Publication Date
2025
College
College of Professional Studies
Document Type
Poster
Description
Falls in older adults are a leading cause of injury, hospitalization, and reduced independence, with significant healthcare costs and impacts on quality of life. Traditional fall risk assessments (e.g., clinical tests) are time-consuming, subjective, and may lack predictive accuracy. Advances in wearable sensors, AI, and machine learning offer real-time, objective, and scalable solutions for fall risk prediction and prevention. The purpose of this review is to synthesize current evidence on Artificial Intelligence-driven fall risk assessment tools and highlight gaps for future research.
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