Human Fall Down Recognition Using Coordinates Key Points Skeleton

Authors

  • Mohammed Abduljabbar Ali University of Technology/Baghdad
  • Abir Jaafar Hussain Liverpool John Moores University
  • Ahmed T. Sadiq University of Technology/Baghdad

DOI:

https://doi.org/10.3991/ijoe.v18i02.28017

Keywords:

Fall Down, Human Skeleton, Skeleton Tracking

Abstract


Falls pose a substantial threat to human safety and can quickly result in disastrous repercussions. This threat is particularly true for the elderly, where falls are the leading cause of hospitalization and injury-related death. A fall that is detected and responded to quickly has a lower danger and long-term impact. Many real-time fall detection solutions are available; however, these solutions have specific privacy, maintenance, and proper use issues. Vision-based fall event detection has the benefit of being completely private and straightforward to use and maintain. However, in real-world scenarios, falls are diverse and result in high detection instability. This study proposes a novel vision-based technique for fall detection and analyzes an extracted skeleton to define human postures. OpenPose can be used to get skeletal information about the human body. It identifies a fall using three critical parameters: the center of the value of the head and shoulder coordinates, the critical points of the shoulder coordinates, and the distance between the center of the skeleton's head and the floor with the angle between the center of the shoulders and the ground. Our proposed methodology was effective, with a classification accuracy of 97.7%.

Author Biographies

Mohammed Abduljabbar Ali, University of Technology/Baghdad

Computer Science

Abir Jaafar Hussain, Liverpool John Moores University

School of Computer Sciences and Mathematics

Ahmed T. Sadiq, University of Technology/Baghdad

Computer Science

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Published

2022-02-16

How to Cite

Abduljabbar Ali, M., Jaafar Hussain, A., & T. Sadiq, A. (2022). Human Fall Down Recognition Using Coordinates Key Points Skeleton. International Journal of Online and Biomedical Engineering (iJOE), 18(02), pp. 88–104. https://doi.org/10.3991/ijoe.v18i02.28017

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Section

Papers