Artificial Intelligence - Powered Assistive Technologies for Fall Detection in Older Adults
Eugenia Tîrziu, Ciprian Dobre · 2025
This study explores the role of artificial intelligence in assistive technologies for monitoring older adults, focusing on fall detection using the YOLO (You Only Look Once) object detection algorithm with pose estimation. The proposed system uses YOLOv11’s enhanced real-time processing capabilities to accurately detect falls from video feeds, ensuring rapid intervention and improved safety. The system analyzes skeletal movements to distinguish falls from normal activities with greater precision. Real-time testing was conducted using live webcam recordings under dynamic conditions. The results demonstrate significant improvements in detection accuracy, processing speed and system robustness. Furthermore, the system prioritizes data privacy by processing only skeletal key points, eliminating the need for storing personal visual data. These findings highlight the potential of AI-driven monitoring solutions in enhancing the quality of life for older adults, offering a reliable and privacy-conscious assistive technology for fall prevention and safety.