Personalized data analytics for internet-of-things-based health monitoring

The Internet-of-Things (IoT) has great potential to fundamentally alter the delivery of modern healthcare, enabling healthcare solutions outside the limits of conventional clinical settings. It can offer ubiquitous monitoring to at-risk population groups and allow diagnostic care, preventive care, and early intervention in everyday life. These services can have profound impacts on many aspects of health and well-being. However, this field is still at an infancy stage, and the use of IoT-based systems in real-world healthcare applications introduces new challenges. Healthcare applications necessitate satisfactory quality attributes such as reliability and accuracy due to their mission-critical nature, while at the same time, IoT-based systems mostly operate over constrained shared sensing, communication, and computing resources. There is a need to investigate this synergy between the IoT technologies and healthcare applications from a user-centered perspective. Such a study should examine the role and requirements of IoT-based systems in real-world health monitoring applications. Moreover, conventional computing architecture and data analytic approaches introduced for IoT systems are insufficient when used to target health and well-being purposes, as they are unable to overcome the limitations of IoT systems while fulfilling the needs of healthcare applications. This thesis aims to address these issues by proposing an intelligent use of data and computing resources in IoT-based systems, which can lead to a high-level performance and satisfy the stringent requirements. For this purpose, this thesis first delves into the state-of-the-art IoT-enabled healthcare systems proposed for in-home and in-hospital monitoring. The findings are analyzed and categorized into different domains from a user-centered perspective. The selection of home-based applications is focused on the monitoring of the elderly who require more remote care and support compared to other groups of people. In contrast, the hospital-based applications include the role of existing IoT in patient monitoring and hospital management systems. Then, the objectives and requirements of each domain are investigated and discussed. This thesis proposes personalized data analytic approaches to fulfill the requirements and meet the objectives of IoT-based healthcare systems. In this regard, a new computing architecture is introduced, using computing resources in different layers of IoT to provide a high level of availability and accuracy for healthcare services. This architecture allows the hierarchical partitioning of machine learning algorithms in these systems and enables an adaptive system behavior with respect to the user's condition. In addition, personalized data fusion and modeling techniques are presented, exploiting multivariate and longitudinal data in IoT systems to improve the quality attributes of healthcare applications. First, a real-time missing data resilient decision-making technique is proposed for health monitoring systems. The technique tailors various data resources in IoT systems to accurately estimate health decisions despite missing data in the monitoring. Second, a personalized model is presented, enabling variations and event detection in long-term monitoring systems. The model evaluates the sleep quality of users according to their own historical data. Finally, the performance of the computing architecture and the techniques are evaluated in this thesis using two case studies. The first case study consists of real-time arrhythmia detection in electrocardiography signals collected from patients suffering from cardiovascular diseases. The second case study is continuous maternal health monitoring during pregnancy and postpartum. It includes a real human subject trial carried out with twenty pregnant women for seven months

Point Cloud Structural Parts Extraction based on Segmentation Energy Minimization

In this work we consider 3D point sets, which in a typical setting represent unorganized point clouds. Segmentation of these point sets requires first to single out structural components of the unknown surface discretely approximated by the point cloud. Structural components, in turn, are surface patches approximating unknown parts of elementary geometric structures, such as planes, ellipsoids, spheres and so on. The approach used is based on level set methods computing the moving front of the surface and tracing the interfaces between different parts of it. Level set methods are widely recognized to be one of the most efficient methods to segment both 2D images and 3D medical images. Level set methods for 3D segmentation have recently received an increasing interest. We contribute by proposing a novel approach for raw point sets. Based on the motion and distance functions of the level set we introduce four energy minimization models, which are used for segmentation, by considering an equal number of distance functions specified by geometric features. Finally we evaluate the proposed algorithm on point sets simulating unorganized point clouds

Plasma membrane ion channels and epithelial to mesenchymal transition in cancer cells

A variety of studies have suggested that epithelial to mesenchymal transition (EMT) may be important in the progression of cancer in patients through metastasis and/or therapeutic resistance. A number of pathways have been investigated in EMT in cancer cells. Recently, changes in plasma membrane ion channel expression as a consequence of EMT have been reported. Other studies have identified specific ion channels able to regulate aspects of EMT induction. The utility of plasma membrane ion channels as targets for pharmacological modulation make them attractive for therapeutic approaches to target EMT. In this review, we provide an overview of some of the key plasma membrane ion channel types and highlight some of the studies that are beginning to define changes in plasma membrane ion channels as a consequence of EMT and also their possible roles in EMT induction

Online) An Open Access

ABSTRACT The main concern in Wireless Sensor Networks is how to handle with their limited energy resources. The performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. This paper presents a new centralized adaptive Energy Based Clustering protocol through the application of Self organizing map neural networks (called EBC-S) which can cluster sensor nodes, based on multi parameters; energy level and coordinates of sensor nodes. We applied some maximum energy nodes as weights of SOM map units; so that the nodes with higher energy attract the nearest nodes with lower energy levels. Therefore, formed clusters may not necessarily contain adjacent nodes. The new algorithm enables us to form energy balanced clusters and equally distribute energy consumption. Simulation results and comparison with previous protocols (LEACH and LEA2C) prove that our new algorithm is able to extend the lifetime of the network. Keywords: Energy Based Clustering, Self Organizing Map Neural Networks, Wireless Sensor Networks INTRODUCTION The most important difference of Wireless Sensor Network (WSNs) with other wireless networks may be constraints of their resources, especially energy which usually arise from small size of sensor nodes and their batteries which is a prerequisite to WSNs main applications. The main and most important reason of WSNs creation was continuous monitoring of environments where are too hard or impossible for human to access or stay. So there is often low possibility to replace or recharge the dead nodes as well. The other important requirement is that we need a continuous monitoring so the lifetime and network coverage of these networks are our great concerns. As a result, as energy conservation is the main concern in WSNs, but also it should be gained with balanced distribution in whole network space. Balanced distribution of energy in whole network will lead to balanced death of nodes in all regions preventing from lacking network coverage in a rather large part of the network. Energy conservation should be gained by wisely management of energy sources. Several energy conservation schemes have been proposed in the literature while there is a comprehensive survey of energy conservation methods for WSNs and the taxonomy of all into three main approaches (duty-cycling, data reduction, and mobility based approaches

A new method to calculate mathematical morphology using associative memory and cellular learning automata

Abstract: The methods presented in this paper include using auto-associative memory which can be defined as a supervised organizing method which is a specific type of h-sorting which is compatible with the morphologic operators over multi-variables data. Mathematical morphologies for multi-variable images require appropriate sort descriptions that allow us to define and use primitive morphologies operators without any wrong results such as wrong color. All the required calculations are defined with lattice algebra (+,^ and ∨); therefore, the proposed method will be faster with less computation overhead than the previous methods. This method does not use any assumptions of the stochastic process which means that this method is independent of the model. The presented method uses cellular learning automata which results in fewer errors than the mathematical methods due to the feedback from the network

New methods of verification and identification using iris patterns

Abstract: To detect and track eye images, distinctive features of use r eye are used. Generally, an eye-tracking and detection system can be divided into four steps: Face detection, eye region detection, pupil detection and eye tracking. To find the position of pupil, first, face region must be separated from the rest of the image using mixture of Gaussian, this will cause the images background to be non-effective in our next steps. This will result in decreasing the computational complexity and ignoring some factors such as bread. Color entropy in the eye region is used to detect pupil. In the next step, we perform eye tracking. We proposed algorithm for the eye tracking by combining the pupil based Kalman filter with the mean shift algorithm. That use modal recognition technique and is based on pictures whit high equality of eye iris .Iris modals in comparison whit other properties in biometrics system are more resistance and credit .In this paper we use from fractals technique for iris recognition. Fractals are important in these aspects that can express complicated pictures with applying several simple codes. Until, That cause to iris tissue change from depart coordination to polar coordination and adjust for light rates. While performing other pre-process, fault rates will be less than EER, and lead to decreasing recognition time, account table cost and grouping precise improvement

Supporting lifestyle change in obese pregnant mothers through the wearable internet-of-things (SLIM) -intervention for overweight pregnant women : Study protocol for a quasi-experimental trial

ObjectivesTo assess, in terms of self-efficacy in weight management, the effectiveness of the SLIM lifestyle intervention among overweight or obese women during pregnancy and after delivery, and further to exploit machine learning and event mining approaches to build personalized models. Additionally, the aim is to evaluate the implementation of the SLIM intervention.MethodsThis prospective trial, which is a non-randomized, quasi-experimental, pre-post intervention, includes an embedded mixed-method process evaluation. The SLIM Intervention is delivered by public health nurses (n = 9) working in maternity clinics. The public health nurses recruited overweight women (n = 54) at their first antenatal visit using convenience sampling. The core components of the intervention i.e. health technology, motivational interviewing, feedback, and goal setting, are utilized in antenatal visits in maternity clinics starting from gestational week 15 or less and continuing to 12 weeks after delivery. Mixed effect models are used to evaluate change over time in self-efficacy, weight management and weight change. Simple mediation models are used to assess calories consumed and moderate to vigorous physical activity (MVPA) as mediators between self-efficacy and weight change. Signal processing and machine learning techniques are exploited to extract events from the data collected via the Oura ring and smartphone-based questionnaires.DiscussionThe SLIM intervention was developed in collaboration with overweight women and public health nurses working in maternity clinics. This study evaluates the effectiveness of the intervention among overweight women in increasing self-efficacy and achieving a healthy weight; thus, impacting the healthy lifestyle and long-term health of the whole family. The long-term objective is to contribute to women's health by supporting weight-management through behavior change via interventions conducted in maternity clinics.Peer reviewe

Detection of COVID-19 Using Heart Rate and Blood Pressure: Lessons Learned from Patients with ARDS

The world has been affected by COVID-19 coronavirus. At the time of this study, the number of infected people in the United States is the highest globally (7.9 million infections). Within the infected population, patients diagnosed with acute respiratory distress syndrome (ARDS) are in more life-threatening circumstances, resulting in severe respiratory system failure. Various studies have investigated the infections to COVID-19 and ARDS by monitoring laboratory metrics and symptoms. Unfortunately, these methods are merely limited to clinical settings, and symptom-based methods are shown to be ineffective. In contrast, vital signs (e.g., heart rate) have been utilized to early-detect different respiratory diseases in ubiquitous health monitoring. We posit that such biomarkers are informative in identifying ARDS patients infected with COVID-19. In this study, we investigate the behavior of COVID-19 on ARDS patients by utilizing simple vital signs. We analyze the long-term daily logs of blood pressure and heart rate associated with 70 ARDS patients admitted to five University of California academic health centers (containing 42506 samples for each vital sign) to distinguish subjects with COVID-19 positive and negative test results. In addition to the statistical analysis, we develop a deep neural network model to extract features from the longitudinal data. Using only the first eight days of the data, our deep learning model is able to achieve 78.79% accuracy to classify the vital signs of ARDS patients infected with COVID-19 versus other ARDS diagnosed patients

Growth Performance and Body Composition of Pikeperch (Sander lucioperca) Fingerlings under Dietary L-Carnitine

Abstract Growth performance, food efficiency and body composition of pikeperch (Sander lucioperca) were investigated with 1 or 2 g/kg L-carnitine added to the diet. Control diet did not contain L-carnitine. Two hundred pikeperch fingerlings (1.63 g, mean weight) were stocked in each 1 m3 concrete tank and fed equally 6 meals per day for 6 weeks. Higher increment in body weight (5.92 ± 0.37 g), the highest specific growth rate (3.75 ± 0.17) and food efficiency (98.04 ± 4.56), the highest crude protein (64.53 ± 0.84), the lowest crude lipid (21.59 ± 0.23) and significant (P<0.05) lowering in food conversion ratio (1.02 ± 0.05) were obtained with 2 g/kg L-carnitine diet. The greatest survival rate (84.88 ± 0.92) occurred when fingerlings were fed with 1 g/kg L-carnitine. Both treatments with L-carnitine showed lesser rate of cannibalism than in control (1.25± 0.09). Use of dietary L-carnitine improved growth performance and body composition of pikeperch fingerlings