Health Monitoring Framework Research Articles

Fog-based Remote in-Home Health Monitoring Framework

As a result of what happened to the world during the past and current year of the spread of the Covid-19 epidemic, it was necessary to have a reliable health care system for remote observation, especially in care homes for the elderly. There are many research works have been done in this field, but still have limitations in terms of latency, security, response delay, and long execution times. To remove these limitations, this paper introduces a Smart Healthcare Framework called Remote in-Home Health Monitoring (RHHM), which provides an architecture and functionalities in order to facilitate the control of patients' conditions when they are at home. The framework exploits the benefits of fog layers with high-level services such as local storage, local real-time data processing, and embedded data mining for taking responsibility for handling some burdens of the sensor network and the cloud and to become a decision maker. In addition to, it incorporates camera with body sensors in diagnosis for more reliability and efficiency with privacy preserving. The performance of the proposed framework was evaluated using the popular iFogSim toolkit. The results show the proposed system's ability to reduce latency, energy consumption, network communications, and overall response time. The efforts of this work will help support the overall goal to establish a high performance, secured and reliable smart Healthcare system.

Piezoresistive detection of simulated hotspots and the effects of low velocity impact at the mesoscale in nanocomposite bonded energetic materials via multiphysics peridynamics modeling

The growth of hotspots within polymer bonded explosives can lead to thermal decomposition and subsequent initiation of the energetic material. An embedded structural health monitoring framework is proposed where carbon nanotubes are embedded within the polymer binder medium of polymer bonded explosives. The presence of the distributed carbon nanotube network enables the detection of hotspots through the analysis of the piezoresistive sensing response. The piezoresistive response correlates changes in the resistivity with changes in the mesoscale strain and damage distribution. The focus of the present study is on the assessment of the detection of hotspots at the mesoscale through analysis of the mesoscale thermo-electro-mechanical response via a multiphysics peridynamics modeling framework. The framework is also applied to assess the combined effects of thermal loading due to the hotspots with inertial effects due to low velocity impact loading. Prescribed hotspots were randomly seeded at various locations within the energetic material and modeled as regions with fast rising temperatures up to and beyond the point of thermal damage initiation. It has been found that the present model is able to detect the presence of hotspot dominated regions within the energetic material through the piezoresistive sensing mechanism. The influence of prescribed hotspots on the thermo-electro-mechanical response of the energetic material under a combination of thermal and inertial loading was observed to dominate the lower velocity impact response via thermal shock damage. In contrast, the higher velocity impact energies demonstrated an inertially dominated damage response, with the transition between the two damage types occurring at an impact energy around 3.75×105J/m3.

FedHome: Cloud-Edge Based Personalized Federated Learning for In-Home Health Monitoring

In-home health monitoring has attracted great attention for the ageing population worldwide. With the abundant user health data accessed by Internet of Things (IoT) devices and recent development in machine learning, smart healthcare has seen many successful stories. However, existing approaches for in-home health monitoring do not pay sufficient attention to user data privacy and thus are far from being ready for large-scale practical deployment. In this paper, we propose FedHome, a novel cloud-edge based federated learning framework for in-home health monitoring, which learns a shared global model in the cloud from multiple homes at the network edges and achieves data privacy protection by keeping user data locally. To cope with the imbalanced and non-IID distribution inherent in user’s monitoring data, we design a generative convolutional autoencoder (GCAE), which aims to achieve accurate and personalized health monitoring by refining the model with a generated class-balanced dataset from user’s personal data. Besides, GCAE is lightweight to transfer between the cloud and edges, which is useful to reduce the communication cost of federated learning in FedHome. Extensive experiments based on realistic human activity recognition data traces corroborate that FedHome significantly outperforms existing widely-adopted methods.

Model-driven fatigue crack characterization and growth prediction: A two-step, 3-D fatigue damage modeling framework for structural health monitoring

Prevailing fatigue damage evaluation approaches that make use of the acoustic nonlinearity of guided ultrasonic waves (GUWs) are sustained by simplified models, most of which depict three-dimensional (3-D) fatigue damage in a two-dimensional (2-D) domain [1]. Such approximation risks the evaluation accuracy. With such motivation, this study aspires to a new, two-step modeling framework, aimed at accurately characterizing and continuously monitoring fatigue damage, from its embryonic initiation, through progressive growth to formation of macroscopic crack. In the first step, a 3-D, analytical model based on the theory of elastodynamics sheds light on the generation of contact acoustic nonlinearity in GUWs under the modulation of `breathing' behavior of a non-penetrating fatigue crack, on which basis a crack-area-dependent nonlinear damage index is yielded. In the second step, a 3-D fatigue crack growth model predicts the continuous growth of the identified fatigue crack in the length and depth along crack front. The framework is validated using numerical simulation, followed with experiment, in both of which the initiation and progressive growth of a real corner fatigue crack is monitored, with continuous prediction of the crack growth in length and depth. Results have demonstrated the accuracy and precision of the developed modeling framework for characterizing embryonic fatigue damage.

Integrated Health Monitoring for the actuation system of high-speed tilting trains

Tilting trains are designed to reach high speed on pre-existing railroads without the need of adjusting the tracks geometry or building dedicated lines; the tilting of the carbody keeps an acceptable level of comfort by limiting the lateral acceleration felt by passengers when the train runs along curved tracks with speed higher than the balance speed built into the curve geometry. As such, they are often used to reduce travel times on routes with several curves. Tilting is performed through a position-controlled actuation system which operates according to the commands received from the train control system: in the studied configuration, the torque needed to tilt the car body with respect to the bogie is provided by a series of hydraulic actuators, while the position information used to close the control loop comes from two capacitive sensors located in the front and rear part of each vehicle. Tilt angle measurement is vital for the system operation and for ensuring a safe ride; the traditional solution in case of discrepancy between the signals of the two tilt angle sensors of any vehicle is to disable the tilting function while limiting the train speed to avoid issues during changes of direction. In a similar fashion, the failure in one (or more) of the tilting actuators would result in the loss of the tilting capability and the return to a fixed configuration operating at reduced speed. It should be noticed that the negative impact of the loss of the tilting system is not limited to the faulty train, since it might affect the entire traffic schedule on the interested lines. The paper presents an integrated Health Monitoring framework that makes intelligent use of all available information thus enhancing the system availability, allowing its operation even in presence of faulty sensors and detecting the onset of failures in the actuation system. At the same time its use can facilitate maintenance organization, simplify the spare parts logistics and provide help to the traffic management. The proposed framework has been developed taking advantage of a high-fidelity model of the physical system validated through comparison with experimental mission profiles on the Lichtenfels - Saalfeld and Battipaglia - Reggio Calabria routes, which have been used by the train manufacturer to assess the performance of their tilting trains.

Development of novel framework for patient health monitoring system using VANET: an Indian perspective

In a country like India, it is very challenging to provide medical facilities to such a large population. The lack of professionals and super-specialty hospitals has made this problem more acute. Due to excessive vehicles on the roads, the death rate in road accidents has increased a lot. Road accidents share a huge portion of casualties that happen every year across the world. VANET is a growing technology in the field of wireless communication that has enhanced the traffic system and helped in reducing road accidents. Wireless body sensor network provides a continuous health monitoring of a patient without hammering daily life activities and can transmit physical health information over wireless networks in real-time. Integration of WBSN and VANET can be used to develop a framework for remote health monitoring, smart accident detection, real-time alerts to the ambulance, and optimizing the traffic for the ambulance. In this paper, we have presented India’s road traffic safety and accident scenario. We have identified the major causes of road accidents in India. We have given an overview of VANET and WBSN related technologies, their requirements, and applications. Moreover, we proposed a framework of WBSN and VANET based healthcare monitoring systems to provide emergency treatment during traveling in Indian perspectives. We have critically analyzed the implementation issues of the proposed health monitoring system in India.

A probabilistic risk-based decision framework for structural health monitoring

Obtaining the ability to make informed decisions regarding the operation and maintenance of structures, provides a major incentive for the implementation of structural health monitoring (SHM) systems. Probabilistic risk assessment (PRA) is an established methodology that allows engineers to make risk-informed decisions regarding the design and operation of safety-critical and high-value assets in industries such as nuclear and aerospace. The current paper aims to formulate a risk-based decision framework for structural health monitoring that combines elements of PRA with the existing SHM paradigm. As an apt tool for reasoning and decision-making under uncertainty, probabilistic graphical models serve as the foundation of the framework. The framework involves modelling failure modes of structures as Bayesian network representations of fault trees and then assigning costs or utilities to the failure events. The fault trees allow for information to pass from probabilistic classifiers to influence diagram representations of decision processes whilst also providing nodes within the graphical model that may be queried to obtain marginal probability distributions over local damage states within a structure. Optimal courses of action for structures are selected by determining the strategies that maximise expected utility. The risk-based framework is demonstrated on a realistic truss-like structure and supported by experimental data. Finally, a discussion of the risk-based approach is made and further challenges pertaining to decision-making processes in the context of SHM are identified.

Monitoring health and well-being of children and adolescents in Germany since 2003

Abstract Background KiGGS - the German Health Interview and Examination Survey for Children and Adolescents is part of the German health monitoring framework providing data analyses and recommendations for politics based on own primary data collection and secondary data from other sources. In 1998, the Robert Koch Institute was commissioned by the Federal Ministry of Health to develop approaches and instruments for a health survey for children and adolescents aged 0 to 17 years. Methods KiGGS is conducted at regular intervals and acts as a central source of information that collects wide-ranging, reliable data on child and adolescent health. KiGGS comprises a cross-sectional and a longitudinal component. Until now, three nationally representative surveys have been performed: KiGGS baseline (2003-2006), Wave 1 (2009-2012) and Wave 2 (2014-2017). The baseline sample comprises 17,641 children and adolescents. The KiGGS cohort is the longitudinal component of the study. To date, two follow-ups have been accomplished. Results The repeated cross-sectional surveys have provided a profound data basis in order to calculate prevalences and conduct context analyses for each period and identifying changes over time in physical and mental health status, health behaviour, utilisation of health care services and prevention as well as social, family and environmental factors. Additionally, the longitudinal data enable to analyse developments in health and their influencing factors during the life course. Reports and recommendations based on KiGGS data have been useful to support the implementation of national health programs for children and adolescents, e.g. for the prevention of childhood obesity. Conclusions Regular health interview and examination surveys are necessary to provide reliable data to derive recommendations for health politics. Furthermore, they offer a robust method to evaluate actions taken to prevent illness and promote positive health behaviour on the population level.

Shape sensing and damage identification with iFEM on a composite structure subjected to impact damage and non-trivial boundary conditions

The inverse Finite Element Method (iFEM) has recently demonstrated to be a valuable tool, not only for shape sensing, but also for damage identification in a Structural Health Monitoring framework. The algorithm computes the displacement field, and consequently the strain field, based on discrete input strain measures in the structure without any a-priori knowledge of the material properties or the loading condition. In particular, the reconstruction is obtained by a least-squares minimization of an error functional defined by comparing the experimental strain measures with their numerical formulation, function of the displacements. Even though the formulation of the method is general for any arbitrary geometry and constraints, the definition of the correct boundary conditions is not trivial for structures subjected to non-ideal constraints. Thus, the work proposes a superimposition of the effects approach to weight the contribution of different basic models to simulate the real behavior of the structure. Once the component displacement and strain fields are correctly reconstructed, the structural assessment is performed by means of a load-independent anomaly index based on the comparison of the numerical strain reconstructions and their experimental counterparts. The discrepancies with respect to a load-independent baseline are highlighted by a Mahalanobis distance index. The procedure is experimentally verified on a CFRP reinforced panel subjected to impact damage and a compressive fatigue test.

P2D: An Efficient Patient-to-Doctor Framework for Real-Time Health Monitoring and Decision Making

Today, diseases and illnesses are becoming the most dangerous enemy to humans. The number of patients is increasing day after day accompanied with the emergence of new types of viruses and diseases. Indeed, most hospitals suffer from the deficiency of qualified staff needed to continuously monitor patients and act when an urgent situation is detected. Recently, wireless body sensor network (WBSN) has been considered as an efficient technology for real-time health-monitoring applications. It provides a low cost solution for hospitals, performs a relief for staff and allows doctors to remotely track patients. However, the huge amount of data collected by sensors produce two major challenges for WBSN: the quickly depletion of the available sensor energy and the complex decision making by the doctor. In this article, we propose an efficient Patient-to-Doctor (P2D) framework for real-time health monitoring and decision making. P2D works on two levels: sensors and coordinator. At the sensor level, P2D allows to save the sensor energy, by adapting its sensing frequency, and to directly detect any abnormal situation of the patient. Whilst, at the coordinator level, P2D allows to store an archive for each patient, predict the patient situation during the next periods of time and make a suitable decision by the doctors. We conducted a set of simulations on real health data in order to show the relevance of our platforms compared to other existing systems.

Data-driven damage identification technique for steel truss railroad bridges utilizing principal component analysis of strain response

Railway bridges are a critical part of the railway infrastructure system and the majority of these bridges are approaching their expected design lifespan. These bridges need to be maintained effectively. This article proposes a damage detection framework for truss railway bridges based on operational strain responses. The method relies on principal component analysis (PCA) of strain responses of the truss bridge. Strain time-history responses under baseline and damaged bridge conditions are used to compute the principal components which are then ranked based on their corresponding eigenvectors. The results are demonstrated in terms of damage indicators which is obtained by comparing the geometric distance of coordinates of the principal component space between the baseline and damaged bridge condition. The method is numerically verified through a finite element model of a truss railroad bridge with added artificial noise. It is shown that the proposed method could identify, locate and relatively assess the severity of the damage induced by stiffness change (at least 20% to as low as 10%, depending on operational variability and measurement noise level) in instrumented truss elements. This method can be useful in assisting the existing bridge maintenance techniques and formulating an effective structural health monitoring framework.

Internet of Things Framework for Structural Health Monitoring in Nigeria

Internet of Things Framework for Structural Health Monitoring in Nigeria

Online acoustic emission source localization in concrete structures using iterative and evolutionary algorithms

This study is motivated by the need to develop an efficient online structural health monitoring (SHM) framework to accurately localize damage induced acoustic emission (AE) sources in concrete structures. Experimental studies are carried out in concrete slabs considering pencil lead break (PLB) as artificial damage source to initialize acoustic emission (AE) waves. A simplified yet robust Iterative Planar Source (IPS) localization algorithm based on arrival time (ToA) is proposed first to identify arbitrarily selected several damage source locations for (1) rectangular (2)circular, and (3) zig-zag distributed AE sensor network arrangements. The results of the proposed localization algorithm are then compared with those obtained from an evolutionary particle-swarm optimization (PSO) algorithm for each sensor network arrangement to assess the performance of the AE source monitoring strategy. It is found that the zig-zag arrangement of the distributed AE sensors is the most efficient sensor network arrangement for damage source localization. The obtained results clearly represent the accuracy and robustness of the proposed online monitoring framework for localizing damage induced acoustic emission sources in concrete structures without extensive manual intervention.

Deep residual network framework for structural health monitoring

Convolutional neural networks have been widely employed for structural health monitoring and damage identification. The convolutional neural network is currently considered as the state-of-the-art method for structural damage identification due to its capabilities of efficient and robust feature learning in a hierarchical manner. It is a tendency to develop a convolutional neural network with a deeper architecture to gain a better performance. However, when the depth of the network increases to a certain level, the performance will degrade due to the gradient vanishing issue. Residual neural networks can avoid the problem of vanishing gradients by utilizing skip connections, which allows the information flowing to the next layer through identity mappings. In this article, a deep residual network framework is proposed for structural health monitoring of civil engineering structures. This framework is composed of purely residual blocks which operate as feature extractors and a fully connected layer as a regressor. It learns the damage-related features from the vibration characteristics such as mode shapes and maps them into the damage index labels, for example, stiffness reductions of structures. To evaluate the efficacy and robustness of the proposed framework, an intensive evaluation is conducted with both numerical and experimental studies. The comparison between the proposed approach and the state-of-the-art models, including a sparse autoencoder neural network, a shallow convolutional neural network and a convolutional neural network with the same structure but without skip connections, is conducted. In the numerical studies, a 7-storey steel frame is investigated. Four scenarios with considering measurement noise and finite element modelling errors in the data sets are studied. The proposed framework consistently outperforms the state-of-the-art models in all the scenarios, especially for the most challenging scenario, which includes both measurement noise and uncertainties. Experimental studies on a prestressed concrete bridge in the laboratory are conducted. The proposed framework demonstrates consistent damage prediction results on this beam with the state-of-the-art models.

Simulation and health monitoring of a pressure regulating station

Pressure regulating stations (PRS) are critical assets of gas distribution networks. Therefore, it is important that they are closely monitored, and potential faults are identified and rectified before the supply of gas is adversely affected. In this work, we have developed a rigorous framework for continuous health monitoring of the PRS. We first develop a mathematical model that can accurately mimic the behavior of the PRS. The physical model for a PRS improves on existing works in the literature by incorporating a real gas law through a metamodel for the real gas equation using artificial neural network. We have highlighted various potential health issues in a PRS and proposed several automated strategies to monitor them. The proposed framework uses both model-based as well as data-based techniques depending on the nature of available operational data. We have illustrated the potential of our framework using a case study on a PRS with 1 year of simulated data.

Healthcare Monitoring System using Cloud and Machine Learning

The prototype is a working model, incorporating sensors for measuring human parameters like body temperature, heart beat rate. A Raspberry pi microcontroller board is used to analyze the patient's Temperature, heartbeat inputs. This project offers a system that will track the crucial parameters a patient's condition to track continuously. If a patient experiences some critical situation, the unit also triggers an alarm in a patient’s close relative and to the doctor in various methodology. This is very useful for future analyzes and review of the health condition of patients. This project can be adapted for more flexible medical applications, by integrating dental sensors and announcement systems, As a very effective and devoted patient care network, it thus makes it useful in hospitals. The world is facing a widespread problem in recent years, which is increasing the number of elderly people. The home-care dilemma for the elderly is something that is very important. In this, Wireless section is becoming a major platform for many services & applications, Web page tracking is also used here, but also a controller. Paper introduces a standardized health monitoring framework as a step towards the progress that has been made in this department to date.

A Koopman operator approach for machinery health monitoring and prediction with noisy and low-dimensional industrial time series

Data-driven methods for machinery health monitoring and prediction, such as machine learning and statistical pattern recognition techniques, normally requires high quality (less noised), frequently-sampled and large volume time-series data to estimate proper models between system inputs and outputs. However, most of the industrial time-series are highly noisy and only few types of sensory data are available, which challenges the estimation accuracy. This paper proposes a data-driven spectral decomposition framework (denoted as Koopman-CBM) for the machinery health monitoring and prediction problem. Specifically, considering noisy industrial signals in the form of one dimensional time-series, we use the higher-order dynamic mode decomposition (DMD) embeds time-lagged snapshots to increase the spatial complexity of low-dimensional time series and use the total-least-square algorithm to compensate the effect of measurement noise, thereby, extracting accurate dynamical features. The obtained de-noised model characteristics (i.e., eigenvalues, eigenfunctions, Koopman operator) is effective in predicting the system health stages. In parallel, using the Koopman operator as the linear predictor associated with the nonlinear dynamics, we can then perform remaining useful life (RUL) predictions with high accuracy. The experimental validation of the proposed framework is carried out on a rolling bearing datasets for degradation health-stage inspection and RUL prediction. Results show that– compared with other mainstream methods– our approach is capable of identifying the critical degradation stages and achieving higher RUL prediction accuracies.

Nonlinear ultrasonic evaluation of disorderedly clustered pitting damage using an in situ sensor network

Pervasive but insidious, pitting damage—from pitting corrosion in maritime structures through electrical pitting in bearings to debris cloud–induced pitting craters in spacecraft—is a typical modality of material degradation and lesion in engineering assets in harsh service environment. Pitting damage may feature hundreds of clustered, localized craters, cracks, and diverse microscopic defects (e.g. dislocation, micro-voids, and cracks) disorderedly scattered over a wide area. Targeting accurate, holistic evaluation of pitting damage (mainly the existence, location, and size of the pitted area), an insight into the generation of nonlinear features in guided ultrasonic waves (i.e. high-order harmonics) that are triggered by pitting damage, is achieved using a semi-analytical finite element approach, based on which a monotonic correlation between the nonlinear ultrasonic features and the holistic severity of pitting damage is established. With such correlation, a structural health monitoring framework is developed, in conjunction with the use of an in situ sensor network comprising miniaturized piezoelectric wafers, to characterize pitting damage accurately and monitor material deterioration progress continuously. The framework is experimentally validated, in which highly complex pitting damage in a space structure, engendered by a hypervelocity debris cloud, is evaluated precisely.

Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft

The development of an automated health monitoring framework is critical for aviation system safety, especially considering the expected increase in air traffic over the next decade. Conventional approaches such as model-based and exceedance methods have a low detection accuracy and are limited to specific applications. This paper proposes a robust real-time health monitoring framework for detecting performance anomalies, which may impact system safety during flight operations, with high accuracy and generalized applicability. The proposed monitoring framework utilizes sensor data from commercial flight data recorders to predict possible flight performance anomalies. Decimation, a signal processing technique, in conjunction with Savitzky-Golay filtering is utilized to preprocess the dataset and mitigate sampling rate and noise issues that prevent direct usage of historical flight data. Correlation-based feature subset selection is subsequently performed, and these features are used to train a support vector machine that predicts flight performance. With this model, performance anomalies in the test data are automatically detected based on deviations from the predicted flight behavior. The proposed monitoring framework was demonstrated to detect performance anomalies in real-time and exhibited accurate detection capabilities with high computational efficiency.

Dynamic structural health monitoring for concrete gravity dams based on the Bayesian inference

The preservation of concrete dams is a key issue for researchers and practitioners in dam engineering because of the important role played by these infrastructures in the sustainability of our society. Since most of existing concrete dams were designed without considering their dynamic behaviour, monitoring their structural health is fundamental in achieving proper safety levels. Structural Health Monitoring systems based on ambient vibrations are thus crucial. However, the high computational burden related to numerical models and the numerous uncertainties affecting the results have so far prevented structural health monitoring systems for concrete dams from being developed. This study presents a framework for the dynamic structural health monitoring of concrete gravity dams in the Bayesian setting. The proposed approach has a relatively low computational burden, and detects damage and reduces uncertainties in predicting the structural behaviour of dams, thus improving the reliability of the structural health monitoring system itself. The application of the proposed procedure to an Italian concrete gravity dam demonstrates its feasibility in real cases.