IEEE Trans Cybern. 2015 Nov;45(11):2413-24. doi: 10.1109/TCYB.2014.2373393. Epub 2014 Dec 18.
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.
我们提出了新的多层联合步态-姿势流形（multilayer JGPM），用于复杂的人体步态运动建模，其中三个潜在变量在低维流形中联合定义，以表示各种身体姿势。具体来说，姿势变量（沿着姿势流形）表示行走周期中的特定阶段；步态变量（沿着步态流形）表示不同的行走风格；线性比例变量则描述了行走周期中的最大步幅。我们讨论了耦合姿势和步态流形的两种拓扑先验方法，即圆柱和环面，以检验它们在运动建模中的有效性和适用性。我们采用拓扑约束的高斯过程（GP）潜在变量模型来学习多层 JGPM，其中引入了两种新技术，以在有限的训练数据下促进模型学习。首先是训练数据多样化，它创建了一组具有不同步幅的模拟运动数据。其次是拓扑感知局部学习，通过利用局部拓扑结构来加速模型学习。在卡内基梅隆大学运动捕捉数据上的实验结果表明，与几种现有的基于 GP 的运动模型相比，我们提出的多层模型在人体步态运动建模的整体性能方面具有优势。
