This paper aims at understanding the social behavior of people with dementia through the use of technology, specifically by analyzing localization data of patients of an Alzheimer’s assisted care home in Italy. The analysis will allow to promote social relations by enhancing the facility’s spaces and activities, with the ultimate objective of improving residents’ quality of life. To assess social wellness and evaluate the effectiveness of the village areas and activities, this work introduces measures of sociability for both residents and places. Our data analysis is based on classical statistical methods and innovative machine learning techniques. First, we analyze the correlation between relational indicators and factors such as the outdoor temperature and the patients’ movements inside the facility. Then, we use statistical and accessibility analyses to determine the spaces residents appreciate the most and those in need of enhancements. We observe that patients’ sociability is strongly related to the considered factors. From our analysis, outdoor areas result less frequented and need spatial redesign to promote accessibility and attendance among patients. The data awareness obtained from our analysis will also be of great help to caregivers, doctors, and psychologists to enhance assisted care home social activities, adjust patient-specific treatments, and deepen the comprehension of the disease.

Alzheimer’s garden: Understanding social behaviors of patients with dementia to improve their quality of life

Bellini G.;De Angeli N.;Gargano J. P.;Gianella M.;Goi G.;Rossi G.;Masciadri A.;Comai S.
2020-01-01

Abstract

This paper aims at understanding the social behavior of people with dementia through the use of technology, specifically by analyzing localization data of patients of an Alzheimer’s assisted care home in Italy. The analysis will allow to promote social relations by enhancing the facility’s spaces and activities, with the ultimate objective of improving residents’ quality of life. To assess social wellness and evaluate the effectiveness of the village areas and activities, this work introduces measures of sociability for both residents and places. Our data analysis is based on classical statistical methods and innovative machine learning techniques. First, we analyze the correlation between relational indicators and factors such as the outdoor temperature and the patients’ movements inside the facility. Then, we use statistical and accessibility analyses to determine the spaces residents appreciate the most and those in need of enhancements. We observe that patients’ sociability is strongly related to the considered factors. From our analysis, outdoor areas result less frequented and need spatial redesign to promote accessibility and attendance among patients. The data awareness obtained from our analysis will also be of great help to caregivers, doctors, and psychologists to enhance assisted care home social activities, adjust patient-specific treatments, and deepen the comprehension of the disease.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-3-030-58804-5
978-3-030-58805-2
Ambient Assisted Living
Data-driven design
Social behavior
Social wellness assessment
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1167060
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