An overview on structural health monitoring: From the current state-of-the-art to new bio-inspired sensing paradigms

In the last decades, the field of structural health monitoring (SHM) has grown exponentially. Yet, several technical constraints persist, which are preventing full realization of its potential. To upgrade current state-of-the-art technologies, researchers have started to look at nature’s creations giving rise to a new field called ‘biomimetics’, which operates across the border between living and non-living systems. The highly optimised and time-tested performance of biological assemblies keeps on inspiring the development of bio-inspired artificial counterparts that can potentially outperform conventional systems. After a critical appraisal on the current status of SHM, this paper presents a review of selected works related to neural, cochlea and immune-inspired algorithms implemented in the field of SHM, including a brief survey of the advancements of bio-inspired sensor technology for the purpose of SHM. In parallel to this engineering progress, a more in-depth understanding of the most suitable biological patterns to be transferred into multimodal SHM systems is fundamental to foster new scientific breakthroughs. Hence, grounded in the dissection of three selected human biological systems, a framework for new bio-inspired sensing paradigms aimed at guiding the identification of tailored attributes to transplant from nature to SHM is outlined.info:eu-repo/semantics/acceptedVersio

A survey on bio-signal analysis for human-robot interaction

The use of bio-signals analysis in human-robot interaction is rapidly increasing. There is an urgent demand for it in various applications, including health care, rehabilitation, research, technology, and manufacturing. Despite several state-of-the-art bio-signals analyses in human-robot interaction (HRI) research, it is unclear which one is the best. In this paper, the following topics will be discussed: robotic systems should be given priority in the rehabilitation and aid of amputees and disabled people; second, domains of feature extraction approaches now in use, which are divided into three main sections (time, frequency, and time-frequency). The various domains will be discussed, then a discussion of each domain's benefits and drawbacks, and finally, a recommendation for a new strategy for robotic systems

Analysis of Wireless Body-Centric Medical Sensors for Remote Healthcare

Aquesta tesi aborda el problema de trobar solucions confortables, de baixa potència i sense fils per aplicacions mèdiques. La tesi tracta els avantatges i les limitacions de tres tecnologies de comunicació diferents per la mesura de paràmetres del cos i mètodes per redissenyar sensors per avaluacions òptimes centrades en el cos. La tecnologia RFID es considera una de les solucions més influents per superar el problema del consum d'energia limitat, a causa de la presència de molts sensors connectats. També s'ha estudiat la tecnologia Bluetooth de baixa energia per resoldre els problemes de seguretat i la distància de lectura que, en general, representen el coll d'ampolla de RFID pels sensors de cos. Els dispositius analògics poden reduir dràsticament les necessitats d'energia a causa dels sensors i les comunicacions, considerant pocs elements i un mètode de transmissió simple. S'estudia un mètode de comunicació completament passiu, basat en FSS, que permet una distància de lectura raonable amb capacitats de detecció precises i confiables, que s'ha discutit en aquesta tesi. L'objectiu d'aquesta tesi és investigar múltiples tecnologies sense fils per dispositius portàtils per identificar solucions adequades per aplicacions particulars en el camp mèdic. El primer objectiu és demostrar la facilitat d'ús de les tecnologies econòmiques sense bateria com un indicador útil de paràmetres fisiopatològics mitjançant la investigació de les propietats de les etiquetes RFID. A més a més, s'ha abordat un aspecte més complex respecte a l'ús de petits components passius com sensors sense fils per trastorns del son. Per últim, un altre objectiu de la tesi és el desenvolupament d'un sistema completament autònom que utilitzi tecnologia BLE per obtenir propietats avançades mantenint baix tant el consum com el preuEsta tesis aborda el problema de encontrar soluciones confortables, inalámbricas y de baja potencia para aplicaciones médicas. La tesis discute las ventajas y limitaciones de tres tecnologías de comunicación diferentes para la medición en el cuerpo y los métodos para elegir y remodelar los sensores para evaluaciones óptimas centradas en el cuerpo. La tecnología RFID se considera una de las soluciones más influyentes para superar el consumo de energía limitado debido a la presencia de muchos sensores conectados. Además, la baja energía de Bluetooth se ha estudiado se ha estudiado la tecnologia Bluetooth de baja energia para resolver los problemas de seguridad y la distancia de lectura que, en general, representan el cuello de botella de la RFID para los sensores de cuerpo. Los dispositivos analógicos pueden reducir drásticamente las necesidades de energía debido a los sensores y las comunicaciones, considerando pocos elementos y un método de transmisión simple. Se estudia un método de comunicación completamente pasivo, basado en FSS, que permite una distancia de lectura razonable con capacidades de detección precisas y confiables, que se ha discutido en esta tesis. El objetivo de esta tesis es investigar múltiples tecnologías inalámbricas para dispositivos portátiles para identificar soluciones adecuadas para aplicaciones particulares en campos médicos. El primer objetivo es demostrar la facilidad de uso de las tecnologías económicas sin batería como un indicador útil de dichos parámetros fisiopatológicos mediante la investigación de las propiedades de las etiquetas RFID. Además, se ha abordado un aspecto más complejo con respecto al uso de pequeños componentes pasivos como sensores inalámbricos para enfermedades del sueño. Por último, un resultado de la tesis es desarrollar un sistema completamente autónomo que utilice la tecnología BLE para obtener propiedades avanzadas que mantengan la baja potencia y un precio bajo.This thesis addresses the problem of comfortable, low powered and, wireless solutions for specific body-worn sensing. The thesis discusses advantages and limitations of three different communication technologies for on body measurement and investigate methods to reshape sensors for optimum body-centric assessments. The RFID technology is considered one of the most influential solutions to overcome the limitated power consumption due to the presence of many sensors connected. Further, the Bluetooth low energy has been studied to solve security problems and reading distance that overall represent the bottleneck of the RFID for the body-worn sensors. Analog devices can drastically reduce the energy needs due to the sensors and the communications, considering few elements and a simple transmitting method. An entirely passive communication method, based on FSS is studied, enabling a reasonable reading distance with precise and reliable sensing capabilities, which has been discussed in this thesis. The objective of this thesis is to investigate multiple wireless technologies for wearable devices to identify suitable solutions for particular applications in medical fields. The first objective is to demonstrate the usability of the inexpensive battery-less technologies as a useful indicator of such a physio-pathological parameters by investigating the properties of the RFID tags. Furthermore, a more complex aspect regards the use of small passive components as wireless sensors for sleep diseases has been addressed. Lastly, an outcome of the thesis is to develop an entirely autonomous system using the BLE technology to obtain advanced properties keeping low power and a low price

Updates of Wearing Devices (WDs) In Healthcare, And Disease Monitoring

 With the rising pervasiveness of growing populace, aging and chronic illnesses consistently rising medical services costs, the health care system is going through a crucial change from the conventional hospital focused system to an individual-focused system. Since the twentieth century, wearable sensors are becoming widespread in medical care and biomedical monitoring systems, engaging consistent estimation of biomarkers for checking of the diseased condition and wellbeing, clinical diagnostics and assessment in biological fluids like saliva, blood, and sweat. Recently, the improvements have been centered around electrochemical and optical biosensors, alongside advances with the non-invasive monitoring of biomarkers, bacteria and hormones, etc. Wearable devices have created with a mix of multiplexed biosensing, microfluidic testing and transport frameworks incorporated with flexible materials and body connections for additional created wear ability and effortlessness. These wearables hold guarantee and are fit for a higher understanding of the relationships between analyte focuses inside the blood or non-invasive biofluids and feedback to the patient, which is fundamentally significant in ideal finding, therapy, and control of diseases. In any case, cohort validation studies and execution assessment of wearable biosensors are expected to support their clinical acceptance. In the current review, we discussed the significance, highlights, types of wearables, difficulties and utilizations of wearable devices for biological fluids for the prevention of diseased conditions and real time monitoring of human wellbeing. In this, we sum up the different wearable devices that are developed for health care monitoring and their future potential has been discussed in detail

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Non-Intrusive Gait Recognition Employing Ultra Wideband Signal Detection

A self-regulating and non-contact impulse radio ultra wideband (IR-UWB) based 3D human gait analysis prototype has been modeled and developed with the help of supervised machine learning (SML) for this application for the first time. The work intends to provide a rewarding assistive biomedical application which would help doctors and clinicians monitor human gait trait and abnormalities with less human intervention in the fields of physiological examinations, physiotherapy, home assistance, rehabilitation success determination and health diagnostics, etc. The research comprises IR-UWB data gathered from a number of male and female participants in both anechoic chamber and multi-path environments. In total twenty four individuals have been recruited, where twenty individuals were said to have normal gait and four persons complained of knee pain that resulted in compensated spastic walking patterns. A 3D postural model of human movements has been created from the backscattering property of the radar pulses employing understanding of spherical trigonometry and vector fields. This subjective data (height of the body areas from the ground) of an individual have been recorded and implemented to extract the gait trait from associated biomechanical activity and differentiates the lower limb movement patterns from other body areas. Initially, a 2D postural model of human gait is presented from IR-UWB sensing phenomena employing spherical co-ordinate and trigonometry where only two dimensions such as, distance from radar and height of reflection have been determined. There are five pivotal gait parameters; step frequency, cadence, step length, walking speed, total covered distance, and body orientation which have all been measured employing radar principles and short term Fourier transformation (STFT). Subsequently, the proposed gait identification and parameter characterization has been analysed, tested and validated against popularly accepted smartphone applications with resulting variations of less than 5%. Subsequently, the spherical trigonometric model has been elevated to a 3D postural model where the prototype can determine width of motion, distance from radar, and height of reflection. Vector algebra has been incorporated with this 3D model to measure knee angles and hip angles from the extension and flexion of lower limbs to understand the gait behavior throughout the entire range of bipedal locomotion. Simultaneously, the Microsoft Kinect Xbox One has been employed during the experiment to assist in the validation process. The same vector mathematics have been implemented to the skeleton data obtained from Kinect to determine both the hip and knee angles. The outcomes have been compared by statistical graphical approach Bland and Altman (B&A) analysis. Further, the changes of knee angles obtained from the normal gaits have been used to train popular SMLs such as, k-nearest neighbour (kNN) and support vector machines (SVM). The trained model has subsequently been tested with the new data (knee angles extracted from both normal and abnormal gait) to assess the prediction ability of gait abnormality recognition. The outcomes have been validated through standard and wellknown statistical performance metrics with promising results found. The outcomes prove the acceptability of the proposed non-contact IR-UWB gait recognition to detect gait

State-of-the-art terahertz sensing for food and water security – a comprehensive review

Background: Recently, there has been a dramatic change in the field of terahertz (THz) technology. The recent advancements in the THz radiation sector considering generation, manipulation and detection have brought revolution in this field, which enable the integration of THz sensing systems into real-world. The THz technology presents detection techniques and various issues, while providing significant opportunities for sensing food and water contamination detection. Scope and approach: Many researchers around the world have exploited the potential of invaluable new applications of THz sensing ranging from surveillance, healthcare and recently for food and water contamination detection. The microbial pollution in water and food is one the crucial issues with regard to the sanitary state for drinking water and daily consumption of food. To address this risk, the detection of microbial contamination is of utmost importance, since the consumption of insanitary or unhygienic food can lead to catastrophic illness. Key findings and conclusions: This paper presents a first-time review of the open literature covering the advances in the THz sensing for microbiological contamination of food and water, in addition to state-of-the-art in network architectures, applications and recent industrial developments. With unique superiority, the THz non-destructive detection technology in food inspection and water contamination detection is emerging as a new area of study. With the great progress, some important challenges and future research directions are presented within the field

Damage identification in bridge structures : review of available methods and case studies

Bridges are integral parts of the infrastructure and play a major role in civil engineering. Bridge health monitoring is necessary to extend the life of a bridge and retain safety. Periodic monitoring contributes significantly in keeping these structures operational and extends structural integrity. Different researchers have proposed different methods for identifying bridge damages based on different theories and laboratory tests. Several review papers have been published in the literature on the identification of damage and crack in bridge structures in the last few decades. In this paper, a review of literature on damage identification in bridge structures based on different methods and theories is carried out. The aim of this paper is to critically evaluate different methods that have been proposed to detect damages in different bridges. Different papers have been carefully reviewed, and the gaps, limitations, and superiority of the methods used are identified. Furthermore, in most of the reviews, future applications and several sustainable methods which are necessary for bridge monitoring are covered. This study significantly contributes to the literature by critically examining different methods, giving guidelines on the methods that identify the damages in bridge structures more accurately, and serving as a good reference for other researchers and future works

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Biosensors for Diagnosis and Monitoring

Biosensor technologies have received a great amount of interest in recent decades, and this has especially been the case in recent years due to the health alert caused by the COVID-19 pandemic. The sensor platform market has grown in recent decades, and the COVID-19 outbreak has led to an increase in the demand for home diagnostics and point-of-care systems. With the evolution of biosensor technology towards portable platforms with a lower cost on-site analysis and a rapid selective and sensitive response, a larger market has opened up for this technology. The evolution of biosensor systems has the opportunity to change classic analysis towards real-time and in situ detection systems, with platforms such as point-of-care and wearables as well as implantable sensors to decentralize chemical and biological analysis, thus reducing industrial and medical costs. This book is dedicated to all the research related to biosensor technologies. Reviews, perspective articles, and research articles in different biosensing areas such as wearable sensors, point-of-care platforms, and pathogen detection for biomedical applications as well as environmental monitoring will introduce the reader to these relevant topics. This book is aimed at scientists and professionals working in the field of biosensors and also provides essential knowledge for students who want to enter the field