AI-Based Fall Prevention and Monitoring Systems for Aged Adults in Residential Care Facilities

Purpose: This study explores the application of artificial intelligence (AI) in fall prevention and monitoring systems designed for aged adults in residential care facilities. It aims to assess how AI-driven technologies can enhance safety, reduce fall-related injuries, and improve the quality of life among elderly residents.  Methodology: A comprehensive literature review was conducted, drawing from academic databases such as PubMed, IEEE Xplore, and Scopus. The review focused on studies published in English that examined AI algorithms, sensor technologies, and data analytics for fall prediction and monitoring among individuals aged 65 and older. Studies involving diagnostic or rehabilitative AI applications were excluded. Key metrics such as sensor types, algorithm performance, and system accuracy were analyzed.  Findings: The review reveals a growing adoption of AI-based systems employing machine learning algorithms, including decision trees, support vector machines, and neural networks, for fall risk prediction and detection. Wearable sensors, computer vision, and data analytics have shown promise in reducing false alarms and enhancing fall detection reliability. Despite the demonstrated potential for improving safety and reducing healthcare costs, challenges persist in ensuring data privacy, user acceptance, and system robustness.  Unique Contribution: This research uniquely consolidates current knowledge on AI applications for fall prevention in elderly care, highlighting practical implementations, limitations, and future directions. It underscores the transformative role of AI in proactive elderly care, offering a foundation for developing more adaptive, personalized, and ethical AI-based interventions in residential care environments.