Affinity Propagation Clustering for Older Adults Daily Routine Estimation

Author(s):  
Ana Jimenez Martin ◽  
Ismael Miranda Gordo ◽  
Juan Jesus Garcia Dominguez ◽  
Joaquin Torres-Sospedra ◽  
Sergio Lluva Plaza ◽  
...  

Long-term care for older adults is highly affect by the COVID-19 outbreak. The objective of this rapid review is to understand what we can learn from previous crises or disasters worldwide to optimize the care for older adults in long term care facilities during the outbreak of COVID-19. We searched five electronic databases to identify potentially relevant articles. In total, 23 articles were included in this study. Based on the articles, it appeared that nursing homes benefit from preparing for the situation as best as they can. For instance, by having proper protocols and clear division of tasks and collaboration within the organization. In addition, it is helpful for nursing homes to collaborate closely with other healthcare organizations, general practitioners, informal caregivers and local authorities. It is recommended that nursing homes pay attention to capacity and employability of staff and that they support or relieve staff where possible. With regard to care for the older adults, it is important that staff tries to find a new daily routine in the care for residents as soon as possible. Some practical tips were found on how to communicate with people who have dementia. Furthermore, behavior of people with dementia may change during a crisis. We found tips for staff how to respond and act upon behavior change. After the COVID-19 outbreak, aftercare for staff, residents, and informal caregivers is essential to timely detect psychosocial problems. The consideration between, on the one hand, acute safety and risk reduction (e.g. by closing residential care facilities and isolating residents), and on the other hand, the psychosocial consequences for residents and staff, were discussed in case of other disasters. Furthermore, the search of how to provide good (palliative) care and to maintain quality of life for older adults who suffer from COVID-19 is also of concern to nursing home organizations. In the included articles, the perspective of older adults, informal caregivers and staff is often lacking. Especially the experiences of older adults, informal caregivers, and nursing home staff with the care for older adults in the current situation, are important in formulating lessons about how to act before, during and after the coronacrisis. This may further enhance person-centered care, even in times of crisis. Therefore, we recommend to study these experiences in future research.


2008 ◽  
Vol 9 (10) ◽  
pp. 1373-1381 ◽  
Author(s):  
Ding-yin Xia ◽  
Fei Wu ◽  
Xu-qing Zhang ◽  
Yue-ting Zhuang

2021 ◽  
Author(s):  
Dongming Lin ◽  
Hongjun Wang

Abstract Considering the reconstruction of electromagnetic maps without the prior information of electromagnetic propagation environment in the target area, a new algorithm based on affinity propagation clustering is proposed to complete the electromagnetic map reconstruction of the target area from points to surfaces and then from points and surfaces to a larger surface. Firstly, according to the actual situation, the target area is reasonably divided into grids. Electromagnetic data is sampled by distributed sensing nodes, and a certain number of sample points are selected for affinity propagation clustering to determine the locations of centers of sample points. Secondly, for the incomplete sample data, the Kriging algorithm is used to reconstruct the small circular electromagnetic maps. The class center is the center of the circle and the radius is certain. After that, the obtained small area electromagnetic map data and the data obtained from the sample points are used for domain mapping processing, and the electromagnetic data of a larger area of the target area is obtained. Finally, the overall electromagnetic map is reconstructed through data fusion. The simulation results show that the proposed algorithm is better than several interpolation algorithms. When sample points account for 0.1 of total data points, the RMSE of the result is less than 1.5.


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