27,955 research outputs found

    On the Deployment of Healthcare Applications over Fog Computing Infrastructure

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    Fog computing is considered as the most promising enhancement of the traditional cloud computing paradigm in order to handle potential issues introduced by the emerging Interned of Things (IoT) framework at the network edge. The heterogeneous nature, the extensive distribution and the hefty number of deployed IoT nodes will disrupt existing functional models, creating confusion. However, IoT will facilitate the rise of new applications, with automated healthcare monitoring platforms being amongst them. This paper presents the pillars of design for such applications, along with the evaluation of a working prototype that collects ECG traces from a tailor-made device and utilizes the patient's smartphone as a Fog gateway for securely sharing them to other authorized entities. This prototype will allow patients to share information to their physicians, monitor their health status independently and notify the authorities rapidly in emergency situations. Historical data will also be available for further analysis, towards identifying patterns that may improve medical diagnoses in the foreseeable future

    Medical data processing and analysis for remote health and activities monitoring

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    Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

    Mobile cloud healthcare systems using the concept of point–of–care

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    Recent years have witnessed a rapid growth in delivering/accessing healthcare services on mobile devices. An example of a health practice/application that is benefiting from the mobile evolution is m–health, which is aimed at providing health services to mobile devices on the move. However, mobile devices have restricted computational and storage capacity, and run on batteries that have limited power. These limitations render m–health unable to run the demanding tasks that may be required for accessing/providing health services. The mobile cloud has recently been proposed as a solution for dealing with some of the limitations of mobile devices, such as low storage and computing capacity. However, introducing this solution into the m–health field is not straightforward, as the integration of this technology has specific limitations, such as disconnection issues and concerns over privacy and security. This thesis presents research work investigating the ability to introduce mobile cloud computing technology into the health field (e.g., m–health) to increase the chances of survival in cases of emergencies. This work focuses on providing help to people in emergencies by allowing them to seek/access help via mobile devices reliably and confidently, as well as the ability to build a communication platform between people who require help and professionals who are trusted and qualified to provide it. The concept of point–of–care has been used here, which means providing as much medical support to the public as possible where and when it is needed. This thesis proposes a mobile cloud middleware solution that enhances connectivity aspects by allowing users to create/join a mobile ad–hoc network (MANET) to seek help in the case of emergencies. On the other side, the cloud can reach users who do not have a direct link to the cloud or an Internet connection. The most important advantage of combining a MANET and a mobile cloud is that management tasks such as IP allocation and split/merge operations are shifted to the cloud, which means resources are saved on the mobile side. In addition, two mobile cloud services were designed which have the aim of interacting with users to facilitate help to be provided swiftly in the case of emergencies. The system was deployed and tested on Amazon EC2 cloud and Android–based mobile devices. Experimental results and the reference architecture show that the proposed middleware is feasible and meets pre–defined requirements, such as enhancing the robustness and reliability of the system

    A case study in open source innovation: developing the Tidepool Platform for interoperability in type 1 diabetes management.

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    OBJECTIVE:Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management. MATERIALS AND METHODS:An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software. RESULTS:Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application ("app"), Blip, to visualize the data. Tidepool's software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security. DISCUSSION:By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use. CONCLUSION:The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool's open source, cloud model for health data interoperability is applicable to other healthcare use cases
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