Activities suggestion based on emotions in AAL environments

A survey on agents applications in healthcare: Opportunities, challenges and trends

BACKGROUND AND OBJECTIVE

The agent abstraction is a powerful one, developed decades ago to represent crucial aspects of artificial intelligence research. The meaning has transformed over the years and now there are different nuances across research communities. At its core, an agent is an autonomous computational entity capable of sensing, acting, and capturing interactions with other agents and its environment. This review examines how agent-based techniques have been implemented and evaluated in a specific and very important domain, i.e. healthcare research.

METHODS

We survey key areas of agent-based research in healthcare, e.g. individual and collective behaviours, communicable and non-communicable diseases, and social epidemiology. We propose a systematic search and critical review of relevant recent works, introduced by an exploratory network analysis.

RESULTS

Network analysis enables to devise out 5 main research clusters, the most active authors, and 4 main research topics.

CONCLUSIONS

Our findings support discussion of some future directions for increasing the value of agent-based approaches in healthcare.

The willingness and influencing factors to choose smart senior care among old adults in China

Background

Population aging has become an escalating issue in China resulting in increasing healthcare demand. Smart senior care has the potential to help older adults live independently and relieve the pressure of healthcare including home-based care. This study aimed to explore Chinese older adults’ preferred access models and service content of smart senior care and factors affecting their willingness to choose smart senior care.

Methods

This was a cross-sectional study. A total of 760 community-dwelling older adults from Xuzhou, China were included in this study. Their demographics, family support, health status, smart senior care use, and willingness to choose smart senior care were collected. The Chi-square test was used for single factor analysis of each variable. The statistically significant variables were included in the logistics regression model to analyze factors influencing older adults’ willingness to choose smart senior care. The chi-square goodness of fit test was used to analyze the preferred content and access models of smart senior care; the Bonferroni method was used to correct the results.

Results

The finding indicated that participants’ age, number of children, frequency of children visiting parents, adequate senior care, self-reported health, chronic diseases, smartphone use, and attitude toward smart senior care were significantly associated with their willingness to choose the smart senior care (p < 0.05). For smart senior care access models, participants preferred the remote monitoring model, telephone call model, and the community site model over the health smart home model and the smart application platform model. There was no statistical difference among these three preferred access models (p’ > 0.005). Regarding service content, participants desired medical care service the most (p’ < 0.005).

Conclusions

Chinese older adults’ willingness to choose smart senior care is affected by personal, family, health, and other factors. To develop China’s senior care, we should consider their demand and preference for smart senior care. It is important to enrich the content of smart senior care, especially on medical care services, and maintain the dynamic balance between supply and demand using a diverse supply approach.

Discovering Hidden Mental States in Open Multi-Agent Systems by Leveraging Multi-Protocol Regularities with Machine Learning

The agent paradigm and multi-agent systems are a perfect match for the design of smart cities because of some of their essential features such as decentralization, openness, and heterogeneity. However, these major advantages also come at a great cost. Since agents’ mental states are hidden when the implementation is not known and available, intelligent services of smart cities cannot leverage information from them. We contribute with a proposal for the analysis and prediction of hidden agents’ mental states in a multi-agent system using machine learning methods that learn from past agents’ interactions. The approach employs agent communication languages, which is a core property of these multi-agent systems, to infer theories and models about agents’ mental states that are not accessible in an open system. These mental state models can be used on their own or combined to build protocol models, allowing agents (and their developers) to predict future agents’ behavior for various tasks such as testing and debugging them or making communications more efficient, which is essential in an ambient intelligence environment. This paper’s main contribution is to explore the problem of building these agents’ mental state models not from one, but from several interaction protocols, even when the protocols could have different purposes and provide distinct ambient intelligence services.

A Low-Cost Cognitive Assistant

In this paper, we present in depth the hardware components of a low-cost cognitive assistant. The aim is to detect the performance and the emotional state that elderly people present when performing exercises. Physical and cognitive exercises are a proven way of keeping elderly people active, healthy, and happy. Our goal is to bring to people that are at their homes (or in unsupervised places) an assistant that motivates them to perform exercises and, concurrently, monitor them, observing their physical and emotional responses. We focus on the hardware parts and the deep learning models so that they can be reproduced by others. The platform is being tested at an elderly people care facility, and validation is in process.

Alzheimer’s Garden: Understanding Social Behaviors of Patients with Dementia to Improve Their Quality of Life

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.