With the wide application of the mobile Internet, a series of mobile Internet applications such as the smart community have received much more attention for citizens, especially the anti-fall detection of the elderly who are at home. In view of the fact that some elderes fall down occasionally without timely detection and alarm, which can not be aided in time, resulting in more serious safety problems, this study proposes a fall detection method. In this method, it first scans a specific human body, constructs a human body model using poser, and then maps the two-dimensional coordinates to the corresponding three-dimensional coordinates according to the position of the joint points during the motion and uses the spatial position error prediction algorithm to perform joint points on the mapped three-dimensional coordinates, then aggregates the predicted sub-joints into the three-dimensional space axis of the parent class and predicts the motion state of the parent joint point. When the child joint point and the parent joint point prediction result are simultaneously in a falling state, the proofed result is falling state. Since the established motion model has higher realism in motion characteristics, the data changes of the joint points are real and reliable. After having done experiments with a large number of experimental data, it is proved that this method can accurately and real-timely detect the reaction state when the elder falls down, and the detection accuracy is 99%. Therefore, this proposed method is effective and reliable for fall detection.