Presentation Year

2025

College or Department

Short Description of your Research or Creative Project (700 characters or less)

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.

Permission to Publish Work

Yes

Primary Contact: First Name

Ian

Primary Contact: Last Name

Church

Primary Contact: Email

ichurch97@icloud.com

Primary Contact: I am a

Undergraduate Student

Primary Contact: Phone Number

4439276395

Node ID

1710

Page Classification