Fall prediction based on key points of human bones

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. (C) 2019 Elsevier B.V. All rights reserved.