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Ali, MA, Hussain, AJ and Sadiq, AT (2022) Human Fall Down Recognition Using Coordinates Key Points Skeleton. International journal of online and biomedical engineering, 18 (2). pp. 88-104.
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Human Fall Down Recognition Using Coordinates Key Points Skeleton.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
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 coordi-nates· 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%.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software Q Science > QM Human anatomy Q Science > QP Physiology |
| Divisions: | Computer Science & Mathematics |
| Publisher: | International Association of Online Engineering (IAOE) |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 23 Nov 2022 14:55 |
| Last Modified: | 23 Nov 2022 15:00 |
| DOI or ID number: | 10.3991/ijoe.v18i02.28017 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/18210 |
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