Design and Implementation of an Intelligent Elderly Fall Detection and Alert System Based on YOLOv10

Abstract

With the accelerating global trend of population aging, frequent falls among older adults have become an increasingly serious social issue. To address this challenge, this paper proposes and designs an intelligent elderly fall detection and alert system based on the YOLOv10 deep learning framework. Leveraging advanced computer vision, the system achieves real time, accurate recognition of fall events and employs multi channel alert mechanisms to promptly notify caregivers after a fall occurs. Experimental results show that the system delivers high detection accuracy along with strong real time performance and robustness, effectively reducing both false alarms and missed detections. Compared with traditional methods and several deep learning approaches, it demonstrates significant advantages, providing solid technical support for home based eldercare and smart caregiving.