基于人体行为模型的跌倒行为检测方法

随着移动互联网的广泛应用, 智慧社区等一系列移动互联应用等得到人们的重视, 特别是以居家养老的老年人防跌倒检测备受关注. 针对目前老年人跌倒没有及时得到检测报警, 从而无法及时救助, 进而产生更严重的安全性的问题, 本文提出了一种跌倒检测方法. 本文提出的方法首先对特定人体进行扫描, 利用人体建模工具poser构建出人体模型, 在运动过程中根据关节点位置将二维坐标映射出相应的三维坐标并通过节点位置预测算法对映射后的三维坐标进行关节点位置预测, 然后将预测后的子关节聚合到父类三维空间坐标轴中并预测出父类关节点的运动状态, 当子关节点与父关节点预测结果同时处于跌倒状态, 则判断人体所处于跌倒状态. 由于所建立的运动模型在运动特征上具有较高的真实性, 以此获取关节点的数据变化真实可靠. 经过大量的实验数据表明, 本文提出的跌倒检测方法可以精准实时反应运动状态, 检测准确率为99%, 由此可见本文提出的方法应用于跌倒检测是有效并可靠的.

Fall Behavior Detection Method Based on Human Behavior Model

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.