CDPS-IoT: Cardiovascular Disease Prediction System Based on IoT using Machine Learning

Internet of Things, Machine learning, and Cloud computing are the emerging domains of information communication and technology. These techniques can help to save the life of millions in the medical assisted environment and can be utilized in health-care system where health expertise is less available. Fast food consumption increased from the past few decades, which makes up cholesterol, diabetes, and many more problems that affect the heart and other organs of the body. Changing lifestyle is another parameter that results in health issues including cardio-vascular diseases. Affirming to the World Health Organization, the cardiovascular diseases, or heart diseases lead to more death than any other disease globally. The objective of this research is to analyze the available data pertaining to cardiovascular diseases for prediction of heart diseases at an earlier stage to prevent it from occurring. The dataset of heart disease patients was taken from Jammu and Kashmir, India and stored over the cloud. Stored data is then pre-processed and further analyzed using machine learning techniques for the prediction of heart diseases. The analysis of the dataset using numerous machines learning techniques like Random Forest, Decision Tree, Naive based, K-nearest neighbors, and Support Vector Machine revealed the performance metrics (F1 Score, Precision and Recall) for all the techniques which shows that Naive Bayes is better without parameter tuning while Random Forest algorithm proved as the best technique with hyperparameter tuning. In this paper, the proposed model is developed in such a systematic way that the clinical data can be obtained through the use of IoT with the help of available medical sensors to predict cardiovascular diseases on a real-time basis

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

INTEGRATION OF INTERNET OF THINGS AND HEALTH RECOMMENDER SYSTEMS

The Internet of Things (IoT) has become a part of our lives and has provided many enhancements to day-to-day living. In this project, IoT in healthcare is reviewed. IoT-based healthcare is utilized in remote health monitoring, observing chronic diseases, individual fitness programs, helping the elderly, and many other healthcare fields. There are three main architectures of smart IoT healthcare: Three-Layer Architecture, Service-Oriented Based Architecture (SoA), and The Middleware-Based IoT Architecture. Depending on the required services, different IoT architecture are being used. In addition, IoT healthcare services, IoT healthcare service enablers, IoT healthcare applications, and IoT healthcare services focusing on Smartwatch are presented in this research. Along with IoT in smart healthcare, Health Recommender Systems integration with IoT is important. Main Recommender Systems including Content-based filtering, Collaborative-based filtering, Knowledge-based filtering, and Hybrid filtering with machine learning algorithms are described for the Health Recommender Systems. In this study, a framework is presented for the IoT-based Health Recommender Systems. Also, a case is investigated on how different algorithms can be used for Recommender Systems and their accuracy levels are presented. Such a framework can help with the health issues, for example, risk of going to see the doctor during pandemic, taking quick actions in any health emergencies, affordability of healthcare services, and enhancing the personal lifestyle using recommendations in non-critical conditions. The proposed framework can necessitate further development of IoT-based Health Recommender Systems so that people can mitigate their medical emergencies and live a healthy life

Clinical Prediction on ML based Internet of Things for E-Health Care System

Machine learning (ML) is a powerful method for uncovering hidden patterns in data from the Internet of Things (IoT). These hybrid solutions intelligently improve decision-making in a variety of fields, including education, security, business, and healthcare. IoT uses machine learning to uncover hidden patterns in bulk data, allowing for better forecasting and referral systems. IoT and machine learning have been embraced in healthcare so that automated computers may generate medical records, anticipate diagnoses, and, most critically, monitor patients in real time. On different databases, different ML algorithms work differently. The overall outcomes may be influenced by the variance in anticipated results. In the clinical decision-making process, there is a lot of variation in prognostic results. As a result, it's critical to comprehend the various machine learning methods utilised to handle IoT data in the healthcare industry. Machine learning of adaptive neuro fuzzy inference system (ANFIS) algorithms is being used to monitor human health in this suggested effort. The UCI database is used for initial training and validation of machine learning systems. Using the IoT system, the test phase collects the person's heart rate, blood pressure, and temperature. The test stage assesses if the sensor data obtained by the IoT framework can predict any irregularities in the health state. To evaluate the accuracy of the forecast %, statistical analysis is performed on cloud data acquired from the IoT. Other routines are derived from K-neighbour results

A Multi-Tier Distributed fog-based Architecture for Early Prediction of Epileptic Seizures

Epilepsy is the fourth most common neurological problem. With 50 million people living with epilepsy worldwide, about one in 26 people will continue experiencing recurring seizures during their lifetime. Epileptic seizures are characterized by uncontrollable movements and can cause loss of awareness. Despite the optimal use of antiepileptic medications, seizures are still difficult to control due to their sudden and unpredictable nature. Such seizures can put the lives of patients and others at risk. For example, seizure attacks while patients are driving could affect their ability to control a vehicle and could result in injuries to the patients as well as others. Notifying patients before the onset of seizures can enable them to avoid risks and minimize accidents, thus, save their lives. Early and accurate prediction of seizures can play a significant role in improving patients’ quality of life and helping doctors to administer medications through providing a historical overview of patient's condition over time. The individual variability and the dynamic disparity in differentiating between the pre-ictal phase (a period before the onset of the seizure) and other seizures phases make the early prediction of seizures a challenging task. Although several research projects have focused on developing a reliable seizure prediction model, numerous challenges still exist and need to be addressed. Most of the existing approaches are not suitable for real-time settings, which requires bio-signals collection and analysis in real-time. Various methods were developed based on the analysis of EEG signals without considering the notification latency and computational cost to support monitoring of multiple patients. Limited approaches were designed based on the analysis of ECG signals. ECG signals can be collected using consumer wearable devices and are suitable for light-weight real-time analysis. Moreover, existing prediction methods were developed based on the analysis of seizure state and ignored the investigation of pre-ictal state. The analysis of the pre-ictal state is essential in the prediction of seizures at an early stage. Therefore, there is a crucial need to design a novel computing model for early prediction of epileptic seizures. This model would greatly assist in improving the patients' quality of lives. This work proposes a multi-tier architecture for early prediction of seizures based on the analysis of two vital signs, namely, Electrocardiography (ECG) and Electroencephalogram (EEG) signals. The proposed architecture comprises of three tiers: (1) sensing at the first tier, (2) lightweight analysis based on ECG signals at the second tier, and (3) deep analysis based on EEG signals at the third tier. The proposed architecture is developed to leverage the potential of fog computing technology at the second tier for a real-time signal analytics and ubiquitous response. The proposed architecture can enable the early prediction of epileptic seizures, reduce the notification latency, and minimize the energy consumption on real-time data transmissions. Moreover, the proposed architecture is designed to allow for both lightweight and extensive analytics, thus make accurate and reliable decisions. The proposed lightweight model is formulated using the analysis of ECG signals to detect the pre-ictal state. The lightweight model utilizes the Least Squares Support Vector Machines (LS-SVM) classifier, while the proposed extensive analytics model analyzes EEG signals and utilizes Deep Belief Network (DBN) to provide an accurate classification of the patient’s state. The performance of the proposed architecture is evaluated in terms of latency minimization and energy consumption in comparison with the cloud. Moreover, the performance of the proposed prediction models is evaluated using three datasets. Various performance metrics were used to investigate the prediction model performance, including: accuracy, sensitivity, specificity, and F1-Measure. The results illustrate the merits of the proposed architecture and show significant improvement in the early prediction of seizures in terms of accuracy, sensitivity, and specificity

The Role of Edge Robotics As-a-Service in Monitoring COVID-19 Infection

Deep learning technology has been widely used in edge computing. However, pandemics like covid-19 require deep learning capabilities at mobile devices (detect respiratory rate using mobile robotics or conduct CT scan using a mobile scanner), which are severely constrained by the limited storage and computation resources at the device level. To solve this problem, we propose a three-tier architecture, including robot layers, edge layers, and cloud layers. We adopt this architecture to design a non-contact respiratory monitoring system to break down respiratory rate calculation tasks. Experimental results of respiratory rate monitoring show that the proposed approach in this paper significantly outperforms other approaches. It is supported by computation time costs with 2.26 ms per frame, 27.48 ms per frame, 0.78 seconds for convolution operation, similarity calculation, processing one-minute length respiratory signals, respectively. And the computation time costs of our three-tier architecture are less than that of edge+cloud architecture and cloud architecture. Moreover, we use our three-tire architecture for CT image diagnosis task decomposition. The evaluation of a CT image dataset of COVID-19 proves that our three-tire architecture is useful for resolving tasks on deep learning networks by edge equipment. There are broad application scenarios in smart hospitals in the future

System for monitoring and supporting the treatment of sleep apnea using IoT and big data

[EN] Sleep apnea has become in the sleep disorder that causes greater concern in recent years due to its morbidity and mortality, higher medical care costs and poor people quality of life. Some proposals have addressed sleep apnea disease in elderly people, but they have still some technical limitations. For these reasons, this paper presents an innovative system based on fog and cloud computing technologies which in combination with IoT and big data platforms offers new opportunities to build novel and innovative services for supporting the sleep apnea and to overcome the current limitations. Particularly, the system is built on several low-power wireless networks with heterogeneous smart devices (i.e, sensors and actuators). In the fog, an edge node (Smart IoT Gateway) provides IoT connection and interoperability and pre-processing IoT data to detect events in real-time that might endanger the elderly's health and to act accordingly. In the cloud, a Generic Enabler Context Broker manages, stores and injects data into the big data analyzer for further processing and analyzing. The system's performance and subjective applicability are evaluated using over 30 GB size datasets and a questionnaire fulfilled by medicals specialist, respectively. Results show that the system data analytics improve the health professionals' decision making to monitor and guide sleep apnea treatment, as well as improving elderly people's quality of life. (C) 2018 Elsevier B.V. All rights reserved.This research was supported by the Ecuadorian Government through the Secretary of Higher Education, Science, Technology, and Innovation (SENESCYT) and has received funding from the European Union's "Horizon 2020'' research and innovation program as part of the ACTIVAGE project under Grant 732679 and the Interoperability of Heterogeneous IoT Platforms project (INTER-IoT) under Grant 687283.Yacchirema-Vargas, DC.; Sarabia-Jácome, DF.; Palau Salvador, CE.; Esteve Domingo, M. (2018). System for monitoring and supporting the treatment of sleep apnea using IoT and big data. Pervasive and Mobile Computing. 50:25-40. https://doi.org/10.1016/j.pmcj.2018.07.007S25405

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic