Frailty Insights Detection System (FIDS)—A Comprehensive and Intuitive Dashboard Using Artificial Intelligence and Web Technologies

Abstract

Frailty, known as a syndrome affecting the elderly, have a direct impact on both social well-being and body’s ability to function properly. Specific to geriatric healthcare, the early detection of frailty helps the specialists to mitigate risks of severe health outcomes. This article presents the development process of a system used to determine frailty-specific parameters, focusing on easy-to-use, non-intrusive nature and reliance on objectively measured parameters. The multitude of methodologies and metrics involved in frailty assessment emphasize the multidimensional aspects of this process and the lack of a common and widely accepted methodology as being the gold standard. After the research phase, the frailty-specific parameters considered are physical activity, energy expenditure, unintentional weight loss, and exhaustion, along with additional parameters like daily sedentary time, steps history, heart rate, and body mass index. The system architecture, artificial intelligence models, feature selection, and final prototype results are presented. The last section addresses the challenges, limitations, and future work related to the Frailty Insights Detection System (FIDS).

Keywords

• frailty detection system
• internet of things
• artificial intelligence