On the relationship between manifold learning latent dynamics and zero dynamics for human bipedal walking

Abstract

Dynamic modeling of human bipedal walking is important for studying human locomotion and designing assistive and rehabilitation robotic devices. Physical principle-based models and data-driven learning models are two main methods to obtain human walking dynamics. We present analysis and connections between these two different modeling approaches. Mapping and correspondence are established between the reduced analytical dynamics (i.e., zero dynamics) of the bipedal walking and the learned latent dynamics from walking kinematic data. We present these mapping functions, their properties and applications. Experiments are presented to demonstrate and illustrate the analysis and results.