Fall Detection for Smart Living using YOLOv5
This addresses safety and emergency response for residents in smart living environments, but it is incremental as it applies an existing method to a specific domain.
The paper tackled fall detection in smart homes using YOLOv5mu, achieving a mean average precision of 0.995 for identifying fall events.
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.