The fall is one of the most important research fields of solitary elder healthcare at home based on Internet of Things technology. Current studies mainly focus on the fall detection, which helps medical staffs bring a fallen elder out of danger in time. However, it neither predicts a fall nor provides an effective protection against a fall. This article studies the fall prediction based on human biomechanics equilibrium and body posture characteristics through analyzing three-dimensional skeleton joints data from the depth camera sensor Kinect. The research includes building a human bionic mass model using skeleton joints data from Kinect, determining human balance state, and proposing a fall prediction algorithm based on recurrent neural networks by unbalanced posture features. We evaluate the model and algorithm on an open database. The performance indicates that the fall prediction algorithm by studying human biomechanics can predict a fall (91.7%) and provide a certain amount of time (333ms) before the elder injuring (hitting the floor). This work provides a technical basis and a data analytics approach for the fall protection.