Service oriented centered e-health solution for monitoring and preventing chronic diseases

The modern and continuously changing lifestyles in almost all parts of the world resulted in an increase in the incidence of chronic diseases (CDs). To reduce risks associated with chronic diseases, health professionals are studying various clinical solutions. As a result of recent advances in sensing technology, wireless communications, and distributed communication, the monitoring of patients\u27 health condition and the elaboration of prevention plans are considered the most promising solutions for the treatment of chronic diseases. In this paper, we propose a novel framework for monitoring chronic diseases and tracking their vital signs. The framework relies on the service orientation concepts and standards to integrate various subsystems. Monitoring of subjects\u27 health condition, using various sensors and wireless devices, aims to proactively detect any risk of chronic diseases. The system will allow generating and customizing preventive plans dynamically according to the subject\u27s health profile and context while considering many impelling parameters. As a proof of concept of our monitoring and tracking schemes, we have considered a case study for which we have collected and analyzed preliminary data

Trustworthy Federated Learning: A Survey

Federated Learning (FL) has emerged as a significant advancement in the field of Artificial Intelligence (AI), enabling collaborative model training across distributed devices while maintaining data privacy. As the importance of FL increases, addressing trustworthiness issues in its various aspects becomes crucial. In this survey, we provide an extensive overview of the current state of Trustworthy FL, exploring existing solutions and well-defined pillars relevant to Trustworthy . Despite the growth in literature on trustworthy centralized Machine Learning (ML)/Deep Learning (DL), further efforts are necessary to identify trustworthiness pillars and evaluation metrics specific to FL models, as well as to develop solutions for computing trustworthiness levels. We propose a taxonomy that encompasses three main pillars: Interpretability, Fairness, and Security & Privacy. Each pillar represents a dimension of trust, further broken down into different notions. Our survey covers trustworthiness challenges at every level in FL settings. We present a comprehensive architecture of Trustworthy FL, addressing the fundamental principles underlying the concept, and offer an in-depth analysis of trust assessment mechanisms. In conclusion, we identify key research challenges related to every aspect of Trustworthy FL and suggest future research directions. This comprehensive survey serves as a valuable resource for researchers and practitioners working on the development and implementation of Trustworthy FL systems, contributing to a more secure and reliable AI landscape.Comment: 45 Pages, 8 Figures, 9 Table

SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases

Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture\u27s components provides information about diseases\u27 recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers\u27 desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture\u27s applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed architecture\u27s objectives, including resource awareness, smart data integration and visualization, cost reduction, and performance guarantee

An automatic mobile-health based approach for EEG epileptic seizures detection

In this article, we develop a comprehensive mobile-based approach, which is able to perform the essential processes needed to automatically analyze and detect epileptic seizures using the information contained in electroencephalography (EEG) signals. We first develop and implement an appropriate combination of different algorithms that resample, smooth, remove artifacts, and constantly and adaptively segment signals to prepare them for further processing. We then improve and fully implement a large variety of features introduced in the literature of epileptic seizures detection. We also select the relevant features to reduce a feature vector space and improve the classification process by developing two automated filter and wrapper selection algorithms. We thoroughly compare between these selection algorithms in terms of redundant features, execution time and classification accuracy through three experiments. We subsequently exploit the selected features as input to a machine learning classifier to detect epileptic seizure states in a reasonable time. We experimentally and theoretically evaluate the scalability of the whole algorithm respectively on patients\u27 data available in standard clinical database and on 500 EEG recordings including 500 seizures. Having efficient and scabble algorithm, we develop two extra algorithms to dynamically acquire and transmit EEG signals from wireless sensors attached to patients and to visualize on mobile devices the obtained processing and analysis results. We finally integrate all our algorithms together along with an android mobile application to implement an effective mobile-based EEG monitoring system where its accuracy is tested on live EEG data

Mobile health architecture for obesity management using sensory and social data

One of the principal causes of several chronic diseases (e.g., diabetes, high cholesterol, and hypertension) is the obesity epidemic in high and middle income countries. Obesity also leads to an increasingly negative effect on public health resources. Therefore, obesity and overweight have to be monitored to mitigate and prevent the potential risks generated from the threat of related diseases and from reducing productivity experienced by businesses. A mobile-health monitoring system includes sensing, transmitting, storing, processing, and analyzing intensive, continuous, and heterogeneous medical data. However, current approaches are standalone mobile applications, augmented mobile applications, or mobile health systems. These approaches only consider simple activities (assess, detect, or control obesity) and rely on a mobile phone to perform complex processing operations on the collected data. Such complex operations need (1) efficient data mining techniques, (2) more memory consumption and processing time, and (3) long life mobile battery. In this work, we develop a new comprehensive mobile architecture for tackling the challenging issues of obesity control, monitoring, and prevention. We introduce a set of business requirements considering stakeholders, sensor devices, and architecture requirements to meet our architecture\u27s objectives. Our architecture system can also help individuals track food intake, lifestyle, calories intake, calories consumption, and exercise activities. We analyze the data collected from continuous monitoring using non-invasive sensors, in addition to the data collected from social communities created to propagate awareness and share appropriate information about the obesity problem and its solution. We develop data mining algorithms and sentiment analysis algorithms and generate intelligent suggestions, warnings, and recommendations to control and mitigate the risk of obesity and its related diseases. We develop schemes for reducing data and saving energy, which minimize the amount of network traffic within the community of sensors. Moreover, we totally implement our architecture system as a collection of Web services organized by the model-view-controller design pattern to write, retrieve, and access data to and from the cloud storage firebase. We finally evaluate the efficacy and scalability of the implemented system using a comprehensive cloud database including entered data, calculated data, sensory data, and social data of 50 underweight, overweight, normal, and obese volunteer subjects. The obtained results show our architecture\u27s objectives are fulfilled

Smart data synchronization in m-Health monitoring applications

Nowadays, mobile applications/devices have become the trends, especially, when they were gradually shifted from basic communication services to supporting more sophisticated service provisioning. Mobile applications are usually very light, are nowadays likely to be often connected to the Internet, and can be used quite easily. However, these applications exhibit some challenges related to limited resources they have access to, including limited processing power, memory, storage size, battery power, and intermittent network connection. In fact, these considerations have to be taken seriously into consideration when developing mobile applications especially if those applications will be used for critical services, for example, to collect and report vital health data over a long period of time. In this paper, we study the use of mobile applications for monitoring patient\u27s vital. Mobile devices, through an application, are connected to body-strapped biosensors to collect and synchronize these parameters with information systems. This synchronization should be done in such a way that the cost of synchronization is kept low and urgent readings are delivered as soon as possible. To optimize the synchronization process and reduce its cost, we propose and validate cost-oriented algorithms. A case study is developed to illustrate the applicability and effectiveness of our innovative techniques in making continuous monitoring an efficient process

Hybrid obesity monitoring model using sensors and community engagement

Obesity has been recognized to be among the principal causes of many chronic diseases such as diabetes, cholesterol, hypertension, and other cardiovascular diseases. Therefore, monitoring, controlling, and preventing obesity will mitigate the risks generated from the complications of these diseases. Comprehensive preventive measures are essential to control the spread of obesity, while healthcare systems should be organized on the basis of locally derived data to provide adequate and affordable care to the increasing groups of overweight and obese people. In this paper, we propose a hybrid model that relies on both data collected from sensors and participatory data collected from a social network community established to provide value-added obesity awareness, monitoring, and prevention. The model encompasses some key smart features including tracking food intake, lifestyle, and exercise activities, generating warnings and recommendations, and triggering interventions whenever needed. Our model also mines the collected data to produce statistical analysis that can be used by health authorities to have a clear picture of the health status of the population and might help in making rational and informed decisions. Moreover, we implement a prototype of our model as a set of Web services using the SOA paradigm and lightweight protocols. Promising results of our prototype are reported and analyzed

Synthesis lectures on computational electromagnetics

Chronic diseases such as diabetes and hypertension have been recognized in the last decade among the principal causes of death in the world. Mitigating and controlling the elicited risks necessitate a continuous monitoring to produce accurate recommendations for both patients and physicians. For patient, it will help in adjusting his/her lifestyles, medications, and sport activities. However, for physicians, it helps in taking guided therapy decision. In this paper, we propose an adaptive Expert System (ES) that relies, not only on a set of rules validated by experts, but also linked to an intelligent continuous monitoring scheme that copes with semi-continuous data streams by implementing smart sensing and pre-processing of data. In addition, we implemented an iterative data analytic technique that learns from the past ES experience to continuously improve clinical decision-making and automatically generates validated advices. These advices are visualized via an application interface. We experimented the proposed system using different scenarios of monitoring blood sugar and blood pressure parameters of a population of patients with chronic diseases. The results we have obtained showed that our ES combined with the intelligent monitoring and analytic techniques provide a high accuracy of collected data and evident-based advices

Closing the loop from continuous M-health monitoring to fuzzy logic-based optimized recommendations

Continuous sensing of health metrics might generate a massive amount of data. Generating clinically validated recommendations, out of these data, to patients under monitoring is of prime importance to protect them from risk of falling into severe health degradation. Physicians also can be supported with automated recommendations that gain from historical data and increasing learning cycles. In this paper, we propose a Fuzzy Expert System that relies on data collected from continuous monitoring. The monitoring scheme implements preprocessing of data for better data analytics. However, data analytics implements the loopback feature in order to constantly improve fuzzy rules, knowledge base, and generated recommendations. Both techniques reduced data quantity, improved data quality and proposed recommendations. We evaluate our solution through a series of experiments and the results we have obtained proved that our fuzzy expert system combined with the intelligent monitoring and analytic techniques provide a high accuracy of collected data and valid advices

Photogrammetric engineering

The shift from common diagnosis practices to continuous monitoring based on body sensors has transformed healthcare from hospital-centric to patient-centric. Continuous monitoring generates huge and continuous amount of data revealing changing insights. Existing approaches to analyze streams of data in order to produce validated decisions relied mostly on static learning and analytics techniques. In this paper, we propose an incremental learning and adaptive analytics scheme relying on evident data and rule-based Decision Support System (DSS). The later continuously enriches its knowledge base with incremental learning information impacting the decision and proposing up-to-date recommendations. Some intelligent features augmented the monitoring scheme with data pre-processing and cleansing support, which helped empowering data analytics efficiency. Generated assistances are viewable to users on their mobile devices and to physician via a portal. We evaluate our incremental learning and analytics scheme using seven well-known learning techniques. The set of experimental scenarios of continuous heart rate and ECG monitoring demonstrated that the incremental learning combined with rule-based DSS afforded high classification accuracy, evidenced decision, and validated assistance