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Detecting human falls is vital in several domains, from the elderly care facilities to the dangerous workplaces. Falls in case of elderly people may result in serious injuries, where prompt assistance is necessary to reduce the risk of heavier health problems and complications. The application of fall detection systems may result in significant cost reduction, as falls may lead to hospitalization, surgery, and lengthy rehabilitation. This paper investigates the performance of YOLOv8 models for this task. These models are affordable, their training is fast and cheap, and they are recognized of their speed and efficient operation. The simulation outcomes suggest that these affordable models are very suitable for the fall detection task.

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YOLOv8 Model Architecture Selection for Human Fall Detection

  • 会议论文

  • 发表日期：

    2025年

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  • Tamara Zivkovic,
  • Miodrag Zivkovic,
  • Luka Jovanovic,
  • Jelena Kaljevic,
  • Milos Dobrojevic,
  • Nebojsa Bacanin

摘要

Detecting human falls is vital in several domains, from the elderly care facilities to the dangerous workplaces. Falls in case of elderly people may result in serious injuries, where prompt assistance is necessary to reduce the risk of heavier health problems and complications. The application of fall detection systems may result in significant cost reduction, as falls may lead to hospitalization, surgery, and lengthy rehabilitation. This paper investigates the performance of YOLOv8 models for this task. These models are affordable, their training is fast and cheap, and they are recognized of their speed and efficient operation. The simulation outcomes suggest that these affordable models are very suitable for the fall detection task.