RESEARCH DIRECTIONS IN CLOUD-BASED DECISION SUPPORT SYSTEMS FOR HEALTH MONITORING USING INTERNET-OF-THINGS DRIVEN DATA ACQUISITION (original) (raw)

Internet-of-Everything Oriented Implementation of Secure Digital Health (D-Health) Systems

The past few decades have witnessed incredible advances in human health care, owing to the invention of devices such as MRI scanners, which allow physicians to monitor personal health in more detail than was ever previously possible. Such advances have drastically improved diagnostic quality and patient health care. Central to this incredible progress was the uncanny ability of technologists and academics to invent ever more useful tools to help physicians, be it the X-ray machine, CT, or MRI scanner. Whereas the aforementioned past-decades' tools aimed at acquiring personal data, the advent of the Internet-of-Things, vast computational power available in the cloud, and new data analytics algorithms will completely change the way we acquire and process medical data to improve health care going forward. In this paper, we conduct a quantitative feasibility study of a Digital Health (D-Health) system that is aimed at acquiring and processing health data using the emerging Internet-of-Everything paradigm. We specifically investigate the technological feasibility of communication, software, and data privacy aspects.

A Cloud-Based Smart IoT Platform for Personalized Healthcare Data Gathering and Monitoring System

ASIANCON, 2023

Several technological and informational technologies are currently developing quickly and profoundly altering our contemporary way of life. The Internet of Things (IoT) has become one of the most well-known and frequently discussed technologies out of all those now in use. Researchers, doctors, and patients are facing a wide range of new opportunities and difficulties as a result of the development of numerous new technologies. Yet, it is becoming increasingly challenging to manage and process the massive amounts of data they produce on local systems. Cloud computing's scalability offers a practical solution to this issue. The goal of this study is to present a cloud-based, intelligent IoT platform for a customized healthcare data gathering and monitoring system. This platform intends to advance healthcare services and improve remote patient monitoring. The system may collect data from various sources, combine it with a flexible semantic web, store it in the cloud for future analysis, and show it in an intuitive manner. It is crucial to prioritize privacy issues while developing a healthcare monitoring system that manages Electronic Medical Records (EMRs). The recommended remedy effectively protects privacy while utilizing standard IoT characteristics.

E - Health Monitoring Using a Centralized - Decentralized System

2019

Contemporary development of Wireless Sensor Networks (WSNs) has enabled ubiquitous sensing and actuating for the creation of communication- actuation architecture known as the Internet of Things. The IOT infrastructure has an indispensable role in the E-health care environment and allows connections between different entities such as human beings (patients, medical staff, etc.), medical devices, wireless sensors etc. Cloud computing which provides rapid delivery of IT services in which resources can be retrieved from the Internet with minimal management effort thereby relying on sharing of resources to achieve coherence. Most of the modern and rapidly increasing trends deal with IOT and cloud together. E - Health smart system supports monitoring of a huge number of patients using WSNs and Cloud. Yet this paradigm doesn’t provide absolute security to the data transmission. There are several threats to data like denial of service attacks and modification or theft of the data. This mig...

An IOT Based Decision Making Model for Analyzing Patient's Record

Our research focuses on real-time health control of different numbers of patients. In clinics / hospitals, patient data must be constantly monitored, such as heart rate (HR), blood pressure (BP) and electrocardiogram (ECG). The proposed system monitors the heart rate, blood pressure and ECG data of the patient's body. Cloud computing with the combination of IOT technology is a new way to manage and process sensors data online. More generally, the problem is specifically to address the challenges of the wireless sensor network and the processing of associated data. Much emphasis has been placed on the hardware and not on the data processing part. In this Paper we try to overcome the inconveniences we faced in the past. The proposal contains a cloud-based forecasting mechanism for data assimilation, filtering and prediction of health for a large number of people. The data is generated and processed using the MATLAB simulation and communicated with the cloud-based server, which in turn combines the different pieces of information to provide accurate health predictions. To achieve this fusion of cloud computing and Iot is used when the data from the different sensors placed in each individual are communicated to the cloud-based server which in turn combines the different information to provide an accurate prediction of health status. The scheme is implemented with the help of MATLAB simulation to process medical care data, such as heart rate, ECG and BP. This allowed doctors and health professionals to connect remotely with patients and provide appropriate care.

Access to Health Data from Mobile Devices,Assisted by the Cloud

International journal of engineering research and technology, 2018

Privacy issues are increasingly becoming a cause of headaches for cloud users. Increasing use of cloud service models and e-healthcare systems has helped us to propose a private healthcare system with the health of a private cloud. Our system offers features of a smart key management system, privacy preservation of health data and retrieval with retrieval during emergencies and auditability. Specifically, we integrate key management from pseudorandom number generation for unlink ability, secure indexing method by preserving keyword search that hides access and search patterns and integrates the concept of attribute based patterns based on redundancy with attribute based encryption and threshold signing for role based access to prevent potential misbehavior in both normal and emergency cases. Also the user interface is customizable as the user wishes.

Data Security Approach in Remote Healthcare Monitoring

2023

In the context of an increased need to develop home care systems such as health monitoring systems, this paper presents an innovative system, namely Ro-SmartAgeing system. It represents a concrete, integrative, telemedicine solution, based on the collection, remote storage, and real-time processing of medical data. The Ro-SmartAgeing system reveals the potential of the emerging technologies, the Internet of Things, cloud computing, Big Data, and predictive analytics to induce a positive transformation of healthcare services offered to older people. This paper also focuses on the presentation of two fundamental security mechanisms, which are necessary in telemedicine, and which were employed in order to implement a secure flow of medical data within the Ro-SmartAgeing Integrated Information System.

Emerging information technologies for enhanced healthcare

Computers in Industry, 2015

The appropriate collection and consumption of electronic health information about an individual patient or population is the bedrock of modern healthcare, where electronic medical records (EMR) serve as the main carrier. This paper first introduces the main goal of this special issue and gives a brief guideline. Then, the present situation of the adoption of EMRs is reviewed. After that, the emerging information technologies are presented which have a great impact on the healthcare provision. These include health sensing for medical data collection, medical data analysis and utilization for accurate detection and prediction. Next, cloud computing is discussed, as it may provide scalable and cost-effective delivery of healthcare services. Accordingly, the current state of academic research is documented on emerging information technologies for new paradigms of healthcare service. At last, conclusions are made. ß

A Cloud Computing Solution for Patient's Data Collection in Health Care Institutions

2010 Second International Conference on eHealth, Telemedicine, and Social Medicine, 2010

Existing processes for patients' vital data collection require a great deal of labor work to collect, input and analyze the information. These processes are usually slow and error-prone, introducing a latency that prevents real-time data accessibility. This scenario restrains the clinical diagnostics and monitoring capabilities. We propose a solution to automate this process by using ¿sensors¿ attached to existing medical equipments that are interconnected to exchange service. The proposal is based on the concepts of utility computing and wireless sensor networks. The information becomes available in the ¿cloud¿ from where it can be processed by expert systems and/or distributed to medical staff. The proof-of-concept design applies commodity computing integrated to legacy medical devices, ensuring cost-effectiveness and simple integration.

Toward Practical Privacy-Preserving Analytics for IoT and Cloud-Based Healthcare Systems

IEEE Internet Computing, 2018

Modern healthcare systems now rely on advanced computing methods and technologies, such as Internet of Things (IoT) devices and clouds, to collect and analyze personal health data at an unprecedented scale and depth. Patients, doctors, healthcare providers, and researchers depend on analytical models derived from such data sources to remotely monitor patients, early-diagnose diseases, and find personalized treatments and medications. However, without appropriate privacy protection, conducting data analytics becomes a source of a privacy nightmare. In this article, we present the research challenges in developing practical privacypreserving analytics in healthcare information systems. The study is based on kHealth-a personalized digital healthcare information system that is being developed and tested for disease monitoring. We analyze the data and analytic requirements for the involved parties, identify the privacy assets, analyze existing privacy substrates, and discuss the potential tradeoff among privacy, efficiency, and model quality.