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Human fall detection has been studied and applied in health care and human monitoring systems. Falls often occur in the elderly, and people with neurological and musculoskeletal diseases. It can also appear in heavy work environments, slippery surfaces, or human carelessness. Falls cause many serious health injuries, especially to the spine and brain. Developing tools to warn of falls early is essential and reduces many unnecessary risks. This paper proposes vision-based human fall detection and warning systems using person detection pre-trained models. Thereby, evaluating and comparing the performance between three nano versions of the YOLO (You Only Look Once) architecture, including YOLOv5n, YOLOv8n, and YOLOv11n. The YOLOv8n model achieved the best speed at 109.06 fps (FPS) on an NVIDIA GeForce GTX 1080Ti 11GB GPU as a real-time test result. The demo video can be accessed at this link: https://bit.ly/4flU7Xv .

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Vision-Based Human Fall Detection with Pre-trained YOLO Models: Comparison and Application

  • 会议论文

  • 发表日期：

    2026年

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  • Duy-Linh Nguyen,
  • Xuan-Thuy Vo,
  • Adri Priadana,
  • Jehwan Choi,
  • Kang-Hyun Jo

摘要

Human fall detection has been studied and applied in health care and human monitoring systems. Falls often occur in the elderly, and people with neurological and musculoskeletal diseases. It can also appear in heavy work environments, slippery surfaces, or human carelessness. Falls cause many serious health injuries, especially to the spine and brain. Developing tools to warn of falls early is essential and reduces many unnecessary risks. This paper proposes vision-based human fall detection and warning systems using person detection pre-trained models. Thereby, evaluating and comparing the performance between three nano versions of the YOLO (You Only Look Once) architecture, including YOLOv5n, YOLOv8n, and YOLOv11n. The YOLOv8n model achieved the best speed at 109.06 fps (FPS) on an NVIDIA GeForce GTX 1080Ti 11GB GPU as a real-time test result. The demo video can be accessed at this link: https://bit.ly/4flU7Xv .