Design of Home-Based Elderly Health Care System

2020 ◽  
Vol 7 (2) ◽  
pp. 21-25
Author(s):  
Zhaoyu Li ◽  
Shuang Liu ◽  
Linyan Xue
2017 ◽  
Vol 25 (4) ◽  
pp. 340-360 ◽  
Author(s):  
Qingwen Xu ◽  
Jamie P. Halsall

The global financial crisis of 2008 has caused much dialogue within the social policy framework on how to maintain a sustainable elderly health-care system. This coupled with a migrant crisis have created extra social and economic pressures in Europe in particularly. As it has been well documented by social scientists, people are living longer than ever before. There are two fundamental factors that are helping people live to an old age, which are as follows: (a) a better quality of life and (b) improved health-care system at state level. However, since the global financial crisis of 2008 populations across the world are living in an age of austerity. The age of austerity has brought extra financial pressures on the state, polarizing society by implementing cuts in welfare. The reason many governments across the world (e.g., United States, United Kingdom, and Greece) have enforced a series of austerity measures is fundamentally to reduce debt. The aim of this article is to critically explore the austerity social policy agenda within the context of the debates surrounding the refugee or migrant crisis in the elderly health-care system.


Author(s):  
Shuai Shao ◽  
◽  
Naoyuki Kubota

In recent years, population aging has become an important social issue. We hope to achieve an elderly health care system through technical means. In this study, we developed an elderly health care system. We chose to use environmental sensors to estimate the behavior of older adults. We found that traditional methods have difficulty solving the problem of excessive indoor environmental differences in different households. Therefore, we provide a fuzzy spike neural network. By modifying the sensitivity of input using a fuzzy inference system, we can solve the problem without additional training. In the experiment, we used temperature and humidity data to make an estimation of behavior in the bathroom. The results show that the system can estimate behavior with 97% accuracy and 78% sensitivity.


2016 ◽  
Vol 24 (3) ◽  
Author(s):  
Séfora Luana Evangelista Andrade ◽  
Débora César de Souza Rodrigues ◽  
Anne Jaquelyne Roque Barreto ◽  
Annelissa Andrade Virgínio de Oliveira ◽  
Ana Rita Bizerra do Nascimento Santos ◽  
...  

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