A Study on an AI-Based Fall Prevention Model for Older Adults to Address Gaps in Social Care

As society rapidly transitions into an aging population, the social care gap caused by increasing numbers of elderly living alone and shortage of care workers has emerged as a serious social problem. Falls among the elderly are a major cause of rapidly increasing mortality and disability rates, making preventive approaches essential. This study developed an AI-based fall prevention model that can supplement care gaps by analyzing elderly people's daily behaviors in real-time and predicting fall risks in advance using artificial intelligence technology. Through deep learning-based behavioral pattern analysis and sensor data fusion, fall risk levels were evaluated, and an immediate notification system was implemented when dangerous situations occur. Experimental results showed that the developed model predicted fall risks with 92.3% accuracy and reduced emergency response time by an average of 67%. This is evaluated as an innovative solution that can support safe independent living for the elderly and significantly improve the efficiency of social care systems.