基于确定性学习的人体步态识别方法研究

Research on Method of Human Gait Recognition Based on Deterministic Learning

基于确定性学习理论,提出了一种基于关节角时序数据序列的人体步态识别方法。首先,由人体运动捕捉设备获取关节角时序数据序列,则局部准确的人体步态的内部动力学可通过径向基函数（RBF）网络得到逼近。进一步,证明了逼近误差和相关神经网络（NN）参数的收敛。接下来,通过将NN逼近得到的步态动力学知识存储于常值的RBF网络,可实现人体移动步态特征的有效表达。最后,通过构建步态模式的相似性定义,提出了一种步态时序数据识别的方法,最终可实现准确的步态识别。仿真实验采用类圆规双足机器人验证了所提方法的有效性。

In this paper, we investigate the problem of human gait recognition based on temporal data sequences by utilizing the deterministic learning theory. Firstly, discrete-time joint angle data obtained by the motion-capture equipment or image-processing algorithms forms the temporal data sequences, and locally-accurate approximation of the underlying gait system dynamics is achieved by using radial basis function （RBF） networks. We then prove the convergence of the approximation error and related parameters. Consequently, the joint angle data sequences can effectively represent human gait locomotion by using the knowledge of approximated gait dynamics which is kept in constant RBF networks. Finally, similarity definition for temporal gait data sequences generated from different persons or from different status of one person is given, and a method for recognition of gait temporal data sequences is proposed. We use less complicated simulation examples of compass-like biped robots gait recognition to demonstrate the effectiveness of our schemes.