The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection

Authors

  • Haiguang Chen Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China
  • Susheng He Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China
  • Mingxing Liu Shanghai Normal University, No.100 Haisi Road Fengxian District Shanghai, Shanghai 220000, China

Keywords:

Yolov4, Openpose, Random Forest, Human joint Extraction, Fall detection

Abstract

Nowadays, the care of the elderly has become a social concern. The fall of the elderly has become one of the main factors threatening the health of the elderly. In this paper, we designed a fall detection algorithm based on human joint extraction and object detection.First,yolov4 was used to identify and detect the elderly. Then openpose was used to detect the human joint. Based on the human joint, this paper using Random Forest to classify the status of the elderly, there are three states of the elderly: falling down, lying down and other states. In the detection of a single old man, the accuracy of the model reached 99.3%, the sensitivity and specificity of the model reached 79.3% and 72.1%.

References

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Published

2020-12-11

How to Cite

Chen, H. ., He, S. ., & Liu, M. . (2020). The Elderly Fall Detection Algorithm Based on Human Joint Extraction and Object Detection. International Journal of Computer (IJC), 39(1), 107–114. Retrieved from https://www.ijcjournal.org/index.php/InternationalJournalOfComputer/article/view/1852

Issue

Vol. 39 No. 1 (2020)

Section

Articles

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