78,242 research outputs found

    A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms

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    [EN] Continuous monitoring of diabetic patients improves their quality of life. The use of multiple technologies such as the Internet of Things (IoT), embedded systems, communication technologies, artificial intelligence, and smart devices can reduce the economic costs of the healthcare system. Different communication technologies have made it possible to provide personalized and remote health services. In order to respond to the needs of future intelligent e-health applications, we are called to develop intelligent healthcare systems and expand the number of applications connected to the network. Therefore, the 5G network should support intelligent healthcare applications, to meet some important requirements such as high bandwidth and high energy efficiency. This article presents an intelligent architecture for monitoring diabetic patients by using machine learning algorithms. The architecture elements included smart devices, sensors, and smartphones to collect measurements from the body. The intelligent system collected the data received from the patient, and performed data classification using machine learning in order to make a diagnosis. The proposed prediction system was evaluated by several machine learning algorithms, and the simulation results demonstrated that the sequential minimal optimization (SMO) algorithm gives superior classification accuracy, sensitivity, and precision compared to other algorithms.Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. (2020). A Smart Architecture for Diabetic Patient Monitoring Using Machine Learning Algorithms. Healthcare. 8(3):1-16. https://doi.org/10.3390/healthcare80303481168

    Context-aware support for cardiac health monitoring using federated machine learning

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    Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method f developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user

    Patient monitoring under an ambient intelligence setting

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    Springer - Series Advances in Intelligent and Soft Computing, vol. 72In recent years there has been a growing interest in developing Ambient Intelligence based systems in order to create smart environments for user and environmental monitoring. In fact, higher-level monitoring systems with vital information about the user and the environment around him/her represents an improvement of the quality of care provided. In this paper, we propose an architecture that implements a multi-agent user-profile based system for patient monitoring aimed to improve the assistance and health care provided. This system mixes logical based reasoning mechanisms with context-aware technologies. It is also presented a case based on a scenario developed at a major Portuguese healthcare institution

    A Smart Glucose Monitoring System for Diabetic Patient

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    [EN] Diabetic patients need ongoing surveillance, but this involves high costs for the government and family. The combined use of information and communication technologies (ICTs), artificial intelligence and smart devices can reduce these costs, helping the diabetic patient. This paper presents an intelligent architecture for the surveillance of diabetic disease that will allow physicians to remotely monitor the health of their patients through sensors integrated into smartphones and smart portable devices. The proposed architecture includes an intelligent algorithm developed to intelligently detect whether a parameter has exceeded a threshold, which may or may not involve urgency. To verify the proper functioning of this system, we developed a small portable device capable of measuring the level of glucose in the blood for diabetics and body temperature. We designed a secure mechanism to establish a wireless connection with the smartphone.Rghioui, A.; Lloret, J.; Harane, M.; Oumnad, A. (2020). A Smart Glucose Monitoring System for Diabetic Patient. Electronics. 9(4):1-18. https://doi.org/10.3390/electronics9040678S1189

    Body Area Networks

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    Recent technological advances in integrated circuits, wireless networks, and physiological sensing have enabled miniature, lightweight, low power, intelligent monitoring devices to be integrated into a Body Area Network (BAN). This new type of technology hold much promise for future patient health monitoring. BANs promise inexpensive, unobtrusive, and unsupervised ambulatory monitoring during normal daily activities for long periods of time. However, in order for BANs to become ubiquitous and affordable, a number of challenging issues must be resolved, such as integration, standardisation, system design, customisation, security and privacy, and social issues. This paper presents an overview of many of these issues and indeed the background and rationale of body area networks

    SIM2PeD : intelligent monitoring system for prevention of diabetic foot

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    Diabetes is an endocrine chronic disease that causes high blood sugar level, produced by retardation in, or deficiency of, glucose metabolism in the body of the individual with the disease. Neuropathies and/or angiopathies are complications of diabetes that result in changes in the lower limbs which subsequently evolve for the diabetic foot. Diabetic foot represents one of the most devastating complications of diabetes and can lead to ulcerations, amputations and even death. Based on these, the aim of this work was to develop an Intelligent System for Monitoring the Prevention of Diabetic Foot (SIM2PeD), allowing personalized care from each individual routine. The work consists of a platform integrated with a mobile device to capture individuals’ data, entitled Mobile SIM2PeD, and a web device for monitoring the medical patient, titled Web SIM2PeD. Individuals receive alerts regarding care according to their location and activity directly from their smartphones. After capturing, the information is passed to the expert system (Intelligent module) that generates recommendations from the answers. The developed system presents a model of alerts as the best architecture, to the detriment of the pictogram model. The data captured show that slight displacements in frequency caused large variations of answers delivered to the application. The various experiments conducted made the system performed to be specified, and suitable for the remote monitoring of self-care activities in patients with diabetic foot

    An architecture and protocol for smart continuous eHealth monitoring using 5G

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    [EN] Continuous monitoring of chronic patients improves their quality of life and reduces the economic costs of the sanitary system. However, in order to ensure a good monitoring, high bandwidth and low delay are needed. The 5G technology offers higher bandwidth, lower delays and packets loss than previous technologies. This paper presents an architecture for smart eHealth monitoring of chronic patients. The architecture elements include wearable devices, to collect measures from the body, and a smartphone at the patient side in order to process the data received from the wearable devices. We also need a DataBase with an intelligent system able to send an alarm when it detects that it is happening something anomalous. The intelligent system uses machine learning in BigData taken from different hospitals and the data received from the patient to diagnose and generate alarms. Experiment tests have been done to simulate the traffic from many users to the DataBase in order to evaluate the suitability of 5G in our architecture. When there are few users (less than 200 users), we do not find big differences of round trip time between 4G and 5G, but when there are more users, like 1000 users, it increases considerably reaching 4 times more in 4G The Packet Loss is almost null in 4G until 300 users, while in 5G it is possible to keep it null until 700 users. Our results point out that in order to have high number of patients continuously monitored, it is necessary to use the 5G network because it offers low delays and guarantees the availability of bandwidth for all users.This work has been partially supported by the "Ministerio de Educacion, Cultura y Deporte", through the "Ayudas para contratos predoctorales de Formacion del Profesorado Universitario FPU (Convocatoria 2014)". Grant number FPU14/02953.Lloret, J.; Parra-Boronat, L.; Abdullah, MTA.; Tomás Gironés, J. (2017). An architecture and protocol for smart continuous eHealth monitoring using 5G. Computer Networks. 129(2):340-351. https://doi.org/10.1016/j.comnet.2017.05.018S340351129

    Wearable devices for health remote monitor system

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    It is feasible to see how communication and information technology have advanced at a rapid pace in today’s world. The introduction of wearable technology is one aspect contributing to this progress and has the potential to be an innovative solution to healthcare challenges since it may be utilized for illness prevention and maintenance, such as physical monitoring, as well as patient management. In order to solve some of the healthcare challenges, this paper proposes the development of an intelligent health monitoring system with alerts and continuous monitoring using wearable devices capable of collecting biometric data on human health. The concept was then proven by the development of a prototype using sensors connected to a micro-controller which transmits its information via MQTT to a Node-RED powered dashboard that handles the health metrics monitoring. The designed prototype has proven satisfactory to provide evidences that support the developed research questions.info:eu-repo/semantics/acceptedVersio

    User-centered visual analysis using a hybrid reasoning architecture for intensive care units

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    One problem pertaining to Intensive Care Unit information systems is that, in some cases, a very dense display of data can result. To ensure the overview and readability of the increasing volumes of data, some special features are required (e.g., data prioritization, clustering, and selection mechanisms) with the application of analytical methods (e.g., temporal data abstraction, principal component analysis, and detection of events). This paper addresses the problem of improving the integration of the visual and analytical methods applied to medical monitoring systems. We present a knowledge- and machine learning-based approach to support the knowledge discovery process with appropriate analytical and visual methods. Its potential benefit to the development of user interfaces for intelligent monitors that can assist with the detection and explanation of new, potentially threatening medical events. The proposed hybrid reasoning architecture provides an interactive graphical user interface to adjust the parameters of the analytical methods based on the users' task at hand. The action sequences performed on the graphical user interface by the user are consolidated in a dynamic knowledge base with specific hybrid reasoning that integrates symbolic and connectionist approaches. These sequences of expert knowledge acquisition can be very efficient for making easier knowledge emergence during a similar experience and positively impact the monitoring of critical situations. The provided graphical user interface incorporating a user-centered visual analysis is exploited to facilitate the natural and effective representation of clinical information for patient care
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