199 research outputs found
Strategic Intelligence Monitor on Personal Health Systems (SIMPHS): Report on Typology/Segmentation of the PHS Market
This market segmentation reports for Personal Health Systems (PHS) describes the methodological background and illustrates the principles of classification and typology regarding different fragments forming this market. It discusses different aspects of the market for PHS and highlights challenges towards a stringent and clear-cut typology or defining market segmentation. Based on these findings a preliminary hybrid typology and indications and insights are created in order to be used in the continuation of the SIMPHS project. It concludes with an annex containing examples and cases studies.JRC.DDG.J.4-Information Societ
Towards fog-driven IoT eHealth:Promises and challenges of IoT in medicine and healthcare
Internet of Things (IoT) offers a seamless platform to connect people and objects to one another for enriching and making our lives easier. This vision carries us from compute-based centralized schemes to a more distributed environment offering a vast amount of applications such as smart wearables, smart home, smart mobility, and smart cities. In this paper we discuss applicability of IoT in healthcare and medicine by presenting a holistic architecture of IoT eHealth ecosystem. Healthcare is becoming increasingly difficult to manage due to insufficient and less effective healthcare services to meet the increasing demands of rising aging population with chronic diseases. We propose that this requires a transition from the clinic-centric treatment to patient-centric healthcare where each agent such as hospital, patient, and services are seamlessly connected to each other. This patient-centric IoT eHealth ecosystem needs a multi-layer architecture: (1) device, (2) fog computing and (3) cloud to empower handling of complex data in terms of its variety, speed, and latency. This fog-driven IoT architecture is followed by various case examples of services and applications that are implemented on those layers. Those examples range from mobile health, assisted living, e-medicine, implants, early warning systems, to population monitoring in smart cities. We then finally address the challenges of IoT eHealth such as data management, scalability, regulations, interoperability, device–network–human interfaces, security, and privacy
Smartphone as a Personal, Pervasive Health Informatics Services Platform: Literature Review
Objectives: The article provides an overview of current trends in personal
sensor, signal and imaging informatics, that are based on emerging mobile
computing and communications technologies enclosed in a smartphone and enabling
the provision of personal, pervasive health informatics services.
Methods: The article reviews examples of these trends from the PubMed and
Google scholar literature search engines, which, by no means claim to be
complete, as the field is evolving and some recent advances may not be
documented yet.
Results: There exist critical technological advances in the surveyed
smartphone technologies, employed in provision and improvement of diagnosis,
acute and chronic treatment and rehabilitation health services, as well as in
education and training of healthcare practitioners. However, the most emerging
trend relates to a routine application of these technologies in a
prevention/wellness sector, helping its users in self-care to stay healthy.
Conclusions: Smartphone-based personal health informatics services exist, but
still have a long way to go to become an everyday, personalized
healthcare-provisioning tool in the medical field and in a clinical practice.
Key main challenge for their widespread adoption involve lack of user
acceptance striving from variable credibility and reliability of applications
and solutions as they a) lack evidence-based approach; b) have low levels of
medical professional involvement in their design and content; c) are provided
in an unreliable way, influencing negatively its usability; and, in some cases,
d) being industry-driven, hence exposing bias in information provided, for
example towards particular types of treatment or intervention procedures
Distributed Computing and Monitoring Technologies for Older Patients
This book summarizes various approaches for the automatic detection of health threats to older patients at home living alone. The text begins by briefly describing those who would most benefit from healthcare supervision. The book then summarizes possible scenarios for monitoring an older patient at home, deriving the common functional requirements for monitoring technology. Next, the work identifies the state of the art of technological monitoring approaches that are practically applicable to geriatric patients. A survey is presented on a range of such interdisciplinary fields as smart homes, telemonitoring, ambient intelligence, ambient assisted living, gerontechnology, and aging-in-place technology. The book discusses relevant experimental studies, highlighting the application of sensor fusion, signal processing and machine learning techniques. Finally, the text discusses future challenges, offering a number of suggestions for further research directions
Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities
Research and development work relating to assistive technology
2010-11 (Department of Health)
Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197
Strategic Intelligence Monitor on Personal Health Systems (SIMPHS): Market Structure and Innovation Dynamics
Personal Health Systems (PHS) and Remote Patient Monitoring and Treatment (RMT) have the potential to alter the way healthcare is provided by increasing the quantity and quality of care. This report explores the current status of PHS and, more specifically of the RMT market in Europe. It addresses the question of how these technologies can contribute facing some of the challenges standing in front of the European healthcare delivery systems causes by higher demand pressures through chronic diseases and demographic change combined with diminishing resources for health care. An uptake and diffusion of these services would potentially lead to benefits through a reduction in death rates, and avoid recurring hospitalisation in a cost-effective manner. Yet the report identifies different categories of barriers hampering a full deployment of RMT in Europe. In the concluding part the reports provides a number of tentative policy options specifically aimed at fostering EU-wide deployment of RMT/PHS.JRC.DDG.J.4-Information Societ
An ambient assisted living solution for mobile environments
An Ambient Assisted Living (AAL) mobile health application solution with biofeedback based on body sensors is very useful to perform a data collection for diagnosis in patients whose clinical conditions are not favourable. This system allows comfort, mobility, and efficiency in all the process of data collection providing more confidence and operability. A physical fall may be considered something natural in the life span of a human being from birth to death. In a perfect scenario it would be possible to predict when a fall will occur in order to avoid it. Falls represent a high risk for senior people health. Those falls can cause fractures or injuries causing great dependence and debilitation to the elderly and even death in extreme cases. Falls can be detected by the accelerometer included in most of the available mobile phones or portable digital assistants (PDAs). To reverse this tendency, it can be obtained more accurate data for patients monitoring from the body sensors attached to the human body (such as, electrocardiogram (ECG), electromyography (EMG), blood volume pulse (BVP), electro dermal activity (EDA), and galvanic skin response (GSR)). Then, this dissertation reviews the related literature on this topic and introduces a mobile solution for falls prevention, detection, and biofeedback monitoring. The proposed system collects sensed data that is sent to a smartphone or tablet through Bluetooth. Mobile devices are used to process and display information graphically to users. The falls prevention system uses collected data from sensors in order to control and advice the patient or even to give instructions to treat an abnormal condition to reduce the falls risk. In cases of symptoms that last more time it can even detect a possible disease. The signal processing algorithms plays a key role in the fall prevention system. These algorithms in real time, through the capture of biofeedback data, are needed to extract relevant information from the signals detected to warn the patient. Monitoring and processing data from sensors is realized by a smartphone or tablet that will send warnings to users. All the process is performed in real time. These mobile devices are also used as a gateway to send the collected data to a Web service, which subsequently allows data storage and consultation. The proposed system is evaluated, demonstrated, and validated through a prototype and it is ready for use
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