Year: 2024
Type: preprint
Abstract: This work introduces a fall detection system using the YOLOv5mu model, which achieved a mean average precision (mAP) of 0.995, demonstrating exceptional accuracy in identifying fall events within smart home environments. Enhanced by advanced data augmentation techniques, the model demonstrates significant robustness and adaptability across various conditions. The integration of YOLOv5mu offers precise, real-time fall detection, which is crucial for improving safety and emergency response for residents. Future research will focus on refining the system by incorporating contextual data and exploring multi-sensor approaches to enhance its performance and practical applicability in diverse environments.
Source: arXiv (Cornell University)
Author: Pereira, Gracile Astlin
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402706176