Electronic Solutions for Artificial Intelligence Healthcare

A Sustainable, Interactive Elderly Healthcare System for Nursing Homes: An Interdisciplinary Design

Population ageing becomes a perplexing conundrum with social and economic development. Many senior citizens are now empty nesters because the younger generation prefer to stay in metropolises for a better life. Therefore, living in a nursing home is a popular choice for the aged. This objective-oriented paper proposes a sustainable elderly healthcare system for nursing homes. The main work is the design and implementation of a new rapid and interactive assistance service. Based on cost-effective fingerprint indoor-positioning technology, the alert message that a person is at risk will be immediately sent to nearby people before professionals arrive. Warning messages are available when nearing marked areas (e.g., slippery floors). The parallel path-finding algorithm plays a significant role in finding nearby people and alerting people who approach specific areas. Furthermore, this system provides application programming interfaces to connect to health devices, such as smart bracelets, watches, and glasses. In general, the system is designed to ensure the safety of the elderly and improve management efficiency, which corresponds to present smart elderly care proposals from governments.

Design of a Diagnostic System for Patient Recovery Based on Deep Learning Image Processing: For the Prevention of Bedsores and Leg Rehabilitation

Worldwide COVID-19 infections have caused various problems throughout different countries. In the case of Korea, problems related to the demand for medical care concerning wards and doctors are serious, which were already slowly worsening problems in Korea before the COVID-19 pandemic. In this paper, we propose the direction of developing a system by combining artificial intelligence technology with limited areas that do not require high expertise in the rehabilitation medical field that should be improved in Korea through the prevention of bedsores and leg rehabilitation methods. Regarding the introduction of artificial intelligence technology, medical and related laws and regulations were quite limited, so the actual needs of domestic rehabilitation doctors and advice on the hospital environment were obtained. Satisfaction with the test content was high, the degree of provision of important medical data was 95%, and the angular error was within 5 degrees and suitable for recovery confirmation.

A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields

This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.