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Fall prediction based on key points of human bones

Author

Listed:
  • Xu, Qingzhen
  • Huang, Guangyi
  • Yu, Mengjing
  • Guo, Yanliang

Abstract

With the development of society, the number of old people is increasing. Slow response, osteoporosis and vision loss threaten the health of the elderly. The fall of this problem is an important factor that threatens the health of the elderly. In order to reduce the damage caused by falls, this paper based on the human skeleton map for fall prediction. First using OPENPOSE get the bone map and make it into a data set. Then using transfer learning to train the data set to get a new model Finally, the new model is used to predict the fall. The innovations in this paper are to take bone maps from 2D images and use bone maps to make fall predictions. The bone map is predicted using a convolutional neural network. The final experimental results show that the new model obtained through transfer learning has an accuracy rate of 91.7%. This result fully demonstrates the validity of the proposed model.

Suggested Citation

  • Xu, Qingzhen & Huang, Guangyi & Yu, Mengjing & Guo, Yanliang, 2020. "Fall prediction based on key points of human bones," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    • Handle: RePEc:eee:phsmap:v:540:y:2020:i:c:s0378437119318011
    DOI: 10.1016/j.physa.2019.123205
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    References listed on IDEAS

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    1. Li, Fushan & Gao, Qingyong, 2016. "Blow-up of solution for a nonlinear Petrovsky type equation with memory," Applied Mathematics and Computation, Elsevier, vol. 274(C), pages 383-392.
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    Cited by:

    1. Chengle Fang & Huiyu Xiang & Chongjie Leng & Jiayue Chen & Qian Yu, 2022. "Research on Real-Time Detection of Safety Harness Wearing of Workshop Personnel Based on YOLOv5 and OpenPose," Sustainability, MDPI, vol. 14(10), pages 1-18, May.

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