Paper Contents
Abstract
AbstractFalls are among the leading causes of injury and mortality among older adults in assisted living facilities. Traditional fall prevention measures often rely on manual supervision and reactive responses, which have limitations in timeliness and effectiveness. This research develops an AI-powered monitoring system integrating wearable sensors, ambient sensors, and machine learning algorithms to detect, predict, and prevent falls. The methodology includes system design, data collection, algorithm training, and real-world testing. Results show significant reductions in fall rates and enhanced caregiver confidence, suggesting that such systems can revolutionize fall prevention strategies in elderly care.
Copyright
Copyright © 2024 Dr Jeffrey Rebbish. This is an open access article distributed under the Creative Commons Attribution License.
