Key point trajectory prediction method of human stochastic posture falls

Abstract

ABSTRACT The human body will show very complex and diversified posture changes in the process of falling, including body posture, limb position, angle and movement trajectory, etc. The coordinates of the key points of the model are mapped to the three-dimensional space to form a three-dimensional model and obtain the three-dimensional coordinates of the key points; The construction decomposition method is used to calculate the rotation matrix of each key point, and the rotation matrix is solved to obtain the angular displacement data of the key points on different degrees of freedom. The method of curve fitting combined with the weight distribution kernel function based on self-organizing mapping theory is used to obtain the motion trajectory prediction equation of the human body falling in different degrees of freedom at random positions in three-dimensional space, determine the key point trajectory of human random fall behaviour. The experimental results show that the mapped 3D model is consistent with the real human body structure. This method can accurately determine whether the human body falls or squats randomly, and the prediction results of the key points of the human fall are consistent with the actions of the human body after the fall.