Fall Detection for Smart Living using YOLOv5

TL;DR

This paper presents a highly accurate real-time fall detection system using YOLOv5mu, optimized for smart home safety with advanced data augmentation and robust performance across conditions.

Contribution

Introduces a novel fall detection system utilizing YOLOv5mu with exceptional accuracy and robustness for smart living environments.

Findings

Achieved a mean average precision (mAP) of 0.995.

Demonstrated robustness across various conditions.

Provides real-time fall detection capabilities.

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.