Health Monitoring for Neonatal Care

Abstract: The wireless health monitoring for neonatal care project addresses the critical need for advanced and non-intrusive health monitoring solutions for newborns in neonatal care settings. Approximately 10% of newborns require assistance at birth, and preterm infants often require specialized stabilization or resuscitation. Traditional monitoring methods, such as electrocardiography (ECG) and pulse oximetry (PO), face challenges in electrode application, delayed HR readings, and potential inaccuracies in assessing cardiac output. This project aims to develop a wireless health monitoring system that overcomes these limitations and offers real-time and accurate monitoring of vital parameters, with a focus on heart rate. The proposed system leverages wireless technology to enable continuous and non-intrusive monitoring, enhancing the efficiency of neonatal care in delivery rooms and neonatal intensive care units (NICUs). By incorporating state-of-the-art sensors and communication protocols, the system aims to provide timely and reliable data on neonatal vital signs. The wireless nature of the solution eliminates concerns related to electrode application difficulties, equipment availability, and the risk of hypothermia.

Harnessing IoT for Real-Time Plant Health Monitoring: Challenges and Opportunities

The "Plant Health Monitoring Project" is an intelligent system designed to monitor and maintain optimal conditions for plant growth. This project incorporates sensors such as LDR (Light Dependent Resistor), DHT11 (Temperature and Humidity Sensor), Soil Moisture Sensor, a Water Motor, and a Relay. The collected data, including light intensity, temperature, humidity, and soil moisture, is sent to Thing Speak for real-time monitoring. The system automatically activates the water motor through the relay if the soil moisture level is low, ensuring the plants receive adequate water. This project aims to promote efficient plant care and ensure a healthy growth environment. This project aims to develop an advanced plant health monitoring system by integrating Internet of Things (IoT) technology with machine learning algorithms. The system will utilize various sensors to collect real-time data on environmental conditions such as temperature, humidity, soil moisture, and light intensity, as well as plant health parameters including leaf color, size, and shape. The collected data will be transmitted wirelessly to a central server for analysis. Machine learning algorithms will then be employed to analyze the data and identify patterns indicative of plant stress, diseases, or nutrient deficiencies. The system will provide timely alerts to farmers or gardeners, enabling them to take proactive measures to maintain the health and productivity of their plants. This innovative approach to plant health monitoring has the potential to revolutionize agricultural practices and contribute to increased crop yields and stainability.

Degradation prognostics of aerial bundled cables based on multi-sensor data fusion

ABSTRACT Development of advanced health monitoring sensors and high-performance computing enabled multi-sensors information to analyse degradation in complex engineering systems/components. These sensors are often aimed to capture different physical aspects of a system. Thus, each sensor only contains partial information about the same degradation process. A significant need is to devise a contemporary data fusion method that effectively integrates independent multi-sensors measurements leading to a better prognosis of the degradation process. Unlike conventional data fusion methodologies that fuse multiple sensors’ information prior implementation of prognosis step, this paper presents a novel fusion framework based on multi-sensor prognosis data. The framework is developed using Particle Filtering (PF) technique coupled with Auto-Regressive Integrated Moving Average (ARIMA) model. The proposed framework is applied on historical Non-Destructive Testing (NDT) database generated through Ultrasonic Probe Listening and Thermal Imaging of in-service aerial bundled cables (ABCs) installed in coastal regions to evaluate cable degradation over time. Promising results of f -steps prediction scheme from fused degradation values, as compared to using individual measurement mode data, indicates the efficacy of the proposed method.

Recent Progress in Wearable Triboelectric Nanogenerator for Advanced Health Monitoring and Rehabilitation

Triboelectric nanogenerators (TENGs) have become a vital technology in physical health monitoring and rehabilitation applications, enabling continuous, personalized, and convenient monitoring, facilitating early detection and prevention, and offering valuable data‐driven insights. On‐body wearable triboelectric sensors enable real‐time tracking of vital health parameters, promoting accurate and personalized healthcare interventions. In addition to being self‐powered, these sensors facilitate early detection and prevention by capturing early warning signs of potential health issues, leading to timely interventions. Furthermore, they play a vital role in rehabilitation monitoring and feedback, helping to assess progress during the recovery process. This continuous monitoring, along with artificial intelligence, provides data‐driven insights into patterns, trends, and correlations, facilitating evidence‐based healthcare decision‐making. In this review, we provide an overview of the recent wearable TENG developments for healthcare applications. We discuss how TENGs work and explore a wide range of self‐powered health monitoring and rehabilitation applications. We then summarize recent advancements in healthcare applications, material selection, and unique features to help design highly efficient, accurate, and practical bioapplicable technology. Further, we discuss the challenges and prospects of the TENGs technology for healthcare applications, including miniaturization, moisture resistance, device maintenance, data reliability, and high outpower requirements.

The role of printed electronics in wearable technology evolution

The wearable technology industry has grown rapidly over the past decade, evolving from simple fitness trackers to advanced health monitoring devices and smart clothing. At the heart of this evolution is the field of printed electronics, which offers innovative solutions to the unique challenges of wearables, says Amro Abu Zarour, Project Manager at Beneli in Helsingborg, Sweden.

A Highly Porous and Flexible Carbon Nanotube Array Coated with Gold Nanoparticles: Application in Non‐Enzymatic Ultrasensitive Detection and Monitoring of Blood/Saliva Glucose

AbstractFlexible, reusable, and ultrasensitive glucose sensors are the need of the hour for advanced health monitoring, specifically for economical diabetes management in low‐income countries. Here, we demonstrate an ultra‐sensitive, and extremely lightweight non‐enzymatic glucose sensor based on gold nanoparticles sputtered carbon nanotube aerogel film (Au‐CNT), which is flexible, durable, reusable, and portable. The flexible Au‐CNT aerogel non‐enzymatic glucose sensor showed remarkable sensitivity of 779 μA mM−1 cm−2, a wide linear range of 0.1 mM to 6 mM and a limit of detection (LoD) of 1.47 μM. The sensitivity of our sensor is highest among literature‐reported carbon nanomaterials‐based flexible glucose sensors for selective detection of glucose in blood serum/saliva samples. The Au‐CNT aerogel shows the potential application as practical non‐invasive and invasive self‐monitoring glucose sensor. Therefore, highly sensitive and paper‐thin Au‐CNT electrode is a promising candidate for future commercial robust flexible and wearable biosensor applications.

Highly Stretchable Graphene Scrolls Transistors for Self-Powered Tribotronic Non-Mechanosensation Application.

Owing to highly desired requirements in advanced disease diagnosis, therapy, and health monitoring, noncontact mechanosensation active matrix has drawn considerable attention. To satisfy the practical demands of high energy efficiency, in this report, combining the advantage of multiparameter monitoring, high sensitivity, and high resolution of active matrix field-effect transistor (FET) with triboelectric nanogenerators (TENG), we successfully developed the tribotronic mechanosensation active matrix based on tribotronic ion gel graphene scrolls field-effect transistors (GSFET). The tribopotential produced by TENG served as a gate voltage to modulate carrier transport along the semiconductor channel and realized self-powered ability with considerable decreased energy consumption. To achieve high spatial utilization and more pronounced responsivity of the dielectric of this transistor, ion gel was used to act as a triboelectric layer to conduct friction and contact electrification with external materials directly to produce triboelectric charges to power GFET. This tribopotential-driving device has excellent tactile sensing properties with high sensitivity (1.125 mm-1), rapid response time (~16 ms), and a durability operation of thousands of cycles. Furthermore, the device was transparent and flexible with the capability of spatially mapping touch stimuli and monitoring real-time temperature. Due to all these unique characteristics, this novel noncontact mechanosensation GSFET active matrix provided a new method for self-powered E-skin with promising potential for self-powered wearable devices and intelligent robots.

Neural-inspired artificial synapses based on low-voltage operated organic electrochemical transistors

Artificial synapses inspired by the information processing mechanism of the human neural system serve as a platform to develop low-voltage operated high performance bioelectronics and advanced health monitoring systems.

Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems

Fault diagnosis and health condition monitoring have always been critical issues in the engineering research community [...].

IoT Fog-Enabled Multi-Node Centralized Ecosystem for Real Time Screening and Monitoring of Health Information

In today’s technological and stressful world, when everyone is busy in their daily routines and places blind faith in pharmaceutical advancements to protect their health, the sudden, horrifying effects of the COVID-19 pandemic have resulted in serious emotional and psychological impacts in the general population. In spite of advanced vaccination campaigns, fear and hesitation have become a part of human life since there are a number of people who do not want to take these immunity boosting vaccinations. Such people may become carriers of infectious viruses, leading to a more rapid rate of spread; therefore, this class of spreaders needs to be screened at the earliest opportunity. In this context, there is a need for advanced health monitoring systems which can assist the pharmaceutical industry to monitor and record the health status of people. To address this need and reduce the uncertainty of the situation, this study has designed and tested an Internet of Things (IoT) and Fog computing-based multi-node architecture was for real-time initial screening and recording of such subjects. The proposed system was able to record current body temperature and location coordinates along with the facial images. Further, the proposed system was able to transmit data to a cloud database using internet-connected services. An implementation and reviews-based working environment analysis was conducted to determine the efficacy of the proposed system. It was observed from the statistical analysis that the proposed IoT Fog-enabled ecosystem could be utilized efficiently.

RBM-MCDM framework for optimization of maintenance and inspection intervals of small unmanned aircrafts

PurposeUnmanned aircraft applications are quickly expanded in different fields. These systems are complex that include several subsystems with different types of technologies. Maintenance and inspection planning is necessary to obtain optimal performance and effectiveness. The failure rate in these systems is more than commercial and manned aircraft since they are usually cheaper. But maintenance and operation planning are difficult because we deal with a system that has multi-components, multi-failure models, and different dependencies between subsystems without any advanced health monitoring system. In this paper, this matter is considered and a framework to determine optimal maintenance and inspection plan for this type of system is proposed to improve system reliability and availability. The new criteria according to this field are proposed.Design/methodology/approachMaintenance of unmanned systems influences their readiness; also, according to the complexity of the system and different types of components, maintenance programming is a vital requirement. The plan should consider several criteria and disciplines; thus, multicriteria decision approaches may be useful. On another side, the reliability and safety of unmanned aircraft are the most important requirements in the design and operation phases. The authors consider these parameters and develop a framework based on risk-based maintenance to overcome the problems for unmanned systems. This framework consists of two stages: at the first stage, the critical components and failure modes are determined by FMEA, and in the second stage, the priority of maintenance tasks is determined by a fuzzy multicriteria weighted decision system. In this study, fourteen criteria with different levels of importance are developed and proposed to find the best plan for maintenance and inspection intervals. These criteria have been extracted from the literature review, the author's experience, and expert opinions.FindingsA novel framework for risk-based maintenance has been proposed. Risk determination and risk criteria are the most important factors in this framework. Risks are determined by FMEA, and new criteria are proposed that are used for decision-making. These criteria are proposed based on practical experience and experts' opinions for the maintenance process in the aeronautic industry. These are evaluated by industrial cases, and this framework capability has been demonstrated.Research limitations/implicationsThe proposed framework and criteria for small unmanned aircraft have been developed based on a practical point of view and expert opinion. Thus for implementation in other aeronautic industries, the framework may need a minor modification.Practical implicationsTwo important subsystems of an unmanned aircraft have been studied, and the capabilities of this method have been presented.Originality/valueThis research is original work to determine a maintenance program for unmanned aircraft that their application has rapidly grown up. Practical and design parameters have been considered in this work.

Performance Analysis of Time-Hopping Multiple Access Using Multilevel PPM for Terahertz Nanocommunications

Terahertz (THz)-band nanocommunication is envisioned to revolutionize the future of wireless communications by enabling applications in nanoscale domains such as the Internet of Nanothings, wireless on-chip communications, and advanced health monitoring. However, communication between nanodevices is hindered by the highly frequency-selective and distance-dependent nature of the THz channel, which ultimately restricts nanocommunication distances to a few millimeters. Moreover, attempts by multiple nanodevices to access the channel simultaneously increase the interference in the overall communication system. In this study, a new pulse-based modulation scheme for nanonetworks called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">time-hopping multilevel pulse position modulation (TH ML-PPM)</i> is proposed and its performance in a multiuser nanocommunication scenario is analyzed. In ML-PPM, each nanomachine in the nanonetwork first transforms the transmitted bits into multilevels by using several orthogonal codes and then modulates each multilevel code into a pulse position. The generated ML-PPM signal is subsequently time-hopped to achieve multiple access. Employing orthogonal coding results in spreading gain at the nanoreceiver, which improves the performance of the proposed scheme. The bit error rate and link capacity of the time-hopped multilevel PPM scheme are evaluated for different THz propagation conditions and system design parameters. The results show that for a THz channel with 10% water vapor concentration, a link capacity of more than 100 gigabits per second is achieved across a transmission distance of 0.5 mm.

Coaxial 3D-Printed and kirigami-inspired deployable wearable electronics for complex body surfaces

Wearable electronics used to capture biological signals are substantially important in human–robot interactions, health monitoring, and clinical treatment. However, for curved or irregular body surfaces, intimate interfacing with the skin, which is essential for robust signal recording, is challenging. In this study, flexible core-sheath fiber sensors and adaptive devices were developed using a coaxial 3D printing technique and kirigami-inspired patterns. The printed core-sheath fiber, which was successfully applied in human–robot interaction, exhibited excellent electromechanical properties with a sensing strain range of 700%, and had high accuracy of approximately 3 mN and high electromechanical durability. In addition, the viscoelastic nature of the core material (shear-stiffening gel) provided the fiber array with fine energy dissipation performance against external harm by buffering the impact force by 51% while simultaneously capturing the dynamic impact in 4 ms. Moreover, the introduction of kirigami-inspired deformability to planar electronics facilitated conformable attachment of sensing devices with substantial adjustability to 3D curved surfaces; they can be adapted to shoe pads of different sizes without compromising their sensitivity. The 3D printing technique and kirigami-inspired pattern designs for creating adaptive and flexible wearable electronics hold great potential for advanced health monitoring of diverse and complex epidermal surfaces.

An Efficient Intravenous Drip System For Hospital Enviroment

Healthcare system plays important role in leading a happy life of increased birth rates. At healthcare, preserving the safety of patient is by far the most important matter. Advanced health monitoring systems seem to be the most sought after anyway they offer accurate information while decreasing the hospital’s medical professionals as well as attendees’ stress and over absence of many crucial information. It also is a tedious process and a harder task to demonstrate that a long and tiring method and a harder job is when the injected supplied to a fall’s prevention under a crucial quantity A simpler task is to manually monitor the level of inject able fluid, if not then done with the utmost care, the safety of an individual can be severely affected. This may result to blood or plasma flow becoming diverted through the bloodstream to the Drip tube. When the container is drained fully, air enters its tubing but in exchange enters the vein, which could be disastrous for the patient. So, it would prove very helpful to optimize this system. The blood glucose in the insulin travel container in use in clinics in this study was identified in this study. In this, whenever the IV drip was to be drained, the scheme we had also formed to connect two streams at a same intersection and access the network using control valves system of the Arduino is engineered so that if the injected reaches a dangerous threshold, It is sensed through the use of water content as well as the secondary moving fluid valve is stimulated. This requires use of the control valve as well as the flow analysis software to calculate the measured value.