2,901 research outputs found

    Intelligent Remote Monitoring and Management system for Type1 Diabetes

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    The work presented in this thesis focuses on developing a telemedicine system for better management of type1 diabetes in children and teenagers. The research and development of the system is motivated by the inadequate communication in the current system of management of the disease, which results in non-compliance of patients following the regimen. This non-compliance generally results in uncontrolled blood glucose levels, which can result in hypoglycaemia, hyperglycaemia and later life health complications. This further results in an increase in health care costs. In this context, the thesis presents a novel end-to-end, low cost telemedicine system, WithCare+, developed in close collaboration between the University of Sheffield (Electronics & Electrical Engineering) and Sheffield Children’s Hospital. The system was developed to address the challenges of implementing modern telemedicine in type 1 diabetic care with particular relevance to National Health Service children’s clinics in the United Kingdom, by adopting a holistic care driven approach (involving all stakeholders) based on specific key enabler technologies such as low cost and reconfigurable design. However, one of the major issues with current telemedicine system is non-compliance of the patients due to invasive procedure of the glucose measurement which could be clearly addressed by non-invasive method of glucose measurement. Hence, the thesis also makes a contribution towards non-invasive glucose measurement using Near Infrared spectroscopy in terms of addressing the calibration challenge; two methods are proposed to improve the calibration of the Near Infrared instrument. The first method combines locally weighted regression and partial least square regression and the second method combines digital band pass filtering with support vector regression. The efficacy of the proposed methods is validated in experiments carried out in a non-controlled environment and the results obtained demonstrate that the proposed methods improved the performance of the calibration model in comparison to traditional calibration techniques such as Principal Component Regression and Partial Least Squares regression

    IoMT-Enabled Real-time Blood Glucose Prediction with Deep Learning and Edge Computing

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    Blood glucose (BG) prediction is essential to the success of glycemic control in type 1 diabetes (T1D) management. Empowered by the recent development of the Internet of Medical Things (IoMT), continuous glucose monitoring (CGM) and deep learning technologies have been demonstrated to achieve the state of the art in BG prediction. However, it is challenging to implement such algorithms in actual clinical settings to provide persistent decision support due to the high demand for computational resources, while smartphone-based implementations are limited by short battery life and require users to carry the device. In this work, we propose a new deep learning model using an attention-based evidential recurrent neural network and design an IoMT-enabled wearable device to implement the embedded model, which comprises a low-cost and low-power system on a chip to perform Bluetooth connectivity and edge computing for real-time BG prediction and predictive hypoglycemia detection. In addition, we developed a smartphone app to visualize BG trajectories and predictions, and desktop and cloud platforms to backup data and fine-tune models. The embedded model was evaluated on three clinical datasets including 47 T1D subjects. The proposed model achieved superior performance of root mean square error (RMSE), mean absolute error, and glucose-specific RMSE, and obtained the best accuracy for hypoglycemia detection when compared with a group of machine learning baseline methods. Moreover, we performed hardware-in-the-loop in silico trials with 10 virtual T1D adults to test the whole IoMT system with predictive low-glucose management, which significantly reduced hypoglycemia and improved BG control

    Machine Learning of Lifestyle Data for Diabetes

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    Self-Monitoring of Blood Glucose (SMBG) for Type-2 Diabetes (T2D) remains highly challenging for both patients and doctors due to the complexities of diabetic lifestyle data logging and insufficient short-term and personalized recommendations/advice. The recent mobile diabetes management systems have been proved clinically effective to facilitate self-management. However, most such systems have poor usability and are limited in data analytic functionalities. These two challenges are connected and affected by each other. The ease of data recording brings better data for applicable data analytic algorithms. On the other hand, the irrelevant or inaccurate data input will certainly commit errors and noises. The output of data analysis, as potentially valuable patterns or knowledge, could be the incentives for users to contribute more data. We believe that the incorporation of machine learning technologies in mobile diabetes management could tackle these challenge simultaneously. In this thesis, we propose, build, and evaluate an intelligent mobile diabetes management system, called GlucoGuide for T2D patients. GlucoGuide conveniently aggregates varieties of lifestyle data collected via mobile devices, analyzes the data with machine learning models, and outputs recommendations. The most complicated part of SMBG is diet management. GlucoGuide aims to address this crucial issue using classification models and camera-based automatic data logging. The proposed model classifies each food item into three recommendation classes using its nutrient and textual features. Empirical studies show that the food classification task is effective. A lifestyle-data-driven recommendations framework in GlucoGuide can output short-term and personalized recommendations of lifestyle changes to help patients stabilize their blood glucose level. To evaluate performance and clinical effectiveness of this framework, we conduct a three-month clinical trial on human subjects, in collaboration with Dr. Petrella (MD). Due to the high cost and complexity of trials on humans, a small but representative subject group is involved. Two standard laboratory blood tests for diabetes are used before and after the trial. The results are quite remarkable. Generally speaking, GlucoGuide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months, depending on their before-trial diabetic conditions. cThis clinical dataset has also been expanded and enhanced to generate scientifically controlled artificial datasets. Such datasets can be used for varieties of machine learning empirical studies, as our on-going and future research works. GlucoGuide now is a university spin-off, allowing us to collect a large scale of practical diabetic lifestyle data and make potential impact on diabetes treatment and management

    Applications of the Internet of Medical Things to Type 1 Diabetes Mellitus

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    Type 1 Diabetes Mellitus (DM1) is a condition of the metabolism typified by persistent hyperglycemia as a result of insufficient pancreatic insulin synthesis. This requires patients to be aware of their blood glucose level oscillations every day to deduce a pattern and anticipate future glycemia, and hence, decide the amount of insulin that must be exogenously injected to maintain glycemia within the target range. This approach often suffers from a relatively high imprecision, which can be dangerous. Nevertheless, current developments in Information and Communication Technologies (ICT) and innovative sensors for biological signals that might enable a continuous, complete assessment of the patient’s health provide a fresh viewpoint on treating DM1. With this, we observe that current biomonitoring devices and Continuous Glucose Monitoring (CGM) units can easily obtain data that allow us to know at all times the state of glycemia and other variables that influence its oscillations. A complete review has been made of the variables that influence glycemia in a T1DM patient and that can be measured by the above means. The communications systems necessary to transfer the information collected to a more powerful computational environment, which can adequately handle the amounts of data collected, have also been described. From this point, intelligent data analysis extracts knowledge from the data and allows predictions to be made in order to anticipate risk situations. With all of the above, it is necessary to build a holistic proposal that allows the complete and smart management of T1DM. This approach evaluates a potential shortage of such suggestions and the obstacles that future intelligent IoMT-DM1 management systems must surmount. Lastly, we provide an outline of a comprehensive IoMT-based proposal for DM1 management that aims to address the limits of prior studies while also using the disruptive technologies highlighted beforePartial funding for open access charge: Universidad de Málag

    EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

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    Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

    A Non-invasive Approach to Detection Blood Glucose Levels with Hand Skin Image Processing Using Smartphone

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    Measuring blood sugar levels today still use invasive techniques that are painful so non-invasive monitoring is needed. This study aims to develop a non-invasive technique to identify and detect blood glucose through hand-skin image processing. This development method is by taking invasive blood glucose hand images and 30 participants aged 20-60 years, data analysis is done by image preprocessing, determining the Gray level co-occurrence matrix (GLCM) value, using the backpropagation algorithm to conduct training and data testing. to define a blood glucose monitoring model. The blood glucose detection model is implemented through the android operating system on smartphones by developing the GULAABLE application on smartphones which is simple and easy to use and without blood sampling. This GULAABLE application is to determine the condition of low or high blood glucose and can be used routinely at a low cost. Validating the results by identifying this non-invasive application compared with the results of invasive glucose measurements by applying to 10 participants, the identification results show an accuracy of 80%, so it can be concluded that the GULAABLE application method on smartphones can be used to monitor blood glucose conditions at any time by simply taking hand skin image

    Research progress on wearable devices for daily human health management

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    As the public’s demand for portable access to personal health information continues to expand, wearable devices are not only widely used in clinical practice, but also gradually applied to the daily health management of ordinary families due to their intelligence, miniaturization, and portability. This paper searches the literature of wearable devices through PubMed and CNKI databases, classifies them according to the different functions realized by wearable devices, and briefly describes the algorithms and specific analysis methods of their applications and made a prospect of its development trend in the field of human health
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