Health Monitoring Framework Research Articles

Wearable IoTs and Geo-Fencing Based Framework for COVID-19 Remote Patient Health Monitoring and Quarantine Management to Control the Pandemic

The epidemic disease of Severe Acute Respiratory Syndrome (SARS) called COVID-19 has become a more frequently active disease. Managing and monitoring COVID-19 patients is still a challenging issue for advanced technologies. The first and foremost critical issue in COVID-19 is to diagnose it timely and cut off the chain of transmission by isolating the susceptible and patients. COVID-19 spreads through close interaction and contact with an infected person. It has affected the entire world, and every country is facing the challenges of having adequate medical facilities along with the availability of medical staff in rural and urban areas that have a high number of patients due to the pandemic. Due to the invasive method of treatment, SARS-COVID is spreading swiftly. In this paper, we propose an intelligent health monitoring framework using wearable Internet of Things (IoT) and Geo-fencing for COVID-19 susceptible and patient monitoring, and isolation and quarantine management to control the pandemic. The proposed system consists of four layers, and each layer has different functionality: a wearable sensors layer, IoT gateway layer, cloud server layer, and client application layer for visualization and analysis. The wearable sensors layer consists of wearable biomedical and GPS sensors for physiological parameters, and GPS and Wi-Fi Received Signal Strength Indicator acquisition for health monitoring and user Geo-fencing. The IoT gateway layer provides a Bluetooth and Wi-Fi based wireless body area network and IoT environment for data transmission anytime and anywhere. Cloud servers use Raspberry Pi and ThingSpeak cloud for data analysis and web-based application layers for remote monitoring based on user consent. The susceptible and patient conditions, real-time sensor’s data, and Geo-fencing enables minimizing the spread through close interaction. The results show the effectiveness of the proposed framework.

A quality-based automated procedure for operational modal analysis

Many efforts have been made in the last decade to define Automated Operational Modal Analysis (AOMA) procedures able to process large datasets from long-term monitoring systems. However, some issues are still open and further studies on this topic are needed; in particular following points should be better investigated: i) controlling the tuning parameters to minimize the modelling errors, ii) defining suitable automatization method for processing large datasets and iii) validating the extracted modal parameters. These points are investigated in this paper with the aim of enhancing the current AOMA procedures based on the Stochastic Subspace Identification (SSI) techniques, and the following novelties are introduced: i) a minimization approach for the tuning of the initial parameters in the SSI algorithm, ii) a statistical method to automatically define the cut-off threshold in the hierarchical clustering phase, and iii) a Modal Quality Index (MQI) – ranging from 0 to 1 – to validate the identified modes. The above novelties represent key aspects that allow a real-time check of the modal parameters provided by a monitoring system within a Continuous Structural Health monitoring (CSHM) framework.

A Framework for Maternal Physical Activities and Health Monitoring Using Wearable Sensors.

We propose a physical activity recognition and monitoring framework based on wearable sensors during maternity. A physical activity can either create or prevent health issues during a given stage of pregnancy depending on its intensity. Thus, it becomes very important to provide continuous feedback by recognizing a physical activity and its intensity. However, such continuous monitoring is very challenging during the whole period of maternity. In addition, maintaining a record of each physical activity, and the time for which it was performed, is also a non-trivial task. We aim at such problems by first recognizing a physical activity via the data of wearable sensors that are put on various parts of body. We avoid the use of smartphones for such task due to the inconvenience caused by wearing it for activities such as “eating”. In our proposed framework, a module worn on body consists of three sensors: a 3-axis accelerometer, 3-axis gyroscope, and temperature sensor. The time-series data from these sensors are sent to a Raspberry-PI via Bluetooth Low Energy (BLE). Various statistical measures (features) of this data are then calculated and represented in features vectors. These feature vectors are then used to train a supervised machine learning algorithm called classifier for the recognition of physical activity from the sensors data. Based on such recognition, the proposed framework sends a message to the care-taker in case of unfavorable situation. We evaluated a number of well-known classifiers on various features developed from overlapped and non-overlapped window size of time-series data. Our novel dataset consists of 10 physical activities performed by 61 subjects at various stages of maternity. On the current dataset, we achieve the highest recognition rate of 89% which is encouraging for a monitoring and feedback system.

A Comprehensive Multi-Modal Framework for Plant Health Monitoring

: Plant health monitoring has been a significant field of research since a very long time. The scope of this research work conducted lies in the vast domain of plant pathology with its applications extending in the field of agriculture production monitoring to forest health monitoring. It deals with the data collection techniques based on IOT, pre-processing and post-processing of Image dataset and identification of disease using deep learning model. Therefore, providing a multi-modal end-to-end approach for plant health monitoring. This paper reviews the various methods used for monitoring plant health remotely in a non-invasive manner. An end-to-end low cost framework has been proposed for monitoring plant health by using IOT based data collection methods and cloud computing for a single-point-of-contact for the data storage and processing. The cloud agent gateway connects the devices and collects the data from sensors to ensure a single source of truth. Further, the deep learning computational infrastructure provided by the public cloud infrastructure is exploited to train the image dataset and derive the plant health status

Statistical multivariate monitoring of rolling bearings working under varying operational conditions

In recent years, statistical data-driven approaches have been developed for health monitoring of rolling bearings working under variable operational conditions. Due to the reliance on reference models, most of them encounter with the model mismatch problem. A novel statistical multivariate health monitoring framework is proposed in this article to determine the initial degradation of rolling bearings. On the one hand, multivariate statistical parameters are extracted from original vibration signals to indicate bearing's degradation trajectories from different perspectives, a dynamic autoencoder neural network is extended with a matrix augmentation trick for discovering both dynamic and nonlinear latent representation in the high-dimensional features. On the other hand, an eccentricity index is introduced for measuring information variations in the latent representation, such that bearing health development can be revealed timely. Then, an adaptive threshold is exploited for online monitoring of bearing damage. The effectiveness of the proposed work is substantiated using real vibration signals acquired from two different bearing degradation test rigs. The monitoring performance of the proposed work is evaluated in terms of false alarm rate (FAR). Experimental results confirm that the proposed work can identify early bearing damage under variable working conditions, by circumventing the model mismatch problem. It is found to be more superior than existing techniques.

Online structural health monitoring by model order reduction and deep learning algorithms

Within a structural health monitoring (SHM) framework, we propose a simulation-based classification strategy to move towards online damage localization. The procedure combines parametric Model Order Reduction (MOR) techniques and Fully Convolutional Networks (FCNs) to analyze raw vibration measurements recorded on the monitored structure. First, a dataset of possible structural responses under varying operational conditions is built through a physics-based model, allowing for a finite set of predefined damage scenarios. Then, the dataset is used for the offline training of the FCN. Because of the extremely large number of model evaluations required by the dataset construction, MOR techniques are employed to reduce the computational burden. The trained classifier is shown to be able to map unseen vibrational recordings, e.g. collected on-the-fly from sensors placed on the structure, to the actual damage state, thus providing information concerning the presence and also the location of damage. The proposed strategy has been validated by means of two case studies, concerning a 2D portal frame and a 3D portal frame railway bridge; MOR techniques have allowed us to respectively speed up the analyses about 30 and 420 times. For both the case studies, after training the classifier has attained an accuracy larger than 85%.

A WBAN-Based Framework for Health Condition Monitoring and Faulty Sensor Node Detection Applying ANN

Remote health monitoring framework using wireless body area network with ubiquitous support is gaining popularity. However, faulty sensor data may prove to be critical. Hence, faulty sensor detection is necessary in sensor-based health monitoring. In this paper, an artificial neural network (ANN)-based framework for learning about health condition of patients as well as fault detection in the sensors is proposed. This experiment is done based on human cardiac condition monitoring setup. Related physiological parameters have been collected using wearable sensors from different people. These data are then analyzed using ANN for health condition identification and faulty node detection. Libelium MySignals HW (eHealth Medical Development Shield for Arduino) v2 sensors such as ECG sensor, pulse oximeter sensor, and body temperature sensor have been used for data collection and ARDINO UNO R3 as microcontroller device. ANN method detects faulty sensor data with classification accuracy of 98%. Experimental results and analyses are given to prove the claim.

External loads identification and shape sensing on an aluminum wing box: An integrated approach

The recent development of the Structural Health Monitoring (SHM) framework has required the simultaneous development of the tools fundamentals for its realization. The shape sensing methods and the loads identification ones have established themselves as crucial tools for the monitoring of aerospace structures. The two families of methods have been developed separately, although the possibility to achieve the knowledge of the displacement field and of the external loads together can enable a further progress in the SHM. In this paper, an integrated approach to simultaneously preform loads identification and shape sensing from discrete strain measurements is proposed. The methods is based on a two-steps process. The first step involves the identification of continuously distributed and concentrated external loads from discrete strain measurements. This step is achieved by discretizing the loads with Finite Elements (FE) and by computing the coefficients of influence between the nodal values of the loads and the discrete strain measurements. The second step reduces the shape sensing inverse problem to a simple direct Finite Element Analysis. The loads identified in the previous step are applied to a refined FE model of the structure and the displacement field is easily obtained through a direct FE analysis. This investigation proves that the two-steps method can accurately identify the loads and the displacement field of a wing box subject to an aerodynamic pressure distribution and a set of concentrated forces, when a sufficient number of strain information is available. When the number of discrete strain measurements decreases or the strains are affected by measurement error, the loads are poorly predicted but the method is still capable of an extremely accurate displacements reconstruction.

Uncertainty Quantification Framework for Autonomous System Tracking and Health Monitoring

This work proposes a perspective towards establishing a framework for uncertainty quantification of autonomous system tracking and health monitoring. The approach leverages the use of a predictive process structure, which maps uncertainty sources and their interaction according to the quantity of interest and the goal of the predictive estimation. It is systematic and uses basic elements that are system agnostic, and therefore needs to be tailored according to the specificity of the application. This work is motivated by the interest in low-altitude unmanned aerial vehicle operations, where awareness of vehicle and airspace state becomes more relevant as the density of autonomous operations grows rapidly. Predicted scenarios in the area of small vehicle operations and urban air mobility have no precedent, and holistic frameworks to perform prognostics and health management (PHM) at the system- and airspace-level are missing formal approaches to account for uncertainty. At the end of the paper, two case studies demonstrate implementation framework of trajectory tracking and health diagnosis for a small unmanned aerial vehicle.

Cognitive intelligence in fog computing-inspired veterinary healthcare

A comprehensive smart home-based health monitoring framework for veterinary is proposed in this research. It focuses on the distant monitoring of the health condition of domestic animals within the home environment by utilizing Internet of Medical Things (IoMT) technology. Fog computing is used to formulate a time-based data accumulation and the corresponding Health Severity Index (HSI) is established for determining the animal’s health severity. Also, Time Sensitivity Parameter (TSP) is specified for time-sensitive veterinary healthcare, for which Self Organized Mapping (SOM) is incorporated. Furthermore, Recurrent Neural Network (RNN) model is utilized to provide predictive healthcare services in a time-sensitive manner. The system is implemented over several challenging datasets for validation purposes. Based on the results, the presented framework can acquire a high measure of Precision (94.60%), Accuracy (94.71%), Sensitivity (94.55%), and F-measure (93.48%).

An artificial neural network methodology for damage detection: Demonstration on an operating wind turbine blade

This study presents a novel artificial neural network (ANN) based methodology within a vibration-based structural health monitoring framework for robust damage detection. The ANN-based methodology establishes the nonlinear relationships between selected damage sensitive features (DSF) influenced by environmental and operational variabilities (EOVs) and their corresponding novelty indices computed by the Mahalanobis distance (MD). The ANN regression model is trained and validated based on a reference state (i.e., a healthy structure). The trained model is used to predict the corresponding MD of new observations. The prediction error between the calculated and predicted MD is used as a new novelty index for damage detection. Firstly, an artificial 2D feature set is generated to illustrate how the limitations of solely using the MD-based novelty index can be overcome by the proposed ANN-based methodology. Secondly, the methodology is implemented in data obtained from an in-operation wind turbine with different artificially induced damage scenarios in one of its blades. Finally, the performance of the proposed methodology is evaluated by the metrics of accuracy, F1-score and Matthews correlation coefficient. The results demonstrate the advantages of the proposed methodology by improving damage detectability in all the different damage scenarios despite the influence of EOVs in both the simulated and real data.

ICovidCare: Intelligent health monitoring framework for COVID-19 using ensemble random forest in edge networks

The COVID-19 outbreak is in its growing stage due to the lack of standard diagnosis for the patients. In recent times, various models with machine learning have been developed to predict and diagnose novel coronavirus. However, the existing models fail to take an instant decision for detecting the COVID-19 patient immediately and cannot handle multiple medical sensor data for disease prediction. To handle such challenges, we propose an intelligent health monitoring and prediction framework, namely the iCovidCare model for predicting the health status of COVID-19 patients using the ensemble Random Forest (eRF) technique in edge networks. In the proposed framework, a rule-based policy is designed on the local edge devices to detect the risk factor of a patient immediately using monitoring Temperature sensor values. The real-time health monitoring parameters of different medical sensors are transmitted to the centralized cloud servers for future health prediction of the patients. The standard eRF technique is used to predict the health status of the patients using the proposed data fusion and feature selection strategy by selecting the most significant features for disease prediction. The proposed iCovidCare model is evaluated with a synthetic COVID-19 dataset and compared with the standard classification models based on various performance matrices to show its effectiveness. The proposed model has achieved 95.13% accuracy, which is higher than the standard classification models.

Human-machine collaboration framework for structural health monitoring and resiliency

Post-earthquake damage assessment can be significantly expedited when machine learning (ML) algorithms supported by sensing technologies are used. ML has some limitations when it comes to seismic damage assessment. ML tools require data from each class while training but in the case of seismic structural health monitoring, vibration data are usually available from the undamaged structures. In this paper, a framework called the human-machine collaboration (H-MC) is proposed. The H-MC framework combines the ML tools and human (domain) expertise for damage assessment of real instrumented buildings with only data from undamaged cases. It uses novelty detection as the ML tool and structure-specific analytical model to enable rapid damage detection. Subsequently, the framework is applied to detect damage in real instrumented buildings. The results showed that the H-MC algorithm correctly detected the damaged cases. It also labeled all the undamaged events accurately eliminating false positive detection. Furthermore, it is revealed that the resiliency of a building can be improved when the H-MC method is implemented over traditional field inspection. The proposed framework can be a viable tool for rapid post-earthquake damage assessment which is essential for improving community resiliency.

Design of Health Monitoring Framework Model using oneM2M Standard

The causes of traffic accidents are affected by human factors. Driver’s illness, such as exhaustion, drowsiness, and other chronic diseases, are the critical reasons for this. The creation of the Internet of Things (IoT) technology has tried to resolve these issues. The emphasis is on tracking and regulating driving safety and conditions. Unfortunately, there is no uniform IoT standard for this device. This study aims to provide a model for monitoring and handling the situation of drivers by combining the E-Health Tracking (EHM) and the Automotive Health and Safety (AHS) frameworks. The results of the system design are referred to as In-vehicle E-Health Monitoring (IV-EHM). The IV-EHM framework model analysis based on the oneM2M standard has been carried out. Based on the study, it can be said that the system has the specified requirements.

A dynamic harmonic regression approach for bridge structural health monitoring

Structural damage in a bridge is defined as a significant deviation in the structural response from its standard operating conditions, not explainable by variations in external environmental and operational effects. However, environmental effects such as temperature fluctuations can cause significant seasonal variations in the structural response of a bridge and can mask its changes due to structural damage. This poses a challenge for structural health monitoring of bridges where reliable diagnosis of damage or deterioration is often related to isolation of the responses. To address it, a statistical damage-detection methodology is introduced where strain data are modelled using a dynamic harmonic regression time-series model. Prediction intervals of multi-step ahead forecasts from the dynamic harmonic regression model are then used as statistical control limits within which the observed phenomenon should fall under standard operating conditions. This single recursive structural health monitoring framework for automatic fitting and multi-step ahead forecasting of 1-min interval time-series strain data includes recorded temperature values and diurnal trends as regressors in the model to account for environmental variations. The potential of this method as a robust automatic structural health monitoring strategy is demonstrated on strain data sampled at 1-min interval from a full-scale damaged pre-stressed concrete bridge – before, during and after repair. The proposed method can capture both sudden and daily changes in structural response due to temperature effects, and a rolling multi-step ahead interval forecast was able to identify damage on back-cast data transitioning from a healthy state to a damaged state.

Structural Damage Identification Based on Integrated Utilization of Inverse Finite Element Method and Pseudo-Excitation Approach.

The attempt to integrate the applications of conventional structural deformation reconstruction strategies and vibration-based damage identification methods is made in this study, where, more specifically, the inverse finite element method (iFEM) and pseudo-excitation approach (PE) are combined for the first time, to give rise to a novel structural health monitoring (SHM) framework showing various advantages, particularly in aspects of enhanced adaptability and robustness. As the key component of the method, the inverse finite element method (iFEM) enables precise reconstruction of vibration displacements based on measured dynamic strains, which, as compared to displacement measurement, is much more adaptable to existing on-board SHM systems in engineering practice. The PE, on the other hand, is applied subsequently, relying on the reconstructed displacements for the identification of structural damage. Delamination zones in a carbon fibre reinforced plastic (CFRP) laminate are identified using the developed method. As demonstrated by the damage detection results, the iFEM-PE method possesses apparently improved accuracy and significantly enhanced noise immunity compared to the original PE approach depending on displacement measurement. Extensive parametric study is conducted to discuss the influence of a variety of factors on the effectiveness and accuracy of damage identification, including the influence of damage size and position, measurement density, sensor layout, vibration frequency and noise level. It is found that different factors are highly correlated and thus should be considered comprehensively to achieve optimal detection results. The application of the iFEM-PE method is extended to better adapt to the structural operational state, where multiple groups of vibration responses within a wide frequency band are used. Hybrid data fusion is applied to process the damage index (DI) constructed based on the multiple responses, leading to detection results capable of indicating delamination positions precisely.

A Framework for Pandemic Prediction Using Big Data Analytics

IoT (Internet of Things) devices and smart sensors are used in different life sectors, including industry, business, surveillance, healthcare, transportation, communication, and many others. These IoT devices and sensors produce tons of data that might be valued and beneficial for healthcare organizations if it becomes subject to analysis, which brings big data analytics into the picture. Recently, the novel coronavirus pandemic (COVID-19) outbreak is seriously threatening human health, life, production, social interactions, and international relations. In this situation, the IoT and big data technologies have played an essential role in fighting against the pandemic. The applications might include the rapid collection of big data, visualization of pandemic information, breakdown of the epidemic risk, tracking of confirmed cases, tracking of prevention levels, and adequate assessment of COVID-19 prevention and control. In this paper, we demonstrate a health monitoring framework for the analysis and prediction of COVID-19. The framework takes advantage of Big data analytics and IoT. We perform descriptive, diagnostic, predictive, and prescriptive analysis applying big data analytics using a novel disease real data set, focusing on different pandemic symptoms. This work's key contribution is integrating Big Data Analytics and IoT to analyze and predict a novel disease. The neural network-based model is designed to diagnose and predict the pandemic, which can facilitate medical staff. We predict pandemic using neural networks and also compare the results with other machine learning algorithms. The results reveal that the neural network performs comparatively better with an accuracy rate of 99%.

IoT-Based Interdigital Capacitance Sensing System for Damage Detection in CFRP-Concrete Structures

In this paper, we present an Internet of Things framework for structural health monitoring. The proposed system detects the delimitation and debonding in composite concrete structures: more specifically, develop an IoT-Based Non-Destructive Test (NDT) to detect debonding between Carbon Fiber sheets and Concrete slabs. An Inter Digital Capacitance Sensor is designed and fabricated to function as the primary detection element. The sensor is embedded within an IoT node that manages the measurement process, captures the measurement location automatically, and performs essential data filtering operations with an attached graphical interface that allows basic control and early-access to the measured data. The IoT node integrates within the larger framework via wireless WiFi connection throughout which the data is transferred and control functions are administrated. The paper presents the system design details including mathematical modeling for the capacitance sensing element, finite element simulation results, and practical setup measured data.

Machine Learning Tools for Long-Term Type 2 Diabetes Risk Prediction

A steady rise has been observed in the percentage of elderly people who want and are still able to contribute to society. Therefore, early retirement or exit from the labour market, due to health-related issues, poses a significant problem. Nowadays, thanks to technological advances and various data from different populations, the risk factors investigation and health issues screening are moving towards automation. In the context of this work, a worker-centric, IoT enabled unobtrusive users health, well-being and functional ability monitoring framework, empowered with AI tools, is proposed. Diabetes is a high-prevalence chronic condition with harmful consequences for the quality of life and high mortality rate for people worldwide, in both developed and developing countries. Hence, its severe impact on humans' life, e.g., personal, social, working, can be considerably reduced if early detection is possible, but most research works in this field fail to provide a more personalized approach both in the modeling and prediction process. In this direction, our designed system concerns diabetes risk prediction in which specific components of the Knowledge Discovery in Database (KDD) process are applied, evaluated and incorporated. Specifically, dataset creation, features selection and classification, using different Supervised Machine Learning (ML) models are considered. The ensemble WeightedVotingLRRFs ML model is proposed to improve the prediction of diabetes, scoring an Area Under the ROC Curve (AUC) of 0.884. Concerning the weighted voting, the optimal weights are estimated by their corresponding Sensitivity and AUC of the ML model based on a bi-objective genetic algorithm. Also, a comparative study is presented among the Finnish Diabetes Risk Score (FINDRISC) and Leicester risk score systems and several ML models, using inductive and transductive learning. The experiments were conducted using data extracted from the English Longitudinal Study of Ageing (ELSA) database.

Multiresolution Dynamic Mode Decomposition (mrDMD) of Elastic Waves for Damage Localisation in Piezoelectric Ceramic

The performance of piezoelectric sensors deteriorated due to the presence of defect, delamination, and corrosion that needed to be diagnosed for the effective implementation of the structural health monitoring (SHM) framework. A novel experimental approach based on Coulomb coupling is devised to visualise the interaction of ultrasonic waves with microscale defects in the Lead Zirconate Titanate (PZT). Multiresolution dynamic mode decomposition (mrDMD) technique in conjunction with image registration, and Kullback Leibler (KL) divergence is utilised to diagnose and localise the surface defect in the PZT. The mrDMD technique extracts the spatiotemporal coherent mode and provides an equation-free architecture to reconstruct underlying system dynamics. Additionally, due to the strong connection between mrDMD and Koopman operator theory, the proposed technique is well suited to resolve the nonlinear and dispersive interaction of elastic waves with boundaries and defects. The mrDMD sequentially decomposes the three-dimensional spatiotemporal data into low and high frequency modes. The spectral modes are sensitive to defects based on the scaling of wavelength with the size of the defect. The error due to offset and distortion was minimised with ad hoc image registration technique. Further, localisation and quantification of defect are performed by evaluating the distance metric of the probability distribution of coherent data of mrDMD acquired from healthy and defected samples. In the arena of big-data that is ubiquitous in SHM, the paper demonstrates an efficient damage localisation algorithm that explores the nonlinear system dynamics using spectral multi-mode resolution techniques by sensitising the damage features.