Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling
Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling
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DOI：
10.1109/tcyb.2014.2373393
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发表时间：
2015-11-01
影响因子：
11.8
通讯作者：
Fan, Guoliang
中科院分区：
文献类型：
--
作者：
Ding, Meng;Fan, Guoliang
我们提出了新的多层联合步态姿态流形（多层JGPMS）的复杂的人体步态运动建模，其中三个潜变量被定义为联合在一个低维流形来表示各种身体配置。具体地，姿态变量（沿着姿态流形）表示步行周期中的特定阶段;步态变量（沿着步态流形）表示不同的步行风格;并且线性尺度变量表征步行周期中的最大步幅。我们讨论了两种拓扑先验耦合的姿态和步态流形，即，圆柱形和环形，以检查它们的有效性和运动建模的适用性。我们诉诸于一个拓扑约束高斯过程（GP）的潜变量模型来学习多层JGPM，其中引入了两种新的技术，以促进有限的训练数据下的模型学习。首先是训练数据多样化，创建一组具有不同步幅的模拟运动数据。第二种是拓扑感知的局部学习，利用局部拓扑结构加快模型学习。卡内基梅隆大学的运动捕捉数据上的实验结果表明，我们提出的多层模型的优势，在几个现有的基于GP的运动模型在人体步态运动建模的整体性能。
We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.