scholarly journals The Role of Big Data in Ambient Assisted Living

2016 ◽  
Vol 24 ◽  
Author(s):  
Arne Manzeschke ◽  
Galia Assadi ◽  
Willy Viehöver

Big Data and biopolitics are two major issues currently attracting attention in public health discourse, but also in sociology of knowledge, STS Studies as well as in philosophy of science and bioethics. The paper considers big data to be a new form and instrument of biopolitics (Foucault) which addresses both the categories of body and space. It is expected to fundamentally transform health care systems, domestic environments and practices of self-observation and reflection. Accordingly the paper points out some problems and pitfalls as well as open questions that have emerged in the field of AAL, which merit more attention in future public and academic debate.

2019 ◽  
Author(s):  
Philipp Brauner ◽  
Martina Ziefle

UNSTRUCTURED Many societies face a demographic change that challenges the viability of health and welfare systems. Serious Games for Healthcare as well as Ambient Assisted Living offer support and health benefits for older adults and mitigate the negative effects of the demographic shift. We developed a motion-based serious exercise game for older adults in prototypic Ambient Assisted Living environments. In two user studies outside (n=69) and within (n=64) the AAL context we studied the influence of age, gender, self-efficacy in interacting with technology, and need for achievement on performance, effect of the game, evaluation of the game, and overall acceptance. Both games were evaluated as easy to use and fun to play. Remarkably, both game interventions had a strong pain mitigating effect in older adults (-55%; -66%). Consequently, serious exercise games outside and inside Ambient Assisted Living environments can contribute to individuals' health and well-being and to the stability of health care systems.


2016 ◽  
Vol 18 (3) ◽  
pp. 339-343 ◽  

Health care systems generate a huge volume of different types of data. Due to the complexity and challenges inherent in studying medical information, it is not yet possible to create a comprehensive model capable of considering all the aspects of health care systems. There are different points of view regarding what the most efficient approaches toward utilization of this data would be. In this paper, we describe the potential role of big data approaches in improving health care systems and review the most common challenges facing the utilization of health care big data.


Author(s):  
Pierre Pestieau ◽  
Mathieu Lefebvre

This chapter reviews the public health care systems as well as their challenges. It first shows how expenditure on health care has evolved in previous decades and deals with the reasons for the growth observed in almost every European country. It emphasizes the role of technological progress as a main explanatory factor of the increase in medical expenditure but also points to the challenges facing cost-containment policies. Especially, the main common features of health care systems in Europe, such as third-party payment, single provider approach and cost-based reimbursement are discussed. Finally the chapter shows that although inequalities in health exist in the population, health care systems are redistributive. Reforms are thus needed but the trade-off between budgetary efficiency and equity is difficult.


2018 ◽  
Vol 7 (2.19) ◽  
pp. 52
Author(s):  
J Vivek ◽  
Gandla Maharnisha ◽  
Gandla Roopesh Kumar ◽  
Ch Karun Sagar ◽  
R Arunraj

In  this  paper,  context  awareness  is  a  promising  technology  that  provides  health care services and a niche  area of big data paradigm. The   drift  in  Knowledge  Discovery  from  Data  refers  to  a  set  of  activities  designed  to refine and  extract  new knowledge from complex  datasets.  The   proposed  model  facilitates  a  parallel  mining  of  frequent item sets for Ambient Assisted Living (AAL) System [a.k.a. Health  Care [System]  of  big  data that  reside   inside  a  cloud  environment.  We  extend  a  knowledge  discovery framework for  processing  and  classifying  the  abnormal  conditions of patients having fluctuations in Blood Pressure (BP) and Heart Rate(HR) and storing  this data  sets  called  Big data  into Cloud to access from  anywhere   when  needed.   This   accessed data is used to compare the new data with it, which helps to know the patients health condition.  


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