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The demographic and social changes are causing a gradual increase of the population in situation of dependency. The main concern of the elderly people is their health and its consequences in terms of dependence and also is the primary cause of suffering and self-rated ill health. Since the elderly have different health problems that the rest of the...
Citations
Wireless body area networks (WBANs), mobile devices and cloud computing are the backbone technologies of pervasive healthcare systems. WBANs ecosystem possesses certain limitations such as wireless communication, security, data validation, data consistency and many more, that are needed to be addressed for an efficient WBAN system. Consequently, Cloud Computing is used to overcome WBAN’s limitations. The ubiquitous and scalable nature of the Cloud makes it the most suitable architecture to integrate with WBAN for delivering an efficient pervasive healthcare ecosystem. Although, researchers are focusing on integrating cloud and WBAN system, there is a void of systematic analysis in terms of technology's state-of- the-art, and research directions for improving Quality of Services (QoS). As an endeavour to fill this void, the authors propose this review on Cloud-assisted WBAN ecosystem based on the classical systematic review approach with few modifications. We investigate the role, need and use of Cloud in empowering WBAN. Also, we address various aspects of this integrated ecosystem such as definitions, technologies, Quality of Service (QoS) parameters, and existing solutions. Furthermore, this study helps the readers identify and select the research potentials in their respective areas. This paper presents the first study on Cloudlet-enabled WBAN system using a systematic review approach.
Owing to the growing increase in the world’s ageing population, research has focused on developing information and communication technology (ICT)–based services for personalized care, improved health, and quality social life for the elderly. Recent efforts explore Big Data in order to build mathematical models of personal behavior and lifestyle for analytics. Leveraging Big Data analytics holds enormous potential for solving some of the biggest and most intractable challenges in personalized elderly care through quantified modeling of a person’s lifestyle in a way that takes cognizance of their beliefs, values, and preferences, and connects to a history of events, things, and places around which they have progressively built their lives. However, the idea of discovering patterns to personalize care and inform critical health care decisions for the elderly is challenged as data grow exponentially in volume, become faster and increasingly unstructured, and are generated from sociodigital engagements that often may not accurately reflect the real-world entities and contexts they represent. As a result, the idea raises issues along several dimensions, including social, technical, and context-aware challenges. In this chapter, we present an overview of the state of the art in personalized elderly care, and explore the opportunities and inherent sociotechnical challenges in leveraging Big Data analytics to support elderly care and independent living. Based on this discussion, and arguing that analytics need to take account of the contexts that shape the generation and use of data, ACTVAGE, a context-aware lifestyle-oriented framework for personalized elderly care and independent living is proposed.
Recent data of the European Union reveals that the main chronic pathologies are the Cardiovascular Disease (CVD), the main cause of death in Europe, and respiratory diseases, specially the Chronic Obstructive Pulmonary Disease (COPD). Each year CVD causes over 4 million deaths in Europe alone and over 1.9 million deaths in the European Union (EU). According to the WHO (World Health Organization), in 2030 COPD will be the third leading cause of death, and the first cause of sanitary costs in Europe, due to the profiles of the expenses in health sector and the long time expenses by age groups and their important associate morbidity. New medical applications based on remote monitoring can help treat those chronic diseases but significantly will increase the volume of health information to manage, including data from medical and biological sensors, being then necessary to process this huge volume of data using techniques from Big Data. In this paper we propose one potential solution for creating those new services, based on Big Data processing and IoT concepts.
In a society characterized by aging population and economical crisis it is desirable to reduce the costs of public healthcare systems. It is increasingly necessary to streamline the health system resources leading to the development of new medical services such as telemedicine, monitoring of chronic patients, personalized health services, creating new services for dependants, etc. Those new application and services will significantly increasing the volume of health information to manage, including data from medical and biological sensors, contextual information, health records, reference information, etc., which in turn requires the availability of health applications anywhere, at any time and also access to medical information must be pervasive and mobile. In this paper we propose one potential solution for creating those new services based on cloud computing and vital signs sensors.
