Advanced Health Monitoring Research Articles

Stretchable and Superwettable Colorimetric Sensing Patch for Epidermal Collection and Analysis of Sweat.

Stretchable and wearable sensors allow intimate integration with the human body for health and fitness monitoring. In addition to the acquisition of various physical parameters, quantitative analysis of chemical biomarkers present in sweat may provide vital insights into the physiological state of an individual. A widely investigated system utilizes electrochemical techniques for continuous monitoring of these biomarkers. The required supporting electronics and batteries are often challenging to form a deformable system. In this study, an intrinsically stretchable sensing patch is developed with compliant mechanical properties for conformal attachment to the skin and reliable collection of sweat. In these patches, superhydrophilic colorimetric assays consisting of thermoplastic polyurethane nanofiber textiles decorated with silica nanoparticles are assembled over a styrene-ethylene-butylene-styrene-based superhydrophobic substrate, thereby generating a large wettability contrast to efficiently concentrate the sweat. The system supports multiplexed colorimetric analysis of sweat to quantify pH and ion concentrations with images acquired using smartphones, in which the influence of ambient lighting conditions is largely compensated with a set of reference color markers. Successful demonstrations of insitu analysis of sweat after physical exercises effectively illustrate the practical suitability of the sensing patch, which is attractive for advanced health monitoring, clinical diagnostics, and competitive sports.

Wheel flat analogue fault detector verification study under dynamic testing conditions using a scaled bogie test rig

ABSTRACT Advanced health monitoring of unpowered heavy haul and general freight railway wagons is still an emerging field, limited by the lack of electrical power on-board the vehicles and the cost of instrumenting massive fleets. This paper presents a dynamic verification of an on-board wheel flat detection technique that, using analogue signal processing, reduces the power consumption and hardware costs of condition monitoring sensor nodes. A 1:4 scale bogie test rig was used to record bearing adapter acceleration signals of healthy and defective wheelsets. The data were then used to verify the wheel flat detection technique, which effectively distinguished between healthy and defective acceleration signals, using analogue computing only, without the need for software or a complex algorithm and corresponding hardware. This technique is promising for further development of low-cost and ultra-low power sensor nodes systems that require numerous sensor nodes, such as heavy haul and general freight railway applications.

Optical fibers applied to aerospace systems prognostics: design and development of new FBG-based vibration sensors

Future generation actuation systems will be characterized by ever-increasing complexity. In this context, it will be necessary to adopt advanced health monitoring strategies to guarantee a high level of operational safety and system reliability. Prognostics and Health Management (PHM) is thus emerging as an enabling discipline for the design and operation of future advanced, complex systems. Smart systems with embedded self-monitoring capabilities are nowadays required in order to provide early faults identification and to perform innovative diagnostic and prognostic functions. In aerospace applications, the use of smart sensors could replace various types of traditional sensing elements, commonly used in structural monitoring with the additional capability of performing some prognostics or diagnostics tasks. This work proposes the first results of an experimental campaign aimed at evaluating and validating various packaging solutions for vibration amplification and detection using optical sensors (Fiber Bragg Gratings, FBGs), since characteristics frequencies can be good prognostics indicators of particular failure modes of a system. Several test samples were created by using 3D printed PLA and compared using a variety of bench tests. Results were compared in order to identify the strengths and weaknesses of the various proposed configurations, and were validated by comparing them with numerical simulations and experimental measurements performed with traditional sensors such as strain gages and accelerometers.

Novel technology concepts and architecture for on-board condition-based monitoring of railway running gear: The RUN2Rail vision

Bogies are key subsystems for rolling stock safety and, therefore, meaningful and objective data concerning their condition is of paramount importance for railway operation. These subsystems experience severe service conditions causing wear, damage and degradation of components and affecting the vibrations to which the passengers are exposed. As such, safe and reliable operation, together with a high level of comfort for the passengers, can only be assured by an in-depth, data-based and comprehensive maintenance of the bogie components. In this perspective, advanced health monitoring of the running gear plays a fundamental role as the enabler for condition-based maintenance strategies. This paper reports about work performed in the RUN2Rail project aimed at formulating new concepts for the condition monitoring of the running gear. Three case studies are addressed: wheelsets, powertrain and suspension components. For these cases, the suitable choice and location of sensors is investigated and innovative fault detection and fault classification methods are proposed and preliminarily validated by means of numerical experiments and laboratory tests. A concise outline of the impacts and benefits of each proposed condition monitoring application is also provided.

Generic Approach to Boost the Sensitivity of Metal Oxide Sensors by Decoupling the Surface Charge Exchange and Resistance Reading Process

As one of bottlenecking parameters for practical applications of metal oxide semiconductor based gas sensors, sensitivity enhancement has attracted significant attention in the past few decades. In this work, alternative to conventional strategies of designing sensitive surfaces via morphology/defect/heterojunction control (then operating at an optimized isothermal temperature with a maximal response), a facile enhancement approach by decoupling surface charge exchange and resistance reading process (possessing different temperature-dependent behaviors) through pulsed temperature modulation (PTM) is reported. Substantially magnifying electrical responses of a generic metal oxide (e.g.WO3) MEMS sensor toward diverse analyte molecules is demonstrated. Under the optimal PTM conditions, the response toward 10 ppm NO2 can be boosted from (isothermal) 99.7 to 842.7, the response toward 100 ppm acetone is increased from (isothermal) 2.7 to 425, respectively, which are comparable to or even better than most of the state-of-the-art WO3 based sensors. In comparison to conventional (isothermal) operation, PTM allows to sequentially manipulate the physi/chemisorption of analyte molecules, generation of surface reactive oxygen species (ROS) and sensor resistance reading, and thus provides additional opportunity in boosting electrical response of oxide sensors for advanced health and/or environment monitoring in future.

Dynamic response-based LEDs health and temperature monitoring

This paper presents a number of novel methods to measure the junction temperature and to estimate the health of gallium nitride light-emitting diodes (LEDs). The methods are based on measurements of the dynamic impedance and optical output. Our experimental analysis reveals temperature sensitive parameters of the electrical and optical responses. Moreover, a correlation between the non-radiative current characterizing the active region defects and the small-signal impedance is shown. The demonstrated methods can be applied to enhance existing temperature-monitoring techniques. The derived dependencies also build a foundation for advanced in-field health monitoring of the LEDs using the infrastructure of visible light communication systems. Such methods and techniques are valuable for predictive maintenance of solid state lighting systems.

Machine learning in telemetry data mining of space mission: basics, challenging and future directions

The development of an intelligent artificial satellite health monitoring system is a key issue in aerospace engineering that determines satellite health status and failure using telemetry data. The modern design of data mining and machine learning technologies allows the use of satellite telemetry data and the mining of integrated information to produce an advanced health monitoring system. This paper reviews the current status and presents a framework of necessary processes on data mining to solving various problems in telemetry data such as error detection, prediction, summarization, and visualization of large quantities, and help them understand the health status of the satellite and detect the symptoms of anomalies. Machine learning technologies that include neural networks, fuzzy sets, rough sets, support vector machines, Naive Bayesian, swarm optimization, and deep learning are also presented. Also, this paper reviews a wide range of existing satellite health monitoring solutions and discusses them in the framework of remote data mining techniques. In addition, we are discussing the analysis of space debris flow analysis and the prediction of low earth orbit collision based on our orbital Petri nets model. Challenges to be addressed and future directions of research are identified and an extensive bibliography is also included.

Fog-Computing-Based Heartbeat Detection and Arrhythmia Classification Using Machine Learning

Designing advanced health monitoring systems is still an active research topic. Wearable and remote monitoring devices enable monitoring of physiological and clinical parameters (heart rate, respiration rate, temperature, etc.) and analysis using cloud-centric machine-learning applications and decision-support systems to predict critical clinical states. This paper moves from a totally cloud-centric concept to a more distributed one, by transferring sensor data processing and analysis tasks to the edges of the network. The resulting solution enables the analysis and interpretation of sensor-data traces within the wearable device to provide actionable alerts without any dependence on cloud services. In this paper, we use a supervised-learning approach to detect heartbeats and classify arrhythmias. The system uses a window-based feature definition that is suitable for execution within an asymmetric multicore embedded processor that provides a dedicated core for hardware assisted pattern matching. We evaluate the performance of the system in comparison with various existing approaches, in terms of achieved accuracy in the detection of abnormal events. The results show that the proposed embedded system achieves a high detection rate that in some cases matches the accuracy of the state-of-the-art algorithms executed in standard processors.

Biodegradable polymer material based smart stent: Wireless pressure sensor and 3D printed stent

In this paper, we present a 3D-printed biocompatible and biodegradable polymer stent with an integrated wireless pressure sensor, designed for advanced health monitoring applications. The Polycaprolactone (PCL) based polymer stent has a drug coating similar to conventional drug-eluting stents (DES) and a wireless pressure sensor that enables the real-time measurement of pressure inside the blood vessel. The wireless pressure sensor is made of poly(d-lactide) (PDLA), a biocompatible material that can be decomposed and absorbed together with the polymer stent in the body. The resonance frequency of the fabricated wireless pressure sensor (initially 148 MHz) decreases with blood pressure changes. Experimental results indicate that the sensitivity of the wireless pressure sensor (60 kHz/mmHg) shows a linear frequency shift within the pressure range of 0–220 mmHg.

Machine learning and Data Mining Techniques in Health Monitoring of Artificial Satellites Mission

The development of an intelligent satellite health monitoring system is an essential issue in aerospace engineering that determines the state of health and failure of satellites based on telemetry data. The recent development of data mining and machine learning techniques makes it possible to use satellite telemetry data and to extract integrated information for the production of advanced health monitoring system. The editorial paper can be used as a guideline for the design of an intelligent health monitoring solution based on remote data.

Skin-Inspired Electronics: An Emerging Paradigm.

Future electronics will take on more important roles in people's lives. They need to allow more intimate contact with human beings to enable advanced health monitoring, disease detection, medical therapies, and human-machine interfacing. However, current electronics are rigid, nondegradable and cannot self-repair, while the human body is soft, dynamic, stretchable, biodegradable, and self-healing. Therefore, it is critical to develop a new class of electronic materials that incorporate skinlike properties, including stretchability for conformable integration, minimal discomfort and suppressed invasive reactions; self-healing for long-term durability under harsh mechanical conditions; and biodegradability for reducing environmental impact and obviating the need for secondary device removal for medical implants. These demands have fueled the development of a new generation of electronic materials, primarily composed of polymers and polymer composites with both high electrical performance and skinlike properties, and consequently led to a new paradigm of electronics, termed "skin-inspired electronics". This Account covers recent important advances in skin-inspired electronics, from basic material developments to device components and proof-of-concept demonstrations for integrated bioelectronics applications. To date, stretchability has been the most prominent focus in this field. In contrast to strain-engineering approaches that extrinsically impart stretchability into inorganic electronics, intrinsically stretchable materials provide a direct route to achieve higher mechanical robustness, higher device density, and scalable fabrication. The key is the introduction of strain-dissipation mechanisms into the material design, which has been realized through molecular engineering (e.g., soft molecular segments, dynamic bonds) and physical engineering (e.g., nanoconfinement effect, geometric design). The material design concepts have led to the successful demonstrations of stretchable conductors, semiconductors, and dielectrics without sacrificing their electrical performance. Employing such materials, innovative device design coupled with fabrication method development has enabled stretchable sensors and displays as input/output components and large-scale transistor arrays for circuits and active matrixes. Strategies to incorporate self-healing into electronic materials are the second focus of this Account. To date, dynamic intermolecular interactions have been the most effective approach for imparting self-healing properties onto polymeric electronic materials, which have been utilized to fabricate self-healing sensors and actuators. Moreover, biodegradability has emerged as an important feature in skin-inspired electronics. The incorporation of degradable moieties along the polymer backbone allows for degradable conducting polymers and the use of bioderived materials has led to the demonstration of biodegradable functional devices, such as sensors and transistors. Finally, we highlight examples of skin-inspired electronics for three major applications: prosthetic e-skins, wearable electronics, and implantable electronics.

Biodegradable Polymeric Materials in Degradable Electronic Devices.

Biodegradable electronics have great potential to reduce the environmental footprint of devices and enable advanced health monitoring and therapeutic technologies. Complex biodegradable electronics require biodegradable substrates, insulators, conductors, and semiconductors, all of which comprise the fundamental building blocks of devices. This review will survey recent trends in the strategies used to fabricate biodegradable forms of each of these components. Polymers that can disintegrate without full chemical breakdown (type I), as well as those that can be recycled into monomeric and oligomeric building blocks (type II), will be discussed. Type I degradation is typically achieved with engineering and material science based strategies, whereas type II degradation often requires deliberate synthetic approaches. Notably, unconventional degradable linkages capable of maintaining long-range conjugation have been relatively unexplored, yet may enable fully biodegradable conductors and semiconductors with uncompromised electrical properties. While substantial progress has been made in developing degradable device components, the electrical and mechanical properties of these materials must be improved before fully degradable complex electronics can be realized.

Probabilistic Method to predict Remaining Usage Life of Aircraft Structures

Current trends in the design of new aircraft components and high level innovations in the older type of aircraft are clearly pointing to automation and integration of all aircraft systems to give increased cost efficiency in the aircraft operation. The same trend can be observed in the design, operation and maintenance of aircraft structures where advanced Structure Health Monitoring (SHM) systems are about to enter into service. These advanced systems are designed to provide both diagnostic and prognostic information enabling application of Condition Based Maintenance (CBM) or even Prognostics Maintenance (PM) concepts into the maintenance of the aircraft structures. To make this CBM/PM concept a reality, an accurate, reliable and robust method to predict the Remaining Usage Life (RUL) of the structure is the foremost step.This paper presents a probabilistic method for RUL prediction. A hybrid approach is used, comprising of two different algorithms. The first algorithm adopts fracture- mechanics based fatigue crack growth model. This approach uses physics of failure to predict the crack growth curve and underlying degradation process. It calculates the accurate value of Stress Intensity Factor (SIF) to calculate the crack growth curve. The second algorithm is a mathematical model which quantify various sources of uncertainty such as future loading, crack length, model parameters etc. The process described in this paper results in enhanced remaining usage life estimation by compensating for the aforementioned modeling uncertainties. The model results were verified and validated on a typical aero structure with experimental, FEA simulation and fractography data.

State of the Art Review on Bridges Structural Health Monitoring (Applications and Future Trends)

Abstract New bridges offer opportunities for developing complete structural health monitoring systems for bridge inspection and condition evaluation of the bridges. Existing bridges provide challenges for applying state-of-the-art in structural health monitoring technologies to determine the current conditions of the structural element, connections and systems, to formulate model for estimating the rate of degradation, and to predict the existing and the future capacities of the structural components and systems. Advanced health monitoring systems may lead to better understanding of structural behavior and significant improvements of design, as well as the reduction of the structural inspection requirements. Great benefits due to the introduction of structural health monitoring are being accepted by owners, managers, bridge engineers, etc. This chapter provides health monitoring applications to bridges in different spots of the world as well as future trends and research needs.

Investigations optimum scheduling and TCP mechanism of hybrid topology sensor network in building SHM

Abstract Sensor technology has opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure such as large buildings. Compare to traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure at a lower price and greater range. Numerous domestic buildings, especially long-span buildings, have a low-profile frequency response that is challenging to accurately measure with the application of sensors suitable for integration with lower-cost, lower-power-profile wireless sensor networks. To cover the whole area of interest, sensor network topology is one of the most important issues. The objective of this article is to investigate of hybrid topology scheduling algorithm to determine the optimum TCP (Transmission Control Mechanism) mechanism for assessing large building structural health data using various kinds TCP and scheduling algorithm. Result show that, the Full TCP mechanism is selected as optimum TCP mechanism for monitoring building structural health data.

Building structural health monitoring using dense and sparse topology wireless sensor network

Wireless sensor technology has been opened up numerous opportunities to advanced health and maintenance monitoring of civil infrastructure. Compare to the traditional tactics, it offers a better way of providing relevant information regarding the condition of building structure health at a lower price. Numerous domestic buildings, especially longer-span buildings have a low frequency response and challenging to measure using deployed numbers of sensors. The way the sensor nodes are connected plays an important role in providing the signals with required strengths. Out of many topologies, the dense and sparse topologies wireless sensor network were extensively used in sensor network applications for collecting health information. However, it is still unclear which topology is better for obtaining health information in terms of greatest components, node\'s size and degree. Theoretical and computational issues arising in the selection of the optimum topology sensor network for estimating coverage area with sensor placement in building structural monitoring are addressed. This work is an attempt to fill this gap in high-rise building structural health monitoring application. The result shows that, the sparse topology sensor network provides better performance compared with the dense topology network and would be a good choice for monitoring high-rise building structural health damage.

Metal/Polymer Based Stretchable Antenna for Constant Frequency Far‐Field Communication in Wearable Electronics

Body integrated wearable electronics can be used for advanced health monitoring, security, and wellness. Due to the complex, asymmetric surface of human body and atypical motion such as stretching in elbow, finger joints, wrist, knee, ankle, etc. electronics integrated to body need to be physically flexible, conforming, and stretchable. In that context, state‐of‐the‐art electronics are unusable due to their bulky, rigid, and brittle framework. Therefore, it is critical to develop stretchable electronics which can physically stretch to absorb the strain associated with body movements. While research in stretchable electronics has started to gain momentum, a stretchable antenna which can perform far‐field communications and can operate at constant frequency, such that physical shape modulation will not compromise its functionality, is yet to be realized. Here, a stretchable antenna is shown, using a low‐cost metal (copper) on flexible polymeric platform, which functions at constant frequency of 2.45 GHz, for far‐field applications. While mounted on a stretchable fabric worn by a human subject, the fabricated antenna communicated at a distance of 80 m with 1.25 mW transmitted power. This work shows an integration strategy from compact antenna design to its practical experimentation for enhanced data communication capability in future generation wearable electronics.

Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

Femtosecond-Long Pulse-Based Modulation for Terahertz Band Communication in Nanonetworks

Nanonetworks consist of nano-sized communicating devices which are able to perform simple tasks at the nanoscale. Nanonetworks are the enabling technology of long-awaited applications such as advanced health monitoring systems or high-performance distributed nano-computing architectures. The peculiarities of novel plasmonic nano-transceivers and nano-antennas, which operate in the Terahertz Band (0.1-10 THz), require the development of tailored communication schemes for nanonetworks. In this paper, a modulation and channel access scheme for nanonetworks in the Terahertz Band is developed. The proposed technique is based on the transmission of one-hundred-femtosecond-long pulses by following an asymmetric On-Off Keying modulation Spread in Time (TS-OOK). The performance of TS-OOK is evaluated in terms of the achievable information rate in the single-user and the multi-user cases. An accurate Terahertz Band channel model, validated by COMSOL simulation, is used, and novel stochastic models for the molecular absorption noise in the Terahertz Band and for the multi-user interference in TS-OOK are developed. The results show that the proposed modulation can support a very large number of nano-devices simultaneously transmitting at multiple Gigabits-per-second and up to Terabits-per-second, depending on the modulation parameters and the network conditions.

In-vehicle monitoring and management for military vehicles’ integrated vetronics architectures

Both the rapid uptake of electronics utilisation for military vehicles (vetronics) and the trend in designing vehicles based on open standards and architectures (such as the UK MoD Generic Vehicle Architecture – Def Stan 23-09 GVA) have resulted in modern and future vehicles becoming highly complex platforms. Although most modern vehicles provide advanced diagnostics and health monitoring, most of these systems focus on end systems and applications and there has been little work on monitoring the infrastructure on which these systems operate. Monitoring the operational integrity of the vetronics that enable integration of vehicle subsystems and providing feedback to the crew offers safety and reliability benefits as it allows the crew to assess in real-time, the vehicle’s ability to complete its mission. This paper presents a novel approach to achieve in-vehicle Vetronics Integrity Monitoring and Management (VIMM). The paper highlights the requirements for a generic modular system, and presents both an architecture and proof-of-concept implementation.